Classification of neurologic outcomes from medical notes using natural language processing.

Autor: Fernandes MB; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States., Valizadeh N; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States., Alabsi HS; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States., Quadri SA; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States., Tesh RA; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States., Bucklin AA; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States., Sun H; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States., Jain A; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States., Brenner LN; Harvard Medical School, Boston, MA, United States.; Division of Pulmonary and Critical Care Medicine, MGH, Boston, MA, United States.; Division of General Internal Medicine, MGH, Boston, MA, United States., Ye E; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States., Ge W; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States., Collens SI; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States., Lin S; Harvard Medical School, Boston, MA, United States., Das S; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States., Robbins GK; Harvard Medical School, Boston, MA, United States.; Division of Infectious Diseases, MGH, Boston, MA, United States., Zafar SF; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States., Mukerji SS; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Vaccine and Immunotherapy Center, Division of Infectious Diseases, MGH, Boston, MA, United States., Westover MB; Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States.; Harvard Medical School, Boston, MA, United States.; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States.; McCance Center for Brain Health, MGH, Boston, MA, United States.
Jazyk: angličtina
Zdroj: Expert systems with applications [Expert Syst Appl] 2023 Mar 15; Vol. 214. Date of Electronic Publication: 2022 Nov 06.
DOI: 10.1016/j.eswa.2022.119171
Abstrakt: 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.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Databáze: MEDLINE
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