Classification of neurologic outcomes from medical notes using natural language processing

Classification of neurologic outcomes from medical notes using natural language processing

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 challeng...

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Veröffentlicht in:Expert systems with applications. - 1999. - 214(2023) vom: 15. März
1. Verfasser: Fernandes, Marta B (VerfasserIn)
Weitere Verfasser: 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, Westover, M Brandon
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:Expert systems with applications
Schlagworte:Journal Article Coronavirus Glasgow outcome scale Intensive care unit Machine learning Modified Rankin Scale Natural language processing
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520 |a 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 
650 4 |a Journal Article 
650 4 |a Coronavirus 
650 4 |a Glasgow outcome scale 
650 4 |a Intensive care unit 
650 4 |a Machine learning 
650 4 |a Modified Rankin Scale 
650 4 |a Natural language processing 
700 1 |a Valizadeh, Navid  |e verfasserin  |4 aut 
700 1 |a Alabsi, Haitham S  |e verfasserin  |4 aut 
700 1 |a Quadri, Syed A  |e verfasserin  |4 aut 
700 1 |a Tesh, Ryan A  |e verfasserin  |4 aut 
700 1 |a Bucklin, Abigail A  |e verfasserin  |4 aut 
700 1 |a Sun, Haoqi  |e verfasserin  |4 aut 
700 1 |a Jain, Aayushee  |e verfasserin  |4 aut 
700 1 |a Brenner, Laura N  |e verfasserin  |4 aut 
700 1 |a Ye, Elissa  |e verfasserin  |4 aut 
700 1 |a Ge, Wendong  |e verfasserin  |4 aut 
700 1 |a Collens, Sarah I  |e verfasserin  |4 aut 
700 1 |a Lin, Stacie  |e verfasserin  |4 aut 
700 1 |a Das, Sudeshna  |e verfasserin  |4 aut 
700 1 |a Robbins, Gregory K  |e verfasserin  |4 aut 
700 1 |a Zafar, Sahar F  |e verfasserin  |4 aut 
700 1 |a Mukerji, Shibani S  |e verfasserin  |4 aut 
700 1 |a Westover, M Brandon  |e verfasserin  |4 aut 
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856 4 0 |u http://dx.doi.org/10.1016/j.eswa.2022.119171  |3 Volltext 
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