Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets

Javier Enrique Camacho-Cogollo, Isis Bonet, Bladimir Gil, Ernesto Iadanza

Electronics 2022 article cited by 34

Source Open-access PDF More in Medicine & Health

importance 8/10 ai assisted Sepsis Diagnosis and Treatment critical care stage: research

Problem

Sepsis is hard to identify early, and late diagnosis increases mortality, so better early screening tools are needed.

Why it matters

Early sepsis detection is critical to reducing patient mortality.

What the AI did

Machine learning models were trained on MIMIC-III vital signs, laboratory results, and demographics to predict sepsis early.

Results

ML models showed overall higher performance than the commonly used Sequential Organ Failure Assessment (SOFA); specific metrics not stated in source.

Impact

The evidence supports ML outperforming SOFA on a public retrospective dataset, not clinical deployment.

Record

Venue	Electronics
Published	2022
Work type	article
Authors	Javier Enrique Camacho-Cogollo, Isis Bonet, Bladimir Gil, Ernesto Iadanza
Citations	34 (OpenAlex, at import time)
References	87
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3390/electronics11091507
Open access	gold
Keywords	sepsis, early prediction, machine learning, MIMIC-III, SOFA

Importance rating

8 / 10

major — millions of lives, an industry or a field

Abstract states sepsis is a highly lethal syndrome and early diagnosis is critical to reduce mortality, while existing screening systems have limited efficacy at individual level.

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

More in Medicine & Health

#	Paper	Year	Citations	Importance
1	Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction Yeong Chan Lee, Jiho Cha, Injeong Shim, Woong‐Yang Park, Se Woong Kang...	2023	107	10
2	Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization Tawsifur Rahman, Amith Abdullah Khandakar, Muhammad Abdul Kadir, Khand...	2020	651	10
3	A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach Nitish Biswas, Khandaker Mohammad Mohi Uddin, Sarreha Tasmin Rikta, Sa...	2022	116	10
4	Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Mu...	2022	89	10
5	Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential Kruthi Suvarna, Deeptarup Biswas, Medha Gayathri J. Pai, Arup Acharjee...	2021	76	10