Prioritizing Hospital Admission According to Emergency Using Machine Learning

Abstract: The use of artificial intelligence and machine learning techniques in emergency medicine has grown rapidly. This paper reviews and assesses studies in this field, categorizing them into three areas: prediction and detection of disease, prediction of need for admission, discharge, and mortality, and machine learning-based triage systems. Overall, the studies reviewed demonstrate the potential of artificial intelligence in improving emergency care. However, the accuracy and effectiveness of these algorithms depend on data quality. Further research is needed to validate findings and improve performance in clinical settings.

Authors

Publication Details

Journal
International Journal of Latest Technology in Engineering Management & Applied Science
Published
2025-05-03
DOI
https://doi.org/10.51583/ijltemas.2025.140400024
Primary Topic
Emergency and Acute Care Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Prioritizing Hospital Admission According to Emergency Using Machine Learning

Prachi Sharma, Mayank Singh, Manvi Singh, R. K. Behl
International Journal of Latest Technology in Engineering Management & Applied Science
Emergency and Acute Care Studies
article

Prioritizing Hospital Admission According to Emergency Using Machine Learning

Prachi Sharma, Mayank Singh, Manvi Singh, R. K. Behl
article en

Abstract

Abstract: The use of artificial intelligence and machine learning techniques in emergency medicine has grown rapidly. This paper reviews and assesses studies in this field, categorizing them into three areas: prediction and detection of disease, prediction of need for admission, discharge, and mortality, and machine learning-based triage systems. Overall, the studies reviewed demonstrate the potential of artificial intelligence in improving emergency care. However, the accuracy and effectiveness of these algorithms depend on data quality. Further research is needed to validate findings and improve performance in clinical settings.

International Journal of Latest Technology in Engineering Management & Applied ScienceVol. 14(4)
Openalex Percentile: Top 84%
Emergency and Acute Care Studies
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