Clinical notes as prognostic markers of mortality associated with diabetes mellitus following critical care : A retrospective cohort analysis using machine learning and unstructured big data
(2021) In Computers in Biology and Medicine 132.- Abstract
BACKGROUND: Clinical notes are ubiquitous resources offering potential value in optimizing critical care via data mining technologies.
OBJECTIVE: To determine the predictive value of clinical notes as prognostic markers of 1-year all-cause mortality among people with diabetes following critical care.
MATERIALS AND METHODS: Mortality of diabetes patients were predicted using three cohorts of clinical text in a critical care database, written by physicians (n = 45253), nurses (159027), and both (n = 204280). Natural language processing was used to pre-process text documents and LASSO-regularized logistic regression models were trained and tested. Confusion matrix metrics of each model were calculated and AUROC estimates... (More)
BACKGROUND: Clinical notes are ubiquitous resources offering potential value in optimizing critical care via data mining technologies.
OBJECTIVE: To determine the predictive value of clinical notes as prognostic markers of 1-year all-cause mortality among people with diabetes following critical care.
MATERIALS AND METHODS: Mortality of diabetes patients were predicted using three cohorts of clinical text in a critical care database, written by physicians (n = 45253), nurses (159027), and both (n = 204280). Natural language processing was used to pre-process text documents and LASSO-regularized logistic regression models were trained and tested. Confusion matrix metrics of each model were calculated and AUROC estimates between models were compared. All predictive words and corresponding coefficients were extracted. Outcome probability associated with each text document was estimated.
RESULTS: Models built on clinical text of physicians, nurses, and the combined cohort predicted mortality with AUROC of 0.996, 0.893, and 0.922, respectively. Predictive performance of the models significantly differed from one another whereas inter-rater reliability ranged from substantial to almost perfect across them. Number of predictive words with non-zero coefficients were 3994, 8159, and 10579, respectively, in the models of physicians, nurses, and the combined cohort. Physicians' and nursing notes, both individually and when combined, strongly predicted 1-year all-cause mortality among people with diabetes following critical care.
CONCLUSION: Clinical notes of physicians and nurses are strong and novel prognostic markers of diabetes-associated mortality in critical care, offering potentially generalizable and scalable applications. Clinical text-derived personalized risk estimates of prognostic outcomes such as mortality could be used to optimize patient care.
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- author
- De Silva, Kushan ; Mathews, Noel ; Teede, Helena ; Forbes, Andrew ; Jönsson, Daniel LU ; Demmer, Ryan T and Enticott, Joanne
- publishing date
- 2021-05
- type
- Contribution to journal
- publication status
- published
- keywords
- Big Data, Critical Care, Diabetes Mellitus, Electronic Health Records, Humans, Machine Learning, Natural Language Processing, Prognosis, Reproducibility of Results, Retrospective Studies
- in
- Computers in Biology and Medicine
- volume
- 132
- article number
- 104305
- publisher
- Elsevier
- external identifiers
-
- pmid:33705995
- scopus:85102144887
- ISSN
- 1879-0534
- DOI
- 10.1016/j.compbiomed.2021.104305
- language
- English
- LU publication?
- no
- additional info
- Copyright © 2021 Elsevier Ltd. All rights reserved.
- id
- ae693be1-8001-419a-b920-524899f67231
- date added to LUP
- 2024-07-04 10:45:25
- date last changed
- 2024-07-19 07:13:12
@article{ae693be1-8001-419a-b920-524899f67231,
abstract = {{<p>BACKGROUND: Clinical notes are ubiquitous resources offering potential value in optimizing critical care via data mining technologies.</p><p>OBJECTIVE: To determine the predictive value of clinical notes as prognostic markers of 1-year all-cause mortality among people with diabetes following critical care.</p><p>MATERIALS AND METHODS: Mortality of diabetes patients were predicted using three cohorts of clinical text in a critical care database, written by physicians (n = 45253), nurses (159027), and both (n = 204280). Natural language processing was used to pre-process text documents and LASSO-regularized logistic regression models were trained and tested. Confusion matrix metrics of each model were calculated and AUROC estimates between models were compared. All predictive words and corresponding coefficients were extracted. Outcome probability associated with each text document was estimated.</p><p>RESULTS: Models built on clinical text of physicians, nurses, and the combined cohort predicted mortality with AUROC of 0.996, 0.893, and 0.922, respectively. Predictive performance of the models significantly differed from one another whereas inter-rater reliability ranged from substantial to almost perfect across them. Number of predictive words with non-zero coefficients were 3994, 8159, and 10579, respectively, in the models of physicians, nurses, and the combined cohort. Physicians' and nursing notes, both individually and when combined, strongly predicted 1-year all-cause mortality among people with diabetes following critical care.</p><p>CONCLUSION: Clinical notes of physicians and nurses are strong and novel prognostic markers of diabetes-associated mortality in critical care, offering potentially generalizable and scalable applications. Clinical text-derived personalized risk estimates of prognostic outcomes such as mortality could be used to optimize patient care.</p>}},
author = {{De Silva, Kushan and Mathews, Noel and Teede, Helena and Forbes, Andrew and Jönsson, Daniel and Demmer, Ryan T and Enticott, Joanne}},
issn = {{1879-0534}},
keywords = {{Big Data; Critical Care; Diabetes Mellitus; Electronic Health Records; Humans; Machine Learning; Natural Language Processing; Prognosis; Reproducibility of Results; Retrospective Studies}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Computers in Biology and Medicine}},
title = {{Clinical notes as prognostic markers of mortality associated with diabetes mellitus following critical care : A retrospective cohort analysis using machine learning and unstructured big data}},
url = {{http://dx.doi.org/10.1016/j.compbiomed.2021.104305}},
doi = {{10.1016/j.compbiomed.2021.104305}},
volume = {{132}},
year = {{2021}},
}