In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 7 ( 2022-7-15), p. e0271331-
Abstract:
Unplanned hospital readmissions mean a significant burden for health systems. Accurately estimating the patient’s readmission risk could help to optimise the discharge decision-making process by smartly ordering patients based on a severity score, thus helping to improve the usage of clinical resources. A great number of heterogeneous factors can influence the readmission risk, which makes it highly difficult to be estimated by a human agent. However, this score could be achieved with the help of AI models, acting as aiding tools for decision support systems. In this paper, we propose a machine learning classification and risk stratification approach to assess the readmission problem and provide a decision support system based on estimated patient risk scores.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0271331
DOI:
10.1371/journal.pone.0271331.g001
DOI:
10.1371/journal.pone.0271331.g002
DOI:
10.1371/journal.pone.0271331.g003
DOI:
10.1371/journal.pone.0271331.g004
DOI:
10.1371/journal.pone.0271331.g005
DOI:
10.1371/journal.pone.0271331.t001
DOI:
10.1371/journal.pone.0271331.t002
DOI:
10.1371/journal.pone.0271331.t003
DOI:
10.1371/journal.pone.0271331.t004
DOI:
10.1371/journal.pone.0271331.t005
DOI:
10.1371/journal.pone.0271331.t006
DOI:
10.1371/journal.pone.0271331.s001
DOI:
10.1371/journal.pone.0271331.s002
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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