Machine Learning (ML) Algorithm to Predict Mortality of Trauma Patients Admitted to the Intensive Care Units (ICU) in Northwest Ethiopia

This study holds substantial value for clinical practice by enhancing decision support, enabling early identification of high-risk patients and supporting proactive surveillance and timely interventions. The integration of machine learning for mortality prediction is particularly impactful, as it facilitates remote monitoring and telemedicine, helping to bridge gaps in healthcare access. Additionally, it aids in optimising treatment strategies through patient-centred data, informs health planning and resource allocation, supports personalised care and advances data-driven research and policy-making.

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Publication Details

Journal
Nursing in Critical Care
Published
2025-10-09
DOI
https://doi.org/10.1111/nicc.70190
Primary Topic
Trauma and Emergency Care Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine Learning (ML) Algorithm to Predict Mortality of Trauma Patients Admitted to the Intensive Care Units (ICU) in Northwest Ethiopia

Daniel Shitu Getahun, Baye Tsegaye Amlak, Temesgen Ayenew, Haile Amha et al.
Nursing in Critical Care
Trauma and Emergency Care Studies
article

Machine Learning (ML) Algorithm to Predict Mortality of Trauma Patients Admitted to the Intensive Care Units (ICU) in Northwest Ethiopia

Daniel Shitu Getahun, Baye Tsegaye Amlak, Temesgen Ayenew, Haile Amha, Mengistu Abebe Messelu, Abraham Teym, Mihretie Gedfew, Tadesse Miretie Dessie, Bekele Getenet Tiruneh, Abraham Keffale Mengistu, Makda Fekadie Tewelgne, Tilahun Degu
article en

Abstract

This study holds substantial value for clinical practice by enhancing decision support, enabling early identification of high-risk patients and supporting proactive surveillance and timely interventions. The integration of machine learning for mortality prediction is particularly impactful, as it facilitates remote monitoring and telemedicine, helping to bridge gaps in healthcare access. Additionally, it aids in optimising treatment strategies through patient-centred data, informs health planning and resource allocation, supports personalised care and advances data-driven research and policy-making.

Nursing in Critical CareVol. 30(6)
Jigjiga University (ET), University of Gondar (ET), Debre Markos University (ET)
Openalex Percentile: Top 58%
Trauma and Emergency Care Studies
DOISourceOpenAlex
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Machine Learning (ML) Algorithm to Predict Mortality of Trauma Patients Admitted to the Intensive Care Units (ICU) in Northwest Ethiopia — Daniel Shitu Getahun, Baye Tsegaye Amlak, et al. · Nursing in Critical Care (2025) | TGRS Research Map | TGRS