ArticleBiomedical informatics insights2019
Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer.
Article in Biomedical informatics insights, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 7 citations in OpenAlex.
- Complications After Complex Gastrointestinal Cancer Surgery: Benefits and Costs Associated with Inter-hospital Transfer Among Medicare Beneficiaries.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2021Article
- Factors associated with Interhospital transfers of emergency general surgery patients from emergency departments.The American journal of emergency medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
objectiveTo demonstrate the usefulness of applying supervised machine-learning analyses to identify specific groups of patients that experience high levels of mortality post-interhospital transfer.
methodsThis was a cross-sectional analysis of data from the Health Care Utilization Project 2013 National Inpatient Sample, that applied supervised machine-learning approaches that included (1) classification and regression tree to identify mutually exclusive groups of patients and their associated characteristics of those experiencing the highest levels of mortality and (2) random forest to identify the relative importance of each characteristic's contribution to post-transfer mortality.
resultsA total of 21 independent groups of patients were identified, with 13 of those groups exhibiting at least double the national average rate of mortality post-transfer. Patient characteristics identified as influencing post-transfer mortality the most included: diagnosis of a circulatory disorder, comorbidity of coagulopathy, diagnosis of cancer, and age.
conclusionsEmploying supervised machine-learning analyses enabled the computational feasibility to assess all potential combinations of available patient characteristics to identify groups of patients experiencing the highest rates of mortality post-interhospital transfer, providing potentially useful data to support developing clinical decision support systems in future work.
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Registered trials
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