Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning

AbstractObjectiveAlcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse.Materials and MethodsAn observational cohort of 1422 adult patients admitted to a trauma center between April 2013 and November 2016. Linguistic processing of clinical notes was performed using the clinical Text Analysis and Knowledge Extraction System. The primary analysis was the binary classification of alcohol misuse. The Alcohol Use Disorders Identification Test served as the reference standard.ResultsThe data corpus comprised 91 045 electronic health record notes and 16 091 features. In the final machine learning classifier, 16 features were selected from the first 24 hours of notes for identifying alcohol misuse. The classifier’s performance in the validation cohort had an area under the receiver-operating characteristic curve of 0.78 (95% confidence interval [CI], 0.72 to 0.85). Sensitivity and specificity were at 56.0% (95% CI, 44.1% to 68.0%) and 88.9% (95% CI, 84.4% to 92.8%). The Hosmer-Lemeshow goodness-of-fit test demonstrates the classifier fits the data well (P = .17). A simpler rule-based keyword approach had a decrease in sensitivity when compared with the NLP classifier from 56.0% to 18.2%.ConclusionsThe NLP classifier has adequate predictive validity for identifying alcohol misuse in trauma centers. External validation is needed before its application to augment screening.

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Development and Validation of a Natural Language Processing Algorithm to Extract Descriptors of Microbial Keratitis From the Electronic Health Record

Cornea ◽

10.1097/ico.0000000000002755 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Maria A. Woodward ◽

Nenita Maganti ◽

Leslie M. Niziol ◽

Sejal Amin ◽

Andrew Hou ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Electronic Health Record ◽

Language Processing ◽

Processing Algorithm ◽

Health Record ◽

Microbial Keratitis ◽

Electronic Health ◽

Development And Validation ◽

Natural Language Processing Algorithm

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Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data

10.1101/19011643 ◽

2019 ◽

Author(s):

Daniel M. Bean ◽

James Teo ◽

Honghan Wu ◽

Ricardo Oliveira ◽

Raj Patel ◽

...

Keyword(s):

Atrial Fibrillation ◽

Natural Language Processing ◽

Natural Language ◽

Electronic Health Record ◽

Open Source ◽

Language Processing ◽

Risk Scores ◽

Free Text ◽

Health Record ◽

Electronic Health

AbstractAtrial fibrillation (AF) is the most common arrhythmia and significantly increases stroke risk. This risk is effectively managed by oral anticoagulation. Recent studies using national registry data indicate increased use of anticoagulation resulting from changes in guidelines and the availability of newer drugs.The aim of this study is to develop and validate an open source risk scoring pipeline for free-text electronic health record data using natural language processing.AF patients discharged from 1st January 2011 to 1st October 2017 were identified from discharge summaries (N=10,030, 64.6% male, average age 75.3 ± 12.3 years). A natural language processing pipeline was developed to identify risk factors in clinical text and calculate risk for ischaemic stroke (CHA2DS2-VASc) and bleeding (HAS-BLED). Scores were validated vs two independent experts for 40 patients.Automatic risk scores were in strong agreement with the two independent experts for CHA2DS2-VASc (average kappa 0.78 vs experts, compared to 0.85 between experts). Agreement was lower for HAS-BLED (average kappa 0.54 vs experts, compared to 0.74 between experts).In high-risk patients (CHA2DS2-VASc ≥2) OAC use has increased significantly over the last 7 years, driven by the availability of DOACs and the transitioning of patients from AP medication alone to OAC. Factors independently associated with OAC use included components of the CHA2DS2-VASc and HAS-BLED scores as well as discharging specialty and frailty. OAC use was highest in patients discharged under cardiology (69%).Electronic health record text can be used for automatic calculation of clinical risk scores at scale. Open source tools are available today for this task but require further validation. Analysis of routinely-collected EHR data can replicate findings from large-scale curated registries.

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