Extracting information from the text of electronic medical records to improve case detection: a systematic review

By E. Ford, J. Carroll, Helen E. Smith, D. Scott, J. Cassell. J. Am. Medical Informatics Assoc., 2016

384 citations recorded by the source corpus.

Abstract Background Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall).

Subfields: Artificial Intelligence, Health Information Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bbd1fcf20db62f496b2676eda0c38fbc1590d1a8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Health Information Management

In Molecular Biology

Citations in the corpus

Two-stage Federated Phenotyping and Patient Representation Learning (2019, background): “Converting the unstructured data into useful structured representations will not only help data analysis but also improve efﬁciency in clinical practice (Jagan-nathan et al., 2009; Kreimeyer et al., 2017; Ford et al., 2016; Demner-Fushman et al., 2009; Murff et al., 2011; Friedman et al., 2004).”
Weight Poisoning Attacks on Pretrained Models (2020, background): “These attacks have serious implications: NLP is already used in content ﬁlters and fraud detection systems (Adams et al., 2017; Rajan and Gill, 2012), essay grading algorithms (Zhang, 2013), and legal and medical ﬁltering systems (Qadrud-Din, 2019; Ford et al., 2016).”
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances (2018, neutral): “Such features include the salience of the self-reported experience (i.e. mental health symptoms), determining treatment initiation and outcome evaluation, as well as the complex circumstances influencing presentations and prognoses (e.g., social support networks, recent or past stressful…”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, background): “Ford et al [13] present a systematic review of 67 papers using IE techniques applied to medical records for the purpose of case detection (ie, finding occurrences of specific medical conditions).”