Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore.
Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore.
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DOI：
10.2196/medinform.8204
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发表时间：
2018-06-11
影响因子：
3.2
通讯作者：
Phang JSK
中科院分区：
文献类型：
--
作者：
Hardjojo A;Gunachandran A;Pang L;Abdullah MRB;Wah W;Chong JWC;Goh EH;Teo SH;Lim G;Lee ML;Hsu W;Lee V;Chen MI;Wong F;Phang JSK
自由文本临床记录提供了一个信息来源，补充了传统的疾病监测。为了以电子方式利用这些记录，需要通过自然语言处理算法将它们转换为编码字段。本研究的目的是开发，训练和验证临床病史提取器用于综合征监测（CHESS），这是一种自然语言处理算法，用于从自由文本初级保健记录中提取临床信息。CHESS是一种基于关键词的自然语言处理算法，可提取48种提示呼吸道感染、胃肠道感染、体质性疾病以及其他可能与传染病相关的体征和症状。该算法还捕获断言状态（肯定、否定或怀疑）和症状持续时间。随机提取来自国家医疗保健集团综合诊所（新加坡的主要公共部门初级保健提供者）的电子病历，并由2名人工审查员手动审查，第三名审查员作为裁定员。该算法基于1680个注释进行评估，以人类编码的结果作为参考标准，其中一半数据用于训练，另一半用于验证。1680份临床记录中最常见的症状是呼吸道感染中常见的症状，如咳嗽（744/7703，9.66%）、咽喉痛（591/7703，7.67%）、溢液（552/7703，7.17%）和发热（928/7703，12.04%）。在事件水平上，CHESS在训练数据集上的总体性能为96.7%的精确度和97.6%的召回率，在验证数据集上的总体性能为96.0%的精确度和93.1%的召回率。提示呼吸道和胃肠道感染的症状都以超过90%的准确率和召回率被检测到。CHESS分别在97.3%、97.9%和89.8%的肯定、否定和怀疑体征和症状中正确分配断言状态（总体准确率为97.6%）。症状发作持续时间在81.2%的已知持续时间状态的记录中被正确识别。我们开发了一种名为CHESS的自然语言处理算法，该算法在从初级保健自由文本临床记录中提取体征和症状方面表现良好。除了症状的存在，我们的算法还可以准确地区分肯定，否定和怀疑断言状态和提取症状持续时间。
Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms. The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records. CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration. Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation. The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status. We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations.
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DOI：
10.1016/s1386-5056(02)00057-6
发表时间：
2002-12-04
影响因子：
4.9
作者：
Ruch, P;Baud, R;Geissb端hler, A
通讯作者：
Geissb端hler, A
影响因子：
120.7
作者：
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通讯作者：
Speroff, Theodore
DOI：
10.1136/jamia.2010.003210
发表时间：
2010-09-01
影响因子：
6.4
作者：
Chapman, Wendy W.;Dowling, John N.;Lober, Bill
通讯作者：
Lober, Bill
DOI：
10.1093/cid/cix694
发表时间：
2017-11-13
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
Goh EH;Jiang L;Hsu JP;Tan LWL;Lim WY;Phoon MC;Leo YS;Barr IG;Chow VTK;Lee VJ;Lin C;Lin R;Sadarangani SP;Young B;Chen MI
通讯作者：
Chen MI
影响因子：
3
作者：
Cohen R;Elhadad M;Elhadad N
通讯作者：
Elhadad N