6. Providing evidence: personalized context-sensitive summarization and question answering
6。提供證據：個性化上下文敏感總結和答疑
The need to link evidence to patients’ records was stated in the 1977 assessment of computer-based medical information systems undertaken because of increased concern over the quality and rising costs of medical care [103]. The assessment concluded that the quality and cost concerns could be addressed by medical information systems that will supply physicians with information and incorporate valid ?ndings of medical research [103]. The results of medical research might soon become directly available through querying clinical research databases, however to date, ?ndings of medical research can be primarily found in the literature. Following the 1977 report, medical informatics research focused on understanding physicians’ information needs and enabling physicians’ access to the published results of clinical studies. This research provides a solid foundation for NLP aimed at satisfying physicians’ desiderata. The most desired features include comprehensive specific bottom-line recommendations that anticipate and directly answer clinical questions, rapid access, current information, and evidence-based rationale for recommendations [104].
需要聯繫的證據，病人的病歷中申明1977年開展以計算機為基礎的醫療信息系統的評估，因為日益關注的質量和成本上升的醫療[103]。評估得出的結論是質量和成本問題，可以通過醫療信息系統，將提供資料的醫生和納入有效的醫療研究結果[103]。醫學研究的結果可能很快成為直接可通過查詢臨床研究數據庫，但迄今為止，醫學研究的結果可以主要見於文獻中。繼1977年的報告，醫學信息學研究主要集中在了解醫師的信息需求，使醫生獲得臨床研究結果公佈。這項研究提供了一個堅實的基礎，旨在滿足醫生的必要條件為 NLP。最想要的功能包括全面具體的底線預測和建議，直接回答的臨床問題，快速訪問，當前的信息，和證據為基礎的建議的理由[104]。
6.1. Clinical data and evidence summarization for clinicians
6.1。為臨床的臨床資料和證據匯總
Unlike the comparatively better researched summarization and visualization of structured clinical data [105–108], summarization of clinical narrative is an evolving area of research. Afantenos et al. surveyed the potential of summarization technology in the medical domain [109]. Van Vleck et al. identified information physicians consider relevant to summarizing a patient’s medical history in the medical record. The following categories were identified as necessary to capturing patient’s history: Labs and Tests, Problem and Treatment, History, Findings, Allergies, Meds, Plan, and Identifying Info [110]. Meng et al. approached generation of clinical notes as an extractive summarization problem [111]. In this approach, sentences containing patient information that needs to be repeated are extracted based on their rhetorical categories determined using semantic patterns. This extraction method compares favorably to the baseline extraction method (the position of a sentence in the note) on a test set of 162 sentences in urological clinical notes [111]. Cao et al summarized patients’ discharge summaries into problem lists [70].
不同於相對更好的研究總結和結構性的臨床數據的可視化[105-108]，總結臨床敘事研究發展的領域。 Afantenos等。調查匯總的技術在醫療領域的潛力[109]。範 Vleck等。總結在一個病人的病歷，病歷確定的信息，醫生認為有關。要捕捉患者的病史，實驗室和試驗，存在的問題及處理，歷史，結果，過敏，MEDS，計劃，並確定信息[110]被確定為以下類別。 Meng等人。走近臨床筆記一代作為採掘總結問題[111]。在這種方法中，包含病人的信息，需要重複的句子中提取的基礎上確定使用語義模式的修辭類別。這種提取方法相比，毫不遜色在泌尿外科臨床記錄的162句的測試集上的基線提取方法（注意在一個句子中的位置）[111]。問題列出曹等人總結病人出院摘要[70]。
The PERSIVAL project (a prototype system, not currently in use) summarized medical scienti?c publications [112,113]. The summarization module of the PERSIVAL system generated summaries tailored for physicians and patients. Summaries generated for a physician contained information relevant to a specific patient’s record. Each publication was represented using a set of templates. Templates were then clustered into semantically related units in order to generate a summary [112,113].
PERSIVAL項目（一個原型系統，目前尚未使用）總結醫學科學出版物[112113]。總結 PERSIVAL系統模塊產生的摘要，為醫生和患者量身定做。醫生的生成摘要中包含的信息有關的特定病人的記錄。每個發布代表用一組模板。模板，然後將語義相關的單位集中在以生成摘要[112113]。
Based on the semantic abstraction paradigm, Fiszman et al. are developing a summarization system that relies on SemRep for semantic interpretation of the biomedical literature. The system condenses SemRep predications and presents them in graphical format [114]. We hope to see in the future if the above method holds promise for summarization and visual presentation of clinical notes.
基於語義的抽象範式，Fiszman等。正在開發一個總結系統上依賴於生物醫學文獻的語義解釋 SemRep。該系統凝結 SemRep predications和圖形格式[114]。我們希望看到在未來，如果上面的方法保存總結和視覺表現的臨床筆記承諾。
6.2. Clinical data and evidence summarization for patients
6.2。為患者的臨床資料和證據匯總
The online access to personal health and medical records and the overwhelming amount of health-related information available to patients (alternatively called health care consumers and lay users) pose many interesting questions. Hardcastle and Hallet studied which text segments of a patient record require explanation before being released to patients and what types of explanation are appropriate [115]. Elhadad and Sutaria presented an unsupervised method for building a lexicon of semantically equivalent pairs of technical and lay medical terms [116].
Ahlfeldt et al. surveyed issues related to communicating technical medical terms in everyday language for patients and generating patient-friendly texts [117]. The survey presents research on alleviating the lack of understanding of clinical documents caused by medical terminology. This research includes generation of patient vocabularies and matching those vocabularies and problem lists with standard terminologies; generation of terminological resources, corpora and annotation tool; development of natural consumer anguage generation systems; and customization of patient education materials [117]. Green presents the design of a discourse generator that plans the content and organization of lay-oriented genetic counseling documents to assist drafting letters that summarize the results for patients [118].
在線訪問個人健康和醫療記錄和健康有關的信息提供給患者（或者稱為醫療保健消費者，奠定用戶）絕大多數量，提出了許多有趣的問題。Hardcastle和Hallet研究病人記錄的文本段需要被釋放之前向患者解釋和什麼類型的解釋是適當的[115]。Elhadad和Sutaria提出了建設一個詞彙語義上等同於對技術的無監督方法和非專業醫學術語[116]。
Ahlfeldt等人。在日常語言溝通技術的醫療條件，為病人和病人友好的文本[117]有關調查的問題。調查研究提出減輕缺乏引起的臨床醫療術語文件的理解。這項研究包括病人的詞彙和匹配的詞彙和標準術語的問題列出代代用語上的資源，語料庫和註釋工具;自然消費 anguage發電系統的發展;和定制的患者教育材料[117]。綠色呈現一個話語發生器的設計，計劃奠定面向遺傳諮詢文件的內容和組織協助起草的信件，總結患者的結果[118]。
6.3. Clinical question answering
6.3。臨床問題回答
One of the principal purposes of CDS is answering questions[14]. Questions occurring in clinical situations could pertain to "information on particular patients; data on health and sickness within the local population; medical knowledge; local information on doctors available for referral; information on local social in?uences and expectations; and information on scientific, political, legal, social, management, and ethical changes affecting both how medicine is practiced and how doctors interact with individual patients” [119]. Some questions do not need NLP and can be answered directly by a known resource. For example, the NLM Go Local service19 (which connects users to health services in their local communities and directs users of the Go Local sites to MedlinePlus health information) was established to answer logistics questions by providing access to local information. Questions about particular patients are currently answered by manually browsing or searching the EHR. Answering these questions can be facilitated by summarization (which requires NLP if information is extracted from free-text fields) and visualization tools [105–108]. Facilitating access to medical knowledge by providing answers to clinical questions is an area of active NLP research [120]. The goal of clinical question answering systems is to satisfy medical knowledge questions providing answers in the form of short action items supported by strong evidence.
CDS的主要目的之一是回答問題 [14]。在臨床情況下發生的問題可能涉及到“特別是病人的信息;在當地居民的健康和疾病的數據;醫療知識，對醫生轉介的本地信息，對當地的社會影響和期望的信息;和信息科學，政治，法律，社會，管理，和道德的變化影響都實行醫藥是如何和醫生如何與個別病人的互動“[119]有些問題並不需要NLP和可直接回答已知的資源，例如，NLM的本地service19（連接用戶在當地社區衛生服務，並指導用戶轉到本地網站 MedlinePlus衛生信息）建立物流問題的答案提供訪問本地信息，關於特別是病人的問題，目前正在通過手動瀏覽或搜索的回答電子病歷回答這些問題總結（需要NLP的信息是從自由文本字段中提取）和可視化工具，可以促進[105-108]促進獲得醫療知識，提供臨床問題的答案是一個活躍的領域NLP的研究[120]。臨床問題回答系統的目標是為了滿足醫學知識的問題提供了有力的證據支持短期行動項目的形式答案
Jacquemart and Zweigenbaum studied the feasibility of answering students’ questions in the domain of oral pathology using Web resources. Questions involving pathology,procedures, treatments,examinations, indications, diagnosis and anatomy were used to develop eight broad semantic models comprised of 66 different syntactico-semantic patterns representing the questions. The triple-based model ([concept]–(relation)–[concept]) combined with which, why, and does modalities accounted for a vast majority of questions. The formally represented questions were used to query 10 different search engines. Search results were checked manually to find a passage answering the question in a consistent context[121]
Jacquemart和Zweigenbaum回答學生的問題在口腔病理學域使用網絡資源的可行性研究。涉及病理的問題，被用來開發8大66不同syntactico的語義表示問題的模式組成的語義模型的過程，治療，檢查，適應症，診斷和解剖。基於三重模式（[概念] - （關係） - [概念]）結合，為什麼，不佔絕大多數的問題的方式。正式代表的問題，用於查詢10個不同的搜索引擎。檢查手動搜索結果找到一個通道，在一致的情況下回答問題[121]
The [concept]–(relation)–[concept] triples generated by SemRep can be used to generate conceptual condensates that summarize a set of documents [114], or answer speci?c questions, for example, ?nding the best pharmacotherapy for a given disease[65]. Within the EpoCare project, the same question type is answered by using an SVM to classify MEDLINE abstract sentences as containing an outcome (answer) or not and extracting the high-ranking sentences [122]. The CQA-1.0 system also implements an Evidence Based Medicine (EBM)-inspired approach to outcome extraction [120]. In addition to extracting outcomes from individual MEDLINE abstracts to answer a wide range of questions,the CQA-1.0 system aggregates answers to questions about the best drug therapy into 5–6 drug classes generated based on the individual pharmaceutical treatments extracted from each abstract. Each class is supported by the strongest patient-oriented outcome pertaining to each drug in the class. The EpoCare and CQA-1.0 systems rely on the Patient-Intervention-Comparison-Outcome (PICO) framework developed to help clinicians formulate clinical questions [99]. The MedQA system answers de?nitional questions by integrating information retrieval, extraction, and summarization techniques to automatically generate paragraph-level text [123].
[概念] - （關係） - [概念] SemRep生成的三倍，可以用來產生概念的凝析油，總結一套
文件[114]，或回答具體問題，例如，找到一個給定的疾病的最佳藥物[65]。在該 EpoCare項目中，同樣的問題類型是通過使用SVM分類包含的結果（答案）或不提取的高級的句子[122]論文摘要的句子回答。CQA- 1.0系統還實現了循證醫學（EBM）啟發的結果提取方法[120]。除了提取結果個人論文摘要，回答了範圍廣泛的問題，CQA-1.0系統聚集大約為5-6類藥物的最佳藥物治療的問題的答案從每個抽象提取的個人藥物治療的基礎上產生。每個類是最強的病人為本的有關藥物類中的每個結果的支持。 EpoCare和CQA- 1.0系統依靠病人干預比較成果（皮秒）的框架，以幫助臨床醫生制定臨床問題[99]。 MedQA系統集成信息檢索，提取和總結技術，自動生成段級文字[123]定義問題的答案。
7. Clinical NLP: direct applications of NLP in healthcare
In addition to processing text pertaining to patients and generated by clinicians and researchers, NLP methods have been applied directly to patients’ narratives for diagnostic and prognostic purposes.
The Linguistic Inquiry and Word Count (LIWC)20 tool was used to explore personality expressed through a person’s linguistic style [124]. The LIWC tool (which calculates the percentage of words in written text that match up to 82 language dimensions) was evaluated in predicting post-bereavement improvements in mental and physical health [125], predicting adjustment to cancer [126], differentiating between the Internet message board entries and homepages of pro-anorexics or recovering anorexics [127], and recognizing suicidal and non-suicidal individuals [128]. Pestian et al. demonstrated that the sequential minimization optimization algorithm can classify completer and simulated suicide notes as well as mental health professionals [129].
Another potential clinical NLP application is assessment of neurodegenerative impairments. Roark et al. studied automation of NLP methods for diagnosis of mild cognitive impairment (MCI).Automatic psychometric evaluation included syntactic annotation and analysis of spoken language samples elicited during neuropsychological exams of elderly subjects. Evaluation of syntactic complexity of the narrative was based on analysis of dependency structures and deviations from the standard (for English) rightbranching trees in parse trees of subjects’ utterances. Measures derived from automatic parses highly correlated with manually derived measures, indicating that automatically derived measures may be useful for discriminating between healthy and MCI subjects. [130].
7。臨床 NLP：NLP的直接應用在醫療保健
除了處理有關患者和臨床醫生和研究人員所產生的文本，NLP方法已直接應用於病人的診斷和預後的目的“敘述。 的語言調查和Word計數（LIWC）20工具被用來探索通過人的語言風格表達的個性[124]。 LIWC工具（計算中的單詞匹配多達82個語言尺寸的書面文本的百分比）進行了評估預測後喪親之痛的改進，在精神和身體健康[125]，預測調整為癌症[126]，區分互聯網留言板條目和網頁的親厭食症或恢復厭食症[127]，並認識到自殺及非自殺的人[128]。 Pestian等人。表明，順序最小化的優化算法分類的完備和模擬自殺筆記，以及心理衛生專業人員[129]。
另一個潛在的臨床應用 NLP應用是神經退行性損傷評估。含有Roark等人。 NLP的方法研究自動化診斷輕度認知功能障礙（MCI）的自動心理評估包括口語樣本的語法註釋和分析引起老年受試者的神經心理學考試期間。的敘事語法的複雜性的評價是基於依賴結構和rightbranching科目“話語的解析樹的樹木（英文）的標準偏差分析。從自動產生的高度相關措施分析與手動派生的措施，表明自動派生的措施，可用於健康和MCI科目之間的歧視。 [130]。
Clinical NLP is also used for medication compliance and drug
abuse monitoring. Butler et al. explored usefulness of content analysis of Internet message board postings for detection of potentially abusable opioid analgesics [131]. In this study, attractiveness for abuse of OxyContin, Vicodin, and Kadian determined automatically (using the total number of posts by product, total number of mentions by product (including synonyms and misspellings), total number of posts containing at least one mention of each product,total number of unique authors, and the number of unique authors of posts referencing any of the 3 target products) was compared to the known attractiveness of the products. The numbers of mentions of the products were signi?cantly different and corresponded to the product attractiveness. Based on this and other metrics, the authors conclude that a systematic approach to post-marketing surveillance of Internet chatter related to pharmaceutical products is feasible [131]. Understanding patient compliance issues could help in clinical decisions. This understanding could be gained through processing of informal textual communications found in the publicly available blog postings and e-mail archives. For example, Malouf et al. analyzed 316,373 posts to 19 Internet discussion groups and other websites from 8731 distinct users and found associations (such as cognitive side effects, risks, and dosage related issues) the epilepsy patients and their caregivers have for different medications [132].
NLP也是臨床用於服藥依從性和藥物濫用監測。巴特勒等人。探討互聯網留言板帖子內容分析的用處檢測潛在的abusable阿片類鎮痛藥[131]。在這項研究中，自動確定 OxyContin，Vicodin，並 Kadian濫用的吸引力（按產品的職位總數，提到了產品的總人數（包括同義詞和拼寫錯誤），至少包含一個提到每個職位總數的產品，獨特的作者總數，和獨特的作者引用的3個目標產品）的職位數相比，產品的已知的吸引力。提到的產品的數量均顯著不同，相當於產品的吸引力。和其他指標的基礎上，作者得出結論，上市後監視互聯網相關的醫藥產品的喋喋不休，系統化的方法是可行的[131]。了解患者的依從性問題，可以幫助臨床決策。這種理解可能會獲得通過公開發布的博客文章和電子郵件檔案中發現的非正式文本通信處理。例如，Malouf等。分析316373職位，以19互聯網討論組和8731不同用戶和協會（如認知的副作用，風險，與劑量相關的問題）的癲癇患者和他們的照顧者的其他網站有不同的藥物[132]。
To the best of our knowledge, the applications described in this section are experimental rather than deployed and regularly used in clinical setting. The dif?culties in translation of clinical NLP research into clinical practice and obstacles in determining the level of practical engagement of NLP systems are discussed in the next section.
據我們所知，在本節中所述的應用程序，而不是部署，並在臨床上經常使用的實驗。在翻譯的NLP的臨床研究到臨床實踐，並在確定 NLP的系統的實際參與程度的障礙，困難是在下一節討論。
Most of the above presented methods and systems were developed for speci?c users, document types and CDS goals. Future research might indicate if such systems could be easily retargeted for new users and goals and whether the retargeted systems can compete with those designed for speci?c tasks and clinical systems. Evaluation methods for measuring the impact of NLP methods on healthcare in addition to reliable standardized evaluation of NLP systems need to be developed.
上述提出的方法和系統開發為特定的用戶，文檔類型和CDS目標。未來的研究可能表明，如果這種系統可以很容易地為新用戶和目標重定向和重定向系統是否能與之抗衡的具體任務和臨床系統設計的。除了可靠NLP的系統的標準化評價的測量 NLP的方法對醫療保健的影響的評價方法需要開發。
For several issues very important to the future development of NLP for CDS, there is currently only anecdotal evidence and sparse publications. For example, with few exceptions, we do not know which of the reviewed NLP–CDS systems are actually implemented or deployed, and what makes these systems worthwhile. We might speculate that, for example, MedLEE is successfully integrated with a clinical information system because it was developed and adapted, as needed, for specific users and CDS goals, but the reason for its success could also be its sophisticated NLP. We could better judge which features determine whether NLP–CDS systems are applied outside of the experimental setting if we had more data points. We believe it would be valuable to have a special venue for presenting case studies and analysis of applied NLP systems in the near future.
NLP的CD的未來發展非常重要的幾個問題，是目前唯一的證據和稀疏的出版物。例如，除了少數例外，我們不知道其中的NLP- CDS系統實際上是實施或部署，是什麼讓這些系統值得。我們不妨推測，例如，MedLEE是成功地與臨床信息系統集成，因為它是開發和調整，需要為特定的用戶和CDS的目標，但其成功的原因也可能是其先進的自然語言處理。我們可以更好地判斷哪些功能確定是否NLP- CDS系統應用實驗的設置之外，如果我們有更多的數據點。我們相信這將是有價值的，有一個特殊的案例研究和應用自然語言處理系統的分析，在不久的將來提出場地。
Priorities in NLP development will be determined by the readiness of intended users to adopt NLP. The early successes in NLP and CDS led to high user expectations that were not always met. NLP researchers need to re-gain clinicians’ trust, which is achievable based on better understanding of the NLP strengths and weaknesses by clinicians, as well as significant progress in biomedical NLP. Reacquainting clinicians with NLP can be facilitated by NLP training, well-planned NLP experiments, careful and thoughtful evaluation of the results, high-quality implementation of NLP modules, semi-automated and easier methods for adapting NLP for other domains, and evaluations of NLP–CDS adequacy in satisfying user needs.
在NLP發展的優先任務將是確定的目標用戶願意採用自然語言處理。在NLP和CDS的早期成功導致高的用戶並不總是達到預期。NLP的研究人員需要重新獲得醫生的信任，這是可以實現的基礎上更好地了解NLP的長處和弱點，由臨床醫生，以及在生物醫學 NLP的方面取得了重大進展。NLP的訓練，精心策劃的NLP的實驗，細心周到的評價結果，高品質的NLP模塊的實施，半自動化，更容易適應其他域的自然語言處理的方法，和NLP的評價可以促進 Reacquainting臨床與 NLP- CDS在滿足用戶需求的充足。
We believe NLP can contribute to decision support for all groups involved in the clinical process, but the development will probably focus on the areas for which there is higher demand. For example,if researchers are more eager consumers of NLP than clinicians,NLP research into text mining and literature summarization will continue dominating the field.
我們相信NLP可以有助於在臨床過程中涉及的所有群體的決策支持，但發展很可能會集中在哪些領域有更高的要求。例如，如果研究人員正在比醫生更渴望NLP的消費者，自然語言處理到文本挖掘和總結文學的研究將繼續稱霸該領域。
The NLP CDS tasks are so numerous and complex that this area of research will succeed in making practical impact only as a result of coordinated community-wide effort.
NLP的CDS的任務是如此紛繁複雜的，這一領域的研究將成功只能作為協調社會各界的努力的結果，實際影響。
2011年12月15日 星期四
What can natural language processing do for clinical decision support?Ch#5
The early vision of medical NLP was implemented in the Linguistic String Project (LSP) system that developed the basic components and the formal representation of clinical narrative, and implemented the transformation of the free-text clinical documents into a formal representation [66]. The LSP system evolved into the Medical Language Processor (MLP) that includes the English healthcare syntactic lexicon and medically tagged lexicon, the MLP parser, parsing with English medical grammar, selection with medical co-occurrence patterns, English transformation, syntactic regularization, mapping into medical information format structures, and a set of XML tools for browsing and display.
MedLEE is an NLP system that extracts information from clinical narratives and presents this information in structured form using a controlled vocabulary. MedLEE uses a lexicon to map terms into semantic classes and a semantic grammar to generate formal representation of sentences. It is in use at Columbia University Medical Center, and is one of the few natural language processing systems integrated with clinical information systems. MedLEE has been successfully used to process radiology reports, discharge summaries, sign-out notes, pathology reports, electrocardiogram reports, and echocardiogram reports [10,67–70]. An in-depth overview of the system and a case scenario are provided in [71].
早期醫療 NLP的願景是在語言字符串項目（LSP）系統開發的基本組成部分和臨床敘事的正式代表的實施，並實行自由的文本臨床文件轉化成一個正式的表示[66]。映射到LSP的系統，演變成醫學語言處理器（MLP），其中包括醫療的英語語法詞彙和醫療標籤的詞彙，總綱發展藍圖的解析器，解析英語語法醫療，醫療共生的模式，英國的改造，句法正規化的選擇醫療信息的格式結構，以及一套用於瀏覽和顯示 XML工具。
MedLEE是一種自然語言處理系統，從臨床敘述中提取的信息和使用受控詞表的結構形式呈現信息。 MedLEE使用一個詞彙映射到語義類和語義語法生成句子的正式代表。它是在哥倫比亞大學醫學中心的使用，是為數不多的自然語言處理系統與臨床信息系統集成。 MedLEE已成功地用於處理放射報告，出院摘要，註銷票據，病理報告，心電圖報告，超聲心動圖報告[10,67-70]。 [71]中提供了一個深入系統的概述和情況。
The Text Analytics architecture developed in collaboration between the Mayo Clinic and IBM is using Unstructured Information Management Architecture (UIMA) to identify clinically relevant entities in clinical notes. The entities are subsequently used for information retrieval and data mining [72]. The ongoing development of this architecture resulted in two specialized pipelines:medKAT/P, which extracts cancer characteristics from pathology reports, and cTAKES, which identifies disorders, drugs, anatomical sites, and procedures in clinical notes. Evaluated on a set of manually annotated colon cancer pathology reports, MedTAS/P achieved F1-scores in the 90% range in extraction of histology, anatomical entities, and primary tumors [73]. A lower score achieved for metastatic tumors was attributed to the small number of instances in the training and test sets [73]. cTAKES and HiTEx, described below,are the first generalized clinical NLP systems to be made publicly available.
梅奧診所和IBM之間的合作開發文本分析的架構是使用非結構化信息管理架構（UIMA）在臨床筆記，以確定臨床相關的實體。這些實體隨後用於信息檢索和數據挖掘[72]。這種架構的持續發展造成了兩個專門的管道：medKAT/ P提取癌症的特點，從病理報告，cTAKES，識別障礙，藥物，解剖部位，並在臨床筆記程序。MedTAS/ P手動註明結腸癌的病理報告進行評估，取得90％的範圍內提取組織學，解剖學的F1 -分數實體和原發腫瘤[73]。轉移性腫瘤所取得的分數較低歸因於少數的實例在訓練和測試集[73]。 cTAKES和Hitex和所述，是廣義臨床 NLP的系統可以公之於眾。
Developed at the National Center for Biomedical Computing,Informatics for Integrating Biology & the Bedside (I2B2), the Health Information Text Extraction (HiTEx) tool based on GATE is a modular system that assembles a different pipeline for extracting specific findings from clinical narrative. For example, a pipeline to extract diagnoses is formed by applying sequentially a section splitter, section filter, sentence splitter, sentence tokenizer, POStagger, noun phrase finder, UMLS concept mapper, and negation finder [74]. A pipeline for extraction of family history from discharge summaries and outpatient clinic notes evaluated on 350 sentences achieved 85% precision and 87% recall in identifying diagnoses; 96% precision and 93% recall differentiating family history from patient history; and 92% precision and recall exactly assigning diagnoses to family members [75].
在國家生物醫學計算中心的開發，信息整合生物學及床頭（I2B2），門極的健康信息的文本提取工具（HiTEx）是一個模塊化系統，組裝不同的管道從臨床敘事中提取的具體結果。例如，管道提取的診斷是由應用順序一個部分分離器，過濾器節，句子分配器，句子標記生成器，POStagger，查找名詞短語，UMLS概念映射器，並否定取景器[74]。一個管道提取家族史出院摘要及門診的350句注意到評估的精確度達到85％和87％，在確定診斷召回，96％的精度，從病史和93％召回差異化的家族史;和92％的準確率和召回完全分配家庭成員的診斷[75]。
The MediClass (a ‘‘Medical Classifier”) system was designed to automatically detect clinical events in any electronic medical record by analyzing the coded and free-text portions of the record.It was assessed in detecting care delivery for smoking cessation;immunization adverse events; and subtypes of diabetic retinopathy. Although the system architecture remained constant for each clinical event detection task, new classification rules and terminology were defined for each task [76]. For example, to detect possible vaccine reactions in the clinical notes, MediClass developers identified the relevant concepts and the linguistic structures used in clinical notes to record and attribute an adverse event to an immunization or vaccine [77]. The identified terms and structures were encoded into rules of a MediClass knowledge module that defines the classification scheme for automatic detection of possible vaccine reactions. The scheme requires detecting an explicit mention of an immunization event and detecting or inferring at least one finding of an adverse event [77]. In 227 of 248 cases (92%), MediClass correctly detected a possible vaccine reaction [77].
該 MediClass（a''Medical分類“）系統的目的是要自動檢測通過分析record.It的編碼和自由文本部分被檢測照顧戒菸交付評估任何電子病歷臨床事件;免疫接種後不良事件;... ...而糖尿病視網膜病變的亞型雖然系統架構仍然是每個臨床事件檢測任務不變，新的分類規則和術語為每個任務定義[76]例如，及時發現可能的臨床筆記疫苗反應，MediClass開發商確定有關的概念和臨床記錄用於記錄和不良事件的屬性到一個或疫苗接種的語言結構 [77]。已確定的條款和結構編碼成一個 MediClass知識模塊規則，定義為自動檢測的分類方案可能的疫苗反應，該計劃要求檢測到的免疫活動，並明確提到檢測或推斷至少有一個不良事件的發現[77]，在227248例（92％），MediClass正確偵測到可能的疫苗反應[77]。
5.2. Specialized clinical NLP systems
5.2。專業臨床 NLP系統
The evaluation of the general-purpose architectures in specific tasks and the use of the general-purpose systems as components or foundation of many task- or document-specific systems, make the line between these system types somewhat fuzzy. The differences are most probably not in the end-results but in the initial goals of developing a system to process free text for any task versus solving a specific clinical task. Independent of the starting point and reuse of the general-purpose components, solving a specific task at minimum requires developing a task-specific database and decision rules. Some examples of task-speci?c systems are provided in this section.
評價的具體任務的通用架構和組件或許多基礎任務或文檔特定系統的通用系統的使用，使這些系統類型之間的線有點模糊。差異是最有可能在最終的結果，但在開發一個系統，任何任務與解決一個特定的臨床任務來處理自由文本的初步目標。獨立的出發點和通用組件的重用，至少解決一個特定的任務，需要制定一個特定任務的數據庫和決策規則。本條規定的具體任務系統的一些例子。
5.2.2. Processing radiology reports
Radiology reports are probably the most studied type of clinical narrative. This extremely important source of clinical data provides information not otherwise available in the coded data and allows performing tasks from coding of the findings and impressions [67,80], to detection of imaging technique suggested for followup or repeated examinations [81], to decision support for nosocomial infections [19], to biosurveillance [82]. The complete description of systems developed for processing of radiology reports is beyond the scope of this review. This section outlines the scope and research directions in processing of radiology reports and omits most of the radiology report studies based on the described above general-purpose systems.
5.2.2。處理放射報告
放射科報告可能是臨床敘事研究最多的類型。這極其重要的臨床資料來源提供的信息不能編碼數據，並允許執行任務編碼[67,80]，建議後續或重複檢查的成像技術檢測的結果和印象[81]，決定對院內感染的支持[19]，生物監測[82]。放射報告處理開發完整的系統描述，是本次審查的範圍之外。本節概述了在處理放射報告的範圍和研究方向，並省略了大部分的放射報告的研究基礎上，上述通用系統描述。
A family of systems with the initial goal of processing radiology
reports was developed at the LDS Hospital (Intermountain Healthcare). The Special Purpose Radiology Understanding System (SPRUS) extracts and encodes the findings and the radiologists’ interpretations using information from a diagnostic expert system [80]. SPRUS was followed by the Natural language Understanding Systems (NLUS) and Symbolic Text Processor (SymText) systems that combine semantic knowledge stored and applied in the form of a Bayesian Network with syntactic analysis based on a set of
augmented transition network (ATN) grammars [83,84]. SymText was deployed at LDS Hospital for semi-automatic coding of admit diagnoses to ICD-9 codes [85]. SymText was also used to automatically extract interpretations from Ventilation/Perfusion lung scan reports for monitoring diagnostic performance of radiologists [86]. The accuracy of the system in identifying pneumonia-related concepts and inferring the presence or absence of acute bacterial neumonia was evaluated using 292 chest X-ray reports annotated by physicians and lay persons. The 95% recall, 78% precision, and 85% specificity achieved by the system were comparable to that of physicians and better than that of lay persons [19]. SymText evolved to MPLUS (M+), which also uses a semantic model based on Bayesian Networks (BNs), but differs from SymText in the size and modularity of its semantic BNs and in its use of a chart parser [87]. M+ was evaluated for the extraction of American College of Radiology utilization review codes from 600 head CT reports. The system achieved 87% recall, 98% specificity and 85% precision in classifying reports as positive (containing brain conditions) [87].
一個家庭系統與最初的目標處理放射報告是在LDS醫院（山間健康護理）。特殊用途放射了解系統（SPRUS）提取和編碼結果，並在放射科醫生“從診斷專家系統使用的信息的解釋[80]。 SPRUS其次是自然語言理解（NLUS）和符號文字處理器（SymText）系統結合語義知識的形式存儲和應用基於一組與句法分析的貝葉斯網絡擴充轉移網絡（ATN）文法[83,84]。 SymText部署在LDS醫院承認半自動編碼ICD- 9編碼的診斷[85]。 SymText也被用來自動ically提取肺通氣 /灌注掃描的解釋監測的放射診斷性能報告[86]。在確定肺炎相關系統的精度概念和推理急性細菌的存在或缺乏肺炎是評估使用292胸部X光報告註明醫生和非法律專業人士。召回95％，78％的精度，系統所取得的85％，特異性相媲美，醫生和優於非法律專業人士[19]。 SymText到MPLUS（M +），還採用了基於語義模型的演變貝葉斯網絡（BNS）在大小不同，但是從 SymText和其語義 BNS的模塊化，並在其使用圖表分析器[87]。 M +的評估，美國學院的提取放射利用率從600頭顱 CT報告審查代碼。 “系統實現召回87％，特異性98％和85％的精度分類為陽性的報告（含腦條件）[87]。
M+ was also evaluated for classifying chief complaints into syndrome categories [88]. Currently, M+ has been redesigned as Onyx and is being applied to spoken dental exams [89]. These evolving NLP systems provide examples of successful retargeting to coding of other types of clinical reports.
M +的評價分為綜合徵分類[88]行政投訴。目前，M +的已經被重新設計為瑪瑙，並正在發言[89]牙科檢查。這些不斷發展的自然語言處理系統提供的成功重定向到其他類型的臨床報導的編碼的例子。
The REgenstrief data eXtraction tool (REX) coded raw version 2.x Health Level 7 (HL7) messages to a targeted small to medium sized sets of concepts for a particular purpose in a given kind of narrative text [90]. REX was applied to 39,000 chest X-rays performed at Wishard Hospital in a 21-month period to identify findings related to CHF, tuberculosis, pneumonia, suspected malignancy, compression fractions, and several other disorders. REX achieved 100% specificity for all conditions, 94–100% sensitivity,and 95–100% positive predictive value, outperforming human coders in sensitivity [90]. In contrast, mapping six types of radiology reports to a UMLS subset and then selectively recognizing most salient concepts using information retrieval techniques, resulted in 63% recall and 30% precision [91].
REgenstrief數據提取工具（REX）編碼的原始版本2.x的健康水平7（HL7）的消息有針對性的小為特定目的在敘事文本[90]一種中型集的概念。 Rex是應用到39000威舍德醫院在21個月內，以確定瑞士法郎，肺結核，肺炎，疑有惡變，壓縮分數，和其他一些疾病有關的調查結果進行胸部X射線。 REX取得的所有條件，94-100％的敏感性，以及95-100％，陽性預測值100％的特異性，在敏感性方面贏過人類編碼[90]。相比之下，映射六種類型到UMLS子集的放射報告，然後選擇性地認識到使用信息檢索技術的最顯著的概念，導致63％的召回和30％的精度[91]。
5.2.3. Processing emergency department reports
Topaz targets 55 clinical conditions relevant for detecting pa-
tients with an acute lower respiratory syndrome [92]. Topaz uses three methods for mapping text to the 55 conditions: index UMLS concepts with MetaMap [93]; create compound concepts from UMLS concepts or keywords and section titles (e.g., Section:Neck + UMLS concept for lymphadenopathy = Cervical Lymphadenopathy); and identify measurement-value pairs (e.g.,‘‘temp” + number > 38 degrees Celsius = Fever). Topaz is built on the GATE platform and implements ConText as a GATE module for determining whether indexed conditions are present or absent,experienced by the patient or someone else, and historical, recent,or hypothetical. After integrating potentially multiple mentions of a condition from a report, agreement between Topaz and physicians reading the report was 0.85 using weighted kappa.
5.2.3。處理緊急情況部報告
黃玉目標檢測患者急性下呼吸道綜合徵[92]55有關的臨床情況。黃玉使用的55個條件的映射文本的三種方法：指數 UMLS與 MetaMap的概念[93]; UMLS概念或關鍵詞，節的標題（例如，第：頸部+=頸部淋巴結腫大淋巴結腫大 UMLS概念）創建複合概念;並確定測量值對（例如，“溫度”+數字>=發燒38攝氏度度）。黃玉是建立在門平台，並實現為確定是否索引條件是存在或不存在的，由患者或別人的經驗豐富，歷史，最近，或假想的門模塊中。整合存在多種後的從報告中的一個條件提到，黃玉和醫生閱讀報告之間的協議是0.85，使用加權卡帕
5.2.4. Processing pathology reports
Surgical pathology reports are another trove of clinical data for locating information about appropriate human tissue specimens [94] and supporting cancer research. For example, a preprocessor integrated with MedLEE to abstract 13 types of findings related to risks of developing breast cancer achieved a sensitivity of 90.6% and a precision of 91.6% [69].
In MEDSYNDIKATE, an NLP system for extraction of medical information from pathology reports, the basic sentence-level understanding of the clinical narrative (that takes into consideration grammatical knowledge, conceptual knowledge, and the link between syntactic and conceptual representations) is followed by the text-level analysis that tracks reference relations to eliminate representation errors [95].
Liu et al. assessed the feasibility of utilizing an existing GATE pipeline for extraction of the Gleason score (a measure of tumor grade), tumor stage, and status of lymph node metastasis from free-text pathology reports [96]. The pipeline was evaluated on committing errors related to the text processing and extraction of values from the report, and errors related to semantic disagreement between the report and the gold standard. Each variable had a different profile of errors. Numerous system errors were observed for Gleason Score extraction that requires fine distinctions and TNM stage extraction requiring multiple discrete decisions.
The authors conclude that the existing system could be used to aid manual annotation or could be extended for automatic annotation [96]. These findings second observations of Schadow and McDonald that general-purpose tools and vocabularies need to be adapted to the specific needs of surgical pathology reports [94]. The Cancer Text Information Extraction System (caTIES) system, built on a GATE framework, uses MetaMap [93] and NegEx [51] to annotate findings, diagnoses, and anatomic locations in pathology reports. caTIES provides researchers with the ability to query, browse and create orders for annotated tissue data and physical material across a network of federated sources using automatically annotated pathology reports.
5.2.4。處理病理報告
手術病理報告的其他臨床資料的寶庫，為尋找適當的人體組織標本的信息[94]和支持癌症研究。例如，預處理器集成MedLEE抽象13的結果患上乳腺癌的風險，實現了90.6％的敏感性和91.6％的精度[69]。
MEDSYNDIKATE，病理報告為醫療信息的提取自然語言處理系統，基本句子級的臨床敘事的理解（即需要考慮語法知識，概念性知識，語法和概念的陳述之間的聯繫）其次是文本層次分析跟踪參考的關係，以消除代表性的錯誤 [95]。
Liu等人。評估利用現有門提取的Gleason評分管道（衡量腫瘤分級），腫瘤分期和狀態從自由文本的病理報告 [96]淋巴結轉移的可行性。該管道進行了評估，提交有關的文字處理和從報告中提取值的錯誤，和錯誤有關的報告和金標準的語義之間的分歧。每個變量有不同的配置文件的錯誤。眾多的系統誤差，觀察 Gleason評分提取需要的細微差別和TNM分期提取，需要多個分立的決定。
作者得出結論：現有的系統可以用於急救手冊註釋，或可自動標註 [96]延長。這些發現需要適應手術病理報告的具體需求，通用工具和詞彙 Schadow和麥當勞的第二個觀察 [94]。癌的文本信息提取系統（caTIES）系統，建立在一個門框架，使用MetaMap [93]和NegEx [51]註釋在病理報告結果，診斷和解剖位置。 caTIES提供註明組織的數據和整個網絡的聯合使用自動註明的病理報告來源的物理材料的訂單查詢，瀏覽和創造能力的研究人員。
5.2.5. Processing a mixture of clinical note types
The above studies indicate that natural language processing acceptable for clinical decision support is better achieved using tools developed for specific tasks and document types. It is therefore not surprising that processing of a mixture of clinical notes is successful when the task is well-defined and a small knowledge base is developed specifically for the task. For example, Meystre and Haug created a subset of the UMLS Metathesaurus for 80 problems of interest to their longitudinal Electronic Medical Record and evaluated extraction of these problems from 160 randomly selected discharge summaries, radiology reports, pathology reports,progress notes, and other document types [97]. The evaluation demonstrated that using a general purpose entity extraction tool with a custom data subset, disambiguation, and negation detection achieves 89.2% recall and 75.3% precision [97].
5.2.5。處理臨床注意類型的混合物
上述研究表明，自然語言處理，可以接受的臨床決策支持，更好地實現使用的具體任務和文件類型的開發工具。因此，也就不足為奇了臨床票據的混合物處理任務時，成功的定義，並專門任務開發一個小的知識基礎。例如，Meystre和豪格創造了UMLS Metathesaurus80關心的問題，其縱向的電子病歷和160隨機選擇出院摘要，放射科報告，病理報告，進度說明，以及其它類型的文檔，這些問題的評估提取的子集[97]。評估表明，使用一個自定義的數據子集，消歧，並否定檢測的一個通用實體提取工具實現召回89.2％和75.3％的精度[97]。
The MediClass system [76] was configured to automatically assess delivery of evidence-based smoking-cessation care [98]. A group of clinicians and tobacco-cessation experts met over several weeks to encode the recommended treatment model using the concepts and the types of phrases that provide evidence for smoking-cessation medications, discussions, referral activities, quitting activities, smoking and readiness-to-quit assessments. The treatment model involves five steps, ‘‘5A’s”: (1) ask about smoking status; (2) advise to quit; (3) assess a patient’s willingness to quit; (4)assist the patient’s quitting efforts; and (5) arrange follow-up. Evaluated on 500 patient records containing structured data in addition to progress notes, patient instructions, medications, referrals, reasons for visit, and other smoking-related data, MediClass performance was judged adequate to replace human coders of the 5A’s of smoking-cessation care [98].
MediClass系統[76]被配置為自動評估證據為基礎的戒菸護理[98]的交付。 A組的臨床醫生和煙草戒菸專家會見了幾個星期的編碼建議的治療模式，使用，提供戒菸藥物，討論，轉介活動，戒菸活動，吸煙和準備到的證據的概念和類型的短語退出評估。五個步驟，該治療模式“5A的”：（1）詢問有關吸煙狀況;（2）告知退出;（3）評估病人的戒菸意願;（4）協助病人戒菸的努力;和（5）安排跟進。除了進展注意到的結構化數據500門診記錄評價，病人的指示，藥物，轉介，參觀的原因和其他與吸煙有關的，MediClass性能判斷足夠取代的戒菸保健5A的人類編碼[ 98]。
The InfoBot system under development at the National Library
of Medicine identifies the elements of a well-formed clinical question [99] in clinical notes. It subsequently invokes a question answering module (the CQA 1.0 system described in Section 6.3)that extracts answers to the question about the best care plan for a given patient with the identified problems from the literature,and delivers documents containing the answers [100]. In a pilot evaluation by 16 NIH Clinical Center nurses, each evaluating 15 patient cases, documents containing answers were found to be relevant and useful in the majority of cases [101]. It remains to be seen if such automated methods of linking evidence to a patient’s record can achieve the accuracy of more controlled delivery implemented in Infobuttons, decision support tools that deliver information based on the context of the interaction between a clinician and an EHR [102]. Automatic linking of external knowledge bases and patients’ records will be useful if the NLP systems achieve acceptable accuracy in extraction of bottom-line advice and presentation of this information in an easily comprehensible form. Extraction of the bottom-line advice and answers to clinical questions are presented in the next section.
InfoBot系統下發展，在國家圖書館
醫學標識格式良好的臨床問題[99]在臨床筆記中的元素。隨後，它調用一個問題回答模塊（CQA1.0系統在6.3節所述），提取物為從文獻中發現的問題給予病人最好的照顧計劃有關的問題的答案，並提供包含答案的文件[100]。在16 NIH臨床中心護士的試點評估，評估15個門診病例，文件包含答案發現，在大多數情況下[101]，相關的和有用的。它仍然有待觀察，如果連接的證據病人的記錄等自動化的方法可以實現的更多控制交付的準確性 Infobuttons，決策支持工具，提供信息的，在一個臨床醫生和一個電子病歷[102之間]互動背景下實施。自動連接外部的知識基礎和病人的病歷，將是有益的，如果NLP系統實現可接受的準確度，在底線的意見和容易理解的形式介紹這一信息的提取。提取的底線的意見和臨床問題的答案在下一節。
MedLEE is an NLP system that extracts information from clinical narratives and presents this information in structured form using a controlled vocabulary. MedLEE uses a lexicon to map terms into semantic classes and a semantic grammar to generate formal representation of sentences. It is in use at Columbia University Medical Center, and is one of the few natural language processing systems integrated with clinical information systems. MedLEE has been successfully used to process radiology reports, discharge summaries, sign-out notes, pathology reports, electrocardiogram reports, and echocardiogram reports [10,67–70]. An in-depth overview of the system and a case scenario are provided in [71].
早期醫療 NLP的願景是在語言字符串項目（LSP）系統開發的基本組成部分和臨床敘事的正式代表的實施，並實行自由的文本臨床文件轉化成一個正式的表示[66]。映射到LSP的系統，演變成醫學語言處理器（MLP），其中包括醫療的英語語法詞彙和醫療標籤的詞彙，總綱發展藍圖的解析器，解析英語語法醫療，醫療共生的模式，英國的改造，句法正規化的選擇醫療信息的格式結構，以及一套用於瀏覽和顯示 XML工具。
MedLEE是一種自然語言處理系統，從臨床敘述中提取的信息和使用受控詞表的結構形式呈現信息。 MedLEE使用一個詞彙映射到語義類和語義語法生成句子的正式代表。它是在哥倫比亞大學醫學中心的使用，是為數不多的自然語言處理系統與臨床信息系統集成。 MedLEE已成功地用於處理放射報告，出院摘要，註銷票據，病理報告，心電圖報告，超聲心動圖報告[10,67-70]。 [71]中提供了一個深入系統的概述和情況。
The Text Analytics architecture developed in collaboration between the Mayo Clinic and IBM is using Unstructured Information Management Architecture (UIMA) to identify clinically relevant entities in clinical notes. The entities are subsequently used for information retrieval and data mining [72]. The ongoing development of this architecture resulted in two specialized pipelines:medKAT/P, which extracts cancer characteristics from pathology reports, and cTAKES, which identifies disorders, drugs, anatomical sites, and procedures in clinical notes. Evaluated on a set of manually annotated colon cancer pathology reports, MedTAS/P achieved F1-scores in the 90% range in extraction of histology, anatomical entities, and primary tumors [73]. A lower score achieved for metastatic tumors was attributed to the small number of instances in the training and test sets [73]. cTAKES and HiTEx, described below,are the first generalized clinical NLP systems to be made publicly available.
梅奧診所和IBM之間的合作開發文本分析的架構是使用非結構化信息管理架構（UIMA）在臨床筆記，以確定臨床相關的實體。這些實體隨後用於信息檢索和數據挖掘[72]。這種架構的持續發展造成了兩個專門的管道：medKAT/ P提取癌症的特點，從病理報告，cTAKES，識別障礙，藥物，解剖部位，並在臨床筆記程序。MedTAS/ P手動註明結腸癌的病理報告進行評估，取得90％的範圍內提取組織學，解剖學的F1 -分數實體和原發腫瘤[73]。轉移性腫瘤所取得的分數較低歸因於少數的實例在訓練和測試集[73]。 cTAKES和Hitex和所述，是廣義臨床 NLP的系統可以公之於眾。
Developed at the National Center for Biomedical Computing,Informatics for Integrating Biology & the Bedside (I2B2), the Health Information Text Extraction (HiTEx) tool based on GATE is a modular system that assembles a different pipeline for extracting specific findings from clinical narrative. For example, a pipeline to extract diagnoses is formed by applying sequentially a section splitter, section filter, sentence splitter, sentence tokenizer, POStagger, noun phrase finder, UMLS concept mapper, and negation finder [74]. A pipeline for extraction of family history from discharge summaries and outpatient clinic notes evaluated on 350 sentences achieved 85% precision and 87% recall in identifying diagnoses; 96% precision and 93% recall differentiating family history from patient history; and 92% precision and recall exactly assigning diagnoses to family members [75].
在國家生物醫學計算中心的開發，信息整合生物學及床頭（I2B2），門極的健康信息的文本提取工具（HiTEx）是一個模塊化系統，組裝不同的管道從臨床敘事中提取的具體結果。例如，管道提取的診斷是由應用順序一個部分分離器，過濾器節，句子分配器，句子標記生成器，POStagger，查找名詞短語，UMLS概念映射器，並否定取景器[74]。一個管道提取家族史出院摘要及門診的350句注意到評估的精確度達到85％和87％，在確定診斷召回，96％的精度，從病史和93％召回差異化的家族史;和92％的準確率和召回完全分配家庭成員的診斷[75]。
The MediClass (a ‘‘Medical Classifier”) system was designed to automatically detect clinical events in any electronic medical record by analyzing the coded and free-text portions of the record.It was assessed in detecting care delivery for smoking cessation;immunization adverse events; and subtypes of diabetic retinopathy. Although the system architecture remained constant for each clinical event detection task, new classification rules and terminology were defined for each task [76]. For example, to detect possible vaccine reactions in the clinical notes, MediClass developers identified the relevant concepts and the linguistic structures used in clinical notes to record and attribute an adverse event to an immunization or vaccine [77]. The identified terms and structures were encoded into rules of a MediClass knowledge module that defines the classification scheme for automatic detection of possible vaccine reactions. The scheme requires detecting an explicit mention of an immunization event and detecting or inferring at least one finding of an adverse event [77]. In 227 of 248 cases (92%), MediClass correctly detected a possible vaccine reaction [77].
該 MediClass（a''Medical分類“）系統的目的是要自動檢測通過分析record.It的編碼和自由文本部分被檢測照顧戒菸交付評估任何電子病歷臨床事件;免疫接種後不良事件;... ...而糖尿病視網膜病變的亞型雖然系統架構仍然是每個臨床事件檢測任務不變，新的分類規則和術語為每個任務定義[76]例如，及時發現可能的臨床筆記疫苗反應，MediClass開發商確定有關的概念和臨床記錄用於記錄和不良事件的屬性到一個或疫苗接種的語言結構 [77]。已確定的條款和結構編碼成一個 MediClass知識模塊規則，定義為自動檢測的分類方案可能的疫苗反應，該計劃要求檢測到的免疫活動，並明確提到檢測或推斷至少有一個不良事件的發現[77]，在227248例（92％），MediClass正確偵測到可能的疫苗反應[77]。
5.2. Specialized clinical NLP systems
5.2。專業臨床 NLP系統
The evaluation of the general-purpose architectures in specific tasks and the use of the general-purpose systems as components or foundation of many task- or document-specific systems, make the line between these system types somewhat fuzzy. The differences are most probably not in the end-results but in the initial goals of developing a system to process free text for any task versus solving a specific clinical task. Independent of the starting point and reuse of the general-purpose components, solving a specific task at minimum requires developing a task-specific database and decision rules. Some examples of task-speci?c systems are provided in this section.
評價的具體任務的通用架構和組件或許多基礎任務或文檔特定系統的通用系統的使用，使這些系統類型之間的線有點模糊。差異是最有可能在最終的結果，但在開發一個系統，任何任務與解決一個特定的臨床任務來處理自由文本的初步目標。獨立的出發點和通用組件的重用，至少解決一個特定的任務，需要制定一個特定任務的數據庫和決策規則。本條規定的具體任務系統的一些例子。
5.2.2. Processing radiology reports
Radiology reports are probably the most studied type of clinical narrative. This extremely important source of clinical data provides information not otherwise available in the coded data and allows performing tasks from coding of the findings and impressions [67,80], to detection of imaging technique suggested for followup or repeated examinations [81], to decision support for nosocomial infections [19], to biosurveillance [82]. The complete description of systems developed for processing of radiology reports is beyond the scope of this review. This section outlines the scope and research directions in processing of radiology reports and omits most of the radiology report studies based on the described above general-purpose systems.
5.2.2。處理放射報告
放射科報告可能是臨床敘事研究最多的類型。這極其重要的臨床資料來源提供的信息不能編碼數據，並允許執行任務編碼[67,80]，建議後續或重複檢查的成像技術檢測的結果和印象[81]，決定對院內感染的支持[19]，生物監測[82]。放射報告處理開發完整的系統描述，是本次審查的範圍之外。本節概述了在處理放射報告的範圍和研究方向，並省略了大部分的放射報告的研究基礎上，上述通用系統描述。
A family of systems with the initial goal of processing radiology
reports was developed at the LDS Hospital (Intermountain Healthcare). The Special Purpose Radiology Understanding System (SPRUS) extracts and encodes the findings and the radiologists’ interpretations using information from a diagnostic expert system [80]. SPRUS was followed by the Natural language Understanding Systems (NLUS) and Symbolic Text Processor (SymText) systems that combine semantic knowledge stored and applied in the form of a Bayesian Network with syntactic analysis based on a set of
augmented transition network (ATN) grammars [83,84]. SymText was deployed at LDS Hospital for semi-automatic coding of admit diagnoses to ICD-9 codes [85]. SymText was also used to automatically extract interpretations from Ventilation/Perfusion lung scan reports for monitoring diagnostic performance of radiologists [86]. The accuracy of the system in identifying pneumonia-related concepts and inferring the presence or absence of acute bacterial neumonia was evaluated using 292 chest X-ray reports annotated by physicians and lay persons. The 95% recall, 78% precision, and 85% specificity achieved by the system were comparable to that of physicians and better than that of lay persons [19]. SymText evolved to MPLUS (M+), which also uses a semantic model based on Bayesian Networks (BNs), but differs from SymText in the size and modularity of its semantic BNs and in its use of a chart parser [87]. M+ was evaluated for the extraction of American College of Radiology utilization review codes from 600 head CT reports. The system achieved 87% recall, 98% specificity and 85% precision in classifying reports as positive (containing brain conditions) [87].
一個家庭系統與最初的目標處理放射報告是在LDS醫院（山間健康護理）。特殊用途放射了解系統（SPRUS）提取和編碼結果，並在放射科醫生“從診斷專家系統使用的信息的解釋[80]。 SPRUS其次是自然語言理解（NLUS）和符號文字處理器（SymText）系統結合語義知識的形式存儲和應用基於一組與句法分析的貝葉斯網絡擴充轉移網絡（ATN）文法[83,84]。 SymText部署在LDS醫院承認半自動編碼ICD- 9編碼的診斷[85]。 SymText也被用來自動ically提取肺通氣 /灌注掃描的解釋監測的放射診斷性能報告[86]。在確定肺炎相關系統的精度概念和推理急性細菌的存在或缺乏肺炎是評估使用292胸部X光報告註明醫生和非法律專業人士。召回95％，78％的精度，系統所取得的85％，特異性相媲美，醫生和優於非法律專業人士[19]。 SymText到MPLUS（M +），還採用了基於語義模型的演變貝葉斯網絡（BNS）在大小不同，但是從 SymText和其語義 BNS的模塊化，並在其使用圖表分析器[87]。 M +的評估，美國學院的提取放射利用率從600頭顱 CT報告審查代碼。 “系統實現召回87％，特異性98％和85％的精度分類為陽性的報告（含腦條件）[87]。
M+ was also evaluated for classifying chief complaints into syndrome categories [88]. Currently, M+ has been redesigned as Onyx and is being applied to spoken dental exams [89]. These evolving NLP systems provide examples of successful retargeting to coding of other types of clinical reports.
M +的評價分為綜合徵分類[88]行政投訴。目前，M +的已經被重新設計為瑪瑙，並正在發言[89]牙科檢查。這些不斷發展的自然語言處理系統提供的成功重定向到其他類型的臨床報導的編碼的例子。
The REgenstrief data eXtraction tool (REX) coded raw version 2.x Health Level 7 (HL7) messages to a targeted small to medium sized sets of concepts for a particular purpose in a given kind of narrative text [90]. REX was applied to 39,000 chest X-rays performed at Wishard Hospital in a 21-month period to identify findings related to CHF, tuberculosis, pneumonia, suspected malignancy, compression fractions, and several other disorders. REX achieved 100% specificity for all conditions, 94–100% sensitivity,and 95–100% positive predictive value, outperforming human coders in sensitivity [90]. In contrast, mapping six types of radiology reports to a UMLS subset and then selectively recognizing most salient concepts using information retrieval techniques, resulted in 63% recall and 30% precision [91].
REgenstrief數據提取工具（REX）編碼的原始版本2.x的健康水平7（HL7）的消息有針對性的小為特定目的在敘事文本[90]一種中型集的概念。 Rex是應用到39000威舍德醫院在21個月內，以確定瑞士法郎，肺結核，肺炎，疑有惡變，壓縮分數，和其他一些疾病有關的調查結果進行胸部X射線。 REX取得的所有條件，94-100％的敏感性，以及95-100％，陽性預測值100％的特異性，在敏感性方面贏過人類編碼[90]。相比之下，映射六種類型到UMLS子集的放射報告，然後選擇性地認識到使用信息檢索技術的最顯著的概念，導致63％的召回和30％的精度[91]。
5.2.3. Processing emergency department reports
Topaz targets 55 clinical conditions relevant for detecting pa-
tients with an acute lower respiratory syndrome [92]. Topaz uses three methods for mapping text to the 55 conditions: index UMLS concepts with MetaMap [93]; create compound concepts from UMLS concepts or keywords and section titles (e.g., Section:Neck + UMLS concept for lymphadenopathy = Cervical Lymphadenopathy); and identify measurement-value pairs (e.g.,‘‘temp” + number > 38 degrees Celsius = Fever). Topaz is built on the GATE platform and implements ConText as a GATE module for determining whether indexed conditions are present or absent,experienced by the patient or someone else, and historical, recent,or hypothetical. After integrating potentially multiple mentions of a condition from a report, agreement between Topaz and physicians reading the report was 0.85 using weighted kappa.
5.2.3。處理緊急情況部報告
黃玉目標檢測患者急性下呼吸道綜合徵[92]55有關的臨床情況。黃玉使用的55個條件的映射文本的三種方法：指數 UMLS與 MetaMap的概念[93]; UMLS概念或關鍵詞，節的標題（例如，第：頸部+=頸部淋巴結腫大淋巴結腫大 UMLS概念）創建複合概念;並確定測量值對（例如，“溫度”+數字>=發燒38攝氏度度）。黃玉是建立在門平台，並實現為確定是否索引條件是存在或不存在的，由患者或別人的經驗豐富，歷史，最近，或假想的門模塊中。整合存在多種後的從報告中的一個條件提到，黃玉和醫生閱讀報告之間的協議是0.85，使用加權卡帕
5.2.4. Processing pathology reports
Surgical pathology reports are another trove of clinical data for locating information about appropriate human tissue specimens [94] and supporting cancer research. For example, a preprocessor integrated with MedLEE to abstract 13 types of findings related to risks of developing breast cancer achieved a sensitivity of 90.6% and a precision of 91.6% [69].
In MEDSYNDIKATE, an NLP system for extraction of medical information from pathology reports, the basic sentence-level understanding of the clinical narrative (that takes into consideration grammatical knowledge, conceptual knowledge, and the link between syntactic and conceptual representations) is followed by the text-level analysis that tracks reference relations to eliminate representation errors [95].
Liu et al. assessed the feasibility of utilizing an existing GATE pipeline for extraction of the Gleason score (a measure of tumor grade), tumor stage, and status of lymph node metastasis from free-text pathology reports [96]. The pipeline was evaluated on committing errors related to the text processing and extraction of values from the report, and errors related to semantic disagreement between the report and the gold standard. Each variable had a different profile of errors. Numerous system errors were observed for Gleason Score extraction that requires fine distinctions and TNM stage extraction requiring multiple discrete decisions.
The authors conclude that the existing system could be used to aid manual annotation or could be extended for automatic annotation [96]. These findings second observations of Schadow and McDonald that general-purpose tools and vocabularies need to be adapted to the specific needs of surgical pathology reports [94]. The Cancer Text Information Extraction System (caTIES) system, built on a GATE framework, uses MetaMap [93] and NegEx [51] to annotate findings, diagnoses, and anatomic locations in pathology reports. caTIES provides researchers with the ability to query, browse and create orders for annotated tissue data and physical material across a network of federated sources using automatically annotated pathology reports.
5.2.4。處理病理報告
手術病理報告的其他臨床資料的寶庫，為尋找適當的人體組織標本的信息[94]和支持癌症研究。例如，預處理器集成MedLEE抽象13的結果患上乳腺癌的風險，實現了90.6％的敏感性和91.6％的精度[69]。
MEDSYNDIKATE，病理報告為醫療信息的提取自然語言處理系統，基本句子級的臨床敘事的理解（即需要考慮語法知識，概念性知識，語法和概念的陳述之間的聯繫）其次是文本層次分析跟踪參考的關係，以消除代表性的錯誤 [95]。
Liu等人。評估利用現有門提取的Gleason評分管道（衡量腫瘤分級），腫瘤分期和狀態從自由文本的病理報告 [96]淋巴結轉移的可行性。該管道進行了評估，提交有關的文字處理和從報告中提取值的錯誤，和錯誤有關的報告和金標準的語義之間的分歧。每個變量有不同的配置文件的錯誤。眾多的系統誤差，觀察 Gleason評分提取需要的細微差別和TNM分期提取，需要多個分立的決定。
作者得出結論：現有的系統可以用於急救手冊註釋，或可自動標註 [96]延長。這些發現需要適應手術病理報告的具體需求，通用工具和詞彙 Schadow和麥當勞的第二個觀察 [94]。癌的文本信息提取系統（caTIES）系統，建立在一個門框架，使用MetaMap [93]和NegEx [51]註釋在病理報告結果，診斷和解剖位置。 caTIES提供註明組織的數據和整個網絡的聯合使用自動註明的病理報告來源的物理材料的訂單查詢，瀏覽和創造能力的研究人員。
5.2.5. Processing a mixture of clinical note types
The above studies indicate that natural language processing acceptable for clinical decision support is better achieved using tools developed for specific tasks and document types. It is therefore not surprising that processing of a mixture of clinical notes is successful when the task is well-defined and a small knowledge base is developed specifically for the task. For example, Meystre and Haug created a subset of the UMLS Metathesaurus for 80 problems of interest to their longitudinal Electronic Medical Record and evaluated extraction of these problems from 160 randomly selected discharge summaries, radiology reports, pathology reports,progress notes, and other document types [97]. The evaluation demonstrated that using a general purpose entity extraction tool with a custom data subset, disambiguation, and negation detection achieves 89.2% recall and 75.3% precision [97].
5.2.5。處理臨床注意類型的混合物
上述研究表明，自然語言處理，可以接受的臨床決策支持，更好地實現使用的具體任務和文件類型的開發工具。因此，也就不足為奇了臨床票據的混合物處理任務時，成功的定義，並專門任務開發一個小的知識基礎。例如，Meystre和豪格創造了UMLS Metathesaurus80關心的問題，其縱向的電子病歷和160隨機選擇出院摘要，放射科報告，病理報告，進度說明，以及其它類型的文檔，這些問題的評估提取的子集[97]。評估表明，使用一個自定義的數據子集，消歧，並否定檢測的一個通用實體提取工具實現召回89.2％和75.3％的精度[97]。
The MediClass system [76] was configured to automatically assess delivery of evidence-based smoking-cessation care [98]. A group of clinicians and tobacco-cessation experts met over several weeks to encode the recommended treatment model using the concepts and the types of phrases that provide evidence for smoking-cessation medications, discussions, referral activities, quitting activities, smoking and readiness-to-quit assessments. The treatment model involves five steps, ‘‘5A’s”: (1) ask about smoking status; (2) advise to quit; (3) assess a patient’s willingness to quit; (4)assist the patient’s quitting efforts; and (5) arrange follow-up. Evaluated on 500 patient records containing structured data in addition to progress notes, patient instructions, medications, referrals, reasons for visit, and other smoking-related data, MediClass performance was judged adequate to replace human coders of the 5A’s of smoking-cessation care [98].
MediClass系統[76]被配置為自動評估證據為基礎的戒菸護理[98]的交付。 A組的臨床醫生和煙草戒菸專家會見了幾個星期的編碼建議的治療模式，使用，提供戒菸藥物，討論，轉介活動，戒菸活動，吸煙和準備到的證據的概念和類型的短語退出評估。五個步驟，該治療模式“5A的”：（1）詢問有關吸煙狀況;（2）告知退出;（3）評估病人的戒菸意願;（4）協助病人戒菸的努力;和（5）安排跟進。除了進展注意到的結構化數據500門診記錄評價，病人的指示，藥物，轉介，參觀的原因和其他與吸煙有關的，MediClass性能判斷足夠取代的戒菸保健5A的人類編碼[ 98]。
The InfoBot system under development at the National Library
of Medicine identifies the elements of a well-formed clinical question [99] in clinical notes. It subsequently invokes a question answering module (the CQA 1.0 system described in Section 6.3)that extracts answers to the question about the best care plan for a given patient with the identified problems from the literature,and delivers documents containing the answers [100]. In a pilot evaluation by 16 NIH Clinical Center nurses, each evaluating 15 patient cases, documents containing answers were found to be relevant and useful in the majority of cases [101]. It remains to be seen if such automated methods of linking evidence to a patient’s record can achieve the accuracy of more controlled delivery implemented in Infobuttons, decision support tools that deliver information based on the context of the interaction between a clinician and an EHR [102]. Automatic linking of external knowledge bases and patients’ records will be useful if the NLP systems achieve acceptable accuracy in extraction of bottom-line advice and presentation of this information in an easily comprehensible form. Extraction of the bottom-line advice and answers to clinical questions are presented in the next section.
InfoBot系統下發展，在國家圖書館
醫學標識格式良好的臨床問題[99]在臨床筆記中的元素。隨後，它調用一個問題回答模塊（CQA1.0系統在6.3節所述），提取物為從文獻中發現的問題給予病人最好的照顧計劃有關的問題的答案，並提供包含答案的文件[100]。在16 NIH臨床中心護士的試點評估，評估15個門診病例，文件包含答案發現，在大多數情況下[101]，相關的和有用的。它仍然有待觀察，如果連接的證據病人的記錄等自動化的方法可以實現的更多控制交付的準確性 Infobuttons，決策支持工具，提供信息的，在一個臨床醫生和一個電子病歷[102之間]互動背景下實施。自動連接外部的知識基礎和病人的病歷，將是有益的，如果NLP系統實現可接受的準確度，在底線的意見和容易理解的形式介紹這一信息的提取。提取的底線的意見和臨床問題的答案在下一節。
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