Classification of neurologic outcomes from medical notes using natural language processing. (15th March 2023)

• Record Type: Journal Article
• Authors: Fernandes, Marta B.; Valizadeh, Navid; Alabsi, Haitham S.; Quadri, Syed A.; Tesh, Ryan A.; Bucklin, Abigail A.; Sun, Haoqi; Jain, Aayushee; Brenner, Laura N.; Ye, Elissa; Ge, Wendong; Collens, Sarah I.; Lin, Stacie; Das, Sudeshna; Robbins, Gregory K.; Zafar, Sahar F.; Mukerji, Shibani S.; Brandon Westover, M.
• Is Part Of: Expert systems with applications. Volume 214(2023)
• Publication Date: 2023-03-15
• Subjects: Intensive care unit -- Coronavirus -- Glasgow outcome scale -- Modified Rankin Scale -- Natural language processing -- Machine learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119171
• Languages: English
• ISSNs: 0957-4174

Abstract

Graphical abstract: Highlights: Neurologic outcomes are typically extracted by manual chart review of EHR notes. Chart review is laborious, limiting the scope of EHR based neurologic outcome studies. The NLP model can automatically extract neurological outcomes from medical notes. The NLP model has potential to accelerate the scale of research with EHR data. Abstract: Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely 'good recovery', 'moderate disability', 'severe disability', and 'death' and on the Modified Rankin Scale (mRS), with 7 classes, namely 'no symptoms', 'no significant disability', 'slight disability', 'moderate disability', 'moderately severe disability', 'severe disability', and 'death'. For 428 patients' notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93–0.95) and 0.77 (0.75–0.80) for GOS, and 0.90 (0.89–0.91) and 0.59 (0.57–0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.