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Predicting Inpatient Medication Orders From Electronic Health Record Data
In a general inpatient population, we predicted patient‐specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine‐learning models: A deep learning seque...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325318/ https://www.ncbi.nlm.nih.gov/pubmed/32141068 http://dx.doi.org/10.1002/cpt.1826 |
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author | Rough, Kathryn Dai, Andrew M. Zhang, Kun Xue, Yuan Vardoulakis, Laura M. Cui, Claire Butte, Atul J. Howell, Michael D. Rajkomar, Alvin |
author_facet | Rough, Kathryn Dai, Andrew M. Zhang, Kun Xue, Yuan Vardoulakis, Laura M. Cui, Claire Butte, Atul J. Howell, Michael D. Rajkomar, Alvin |
author_sort | Rough, Kathryn |
collection | PubMed |
description | In a general inpatient population, we predicted patient‐specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine‐learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient’s discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty‐five percent of medications ordered by physicians were ranked in the sequence model’s top‐10 predictions (logistic model: 49%) and 75% ranked in the top‐25 (logistic model: 69%). Ninety‐three percent of the sequence model’s top‐10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR. |
format | Online Article Text |
id | pubmed-7325318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73253182020-07-01 Predicting Inpatient Medication Orders From Electronic Health Record Data Rough, Kathryn Dai, Andrew M. Zhang, Kun Xue, Yuan Vardoulakis, Laura M. Cui, Claire Butte, Atul J. Howell, Michael D. Rajkomar, Alvin Clin Pharmacol Ther Research In a general inpatient population, we predicted patient‐specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine‐learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient’s discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty‐five percent of medications ordered by physicians were ranked in the sequence model’s top‐10 predictions (logistic model: 49%) and 75% ranked in the top‐25 (logistic model: 69%). Ninety‐three percent of the sequence model’s top‐10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR. John Wiley and Sons Inc. 2020-04-11 2020-07 /pmc/articles/PMC7325318/ /pubmed/32141068 http://dx.doi.org/10.1002/cpt.1826 Text en © 2020 Google. Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Rough, Kathryn Dai, Andrew M. Zhang, Kun Xue, Yuan Vardoulakis, Laura M. Cui, Claire Butte, Atul J. Howell, Michael D. Rajkomar, Alvin Predicting Inpatient Medication Orders From Electronic Health Record Data |
title | Predicting Inpatient Medication Orders From Electronic Health Record Data |
title_full | Predicting Inpatient Medication Orders From Electronic Health Record Data |
title_fullStr | Predicting Inpatient Medication Orders From Electronic Health Record Data |
title_full_unstemmed | Predicting Inpatient Medication Orders From Electronic Health Record Data |
title_short | Predicting Inpatient Medication Orders From Electronic Health Record Data |
title_sort | predicting inpatient medication orders from electronic health record data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325318/ https://www.ncbi.nlm.nih.gov/pubmed/32141068 http://dx.doi.org/10.1002/cpt.1826 |
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