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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

De Silva, Kushan (författare)
Monash University, Australia
Mathews, Noel (författare)
Monash University, Australia
Teede, Helena (författare)
Monash University, Australia
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Forbes, Andrew (författare)
Monash University, Australia
Jönsson, Daniel (författare)
Malmö universitet,Odontologiska fakulteten (OD),Swedish Dental Service of Skane
Demmer, Ryan T. (författare)
University of Minnesota, USA; Columbia University, USA
Enticott, Joanne (författare)
Monash University, Australia
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 (creator_code:org_t)
Elsevier, 2021
2021
Engelska.
Ingår i: Computers in Biology and Medicine. - : Elsevier. - 0010-4825 .- 1879-0534. ; 132
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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 & rsquo; 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Omvårdnad (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Nursing (hsv//eng)

Nyckelord

Critical care
Diabetes
Electronic health records
LASSO
Machine learning
Mortality
Natural language processing
Prognosis
Text mining

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