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。
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