Deep learning-based prediction of in-hospital mortality for sepsis

Li Yong, Liu Zhenzhou

Scientific Reports 2024 article cited by 30

Source Open-access PDF More in Medicine & Health

importance 7/10 ai assisted Machine Learning in Healthcare critical care medicine stage: research

Problem

Existing clinical models for assessing mortality risk in sepsis patients rely on limited conventional indicators, leading to inaccurate risk assessment in the ICU.

Why it matters

Sepsis carries high mortality risk, and accurate assessment helps ICU physicians make optimal life-saving decisions.

What the AI did

The authors mined large volumes of electronic medical records with machine learning to refine core indicators and proposed DGFSD, a deep learning mortality risk assessment model for sepsis patients.

Results

not stated in source

Impact

Evidence supports a proposed model; external validation or deployment is not stated.

Record

Venue	Scientific Reports
Published	2024
Work type	article
Authors	Li Yong, Liu Zhenzhou
Citations	30 (OpenAlex, at import time)
References	36
Model or method	DGFSD (deep learning model)
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41598-023-49890-9
Open access	gold
Keywords	sepsis, mortality prediction, deep learning, electronic medical records, ICU

Importance rating

7 / 10

significant — thousands to millions affected

Abstract states sepsis is a serious blood infection with high mortality risk and many complications; accurate ICU risk assessment can save patients' lives.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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