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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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Autores principales: 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., Westover, M. Brandon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974159/
https://www.ncbi.nlm.nih.gov/pubmed/36865787
http://dx.doi.org/10.1016/j.eswa.2022.119171
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author 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.
Westover, M. Brandon
author_facet 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.
Westover, M. Brandon
author_sort Fernandes, Marta B.
collection PubMed
description 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.
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spelling pubmed-99741592023-03-15 Classification of neurologic outcomes from medical notes using natural language processing 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. Westover, M. Brandon Expert Syst Appl Article 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. 2023-03-15 2022-11-06 /pmc/articles/PMC9974159/ /pubmed/36865787 http://dx.doi.org/10.1016/j.eswa.2022.119171 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ).
spellingShingle Article
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.
Westover, M. Brandon
Classification of neurologic outcomes from medical notes using natural language processing
title Classification of neurologic outcomes from medical notes using natural language processing
title_full Classification of neurologic outcomes from medical notes using natural language processing
title_fullStr Classification of neurologic outcomes from medical notes using natural language processing
title_full_unstemmed Classification of neurologic outcomes from medical notes using natural language processing
title_short Classification of neurologic outcomes from medical notes using natural language processing
title_sort classification of neurologic outcomes from medical notes using natural language processing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9974159/
https://www.ncbi.nlm.nih.gov/pubmed/36865787
http://dx.doi.org/10.1016/j.eswa.2022.119171
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