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Review
. 2024 Jul 10;4(4):468-477.
doi: 10.1016/j.jointm.2024.04.006. eCollection 2024 Oct.

Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions

Jyotirmoy Gupta  1 , Amit Kumar Majumder  2 , Diganta Sengupta  3 , Mahamuda Sultana  4 , Suman Bhattacharya  4
Affiliations
Review

Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions

Jyotirmoy Gupta et al. J Intensive Med. .

Abstract

This study investigates the use of computational frameworks for sepsis. We consider two dimensions for investigation - early diagnosis of sepsis (EDS) and mortality prediction rate for sepsis patients (MPS). We concentrate on the clinical parameters on which sepsis diagnosis and prognosis are currently done, including customized treatment plans based on historical data of the patient. We identify the most notable literature that uses computational models to address EDS and MPS based on those clinical parameters. In addition to the review of the computational models built upon the clinical parameters, we also provide details regarding the popular publicly available data sources. We provide brief reviews for each model in terms of prior art and present an analysis of their results, as claimed by the respective authors. With respect to the use of machine learning models, we have provided avenues for model analysis in terms of model selection, model validation, model interpretation, and model comparison. We further present the challenges and limitations of the use of computational models, providing future research directions. This study intends to serve as a benchmark for first-hand impressions on the use of computational models for EDS and MPS of sepsis, along with the details regarding which model has been the most promising to date. We have provided details regarding all the ML models that have been used to date for EDS and MPS of sepsis.

Keywords: Artificial intelligence; Computing methodologies; Early prediction of sepsis; Machine learning; Mortality prediction of sepsis; Sepsis.

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Figures

Figure 1
Figure 1
Different dimensions for use of learning models on clinical data. AI: Artificial intelligence; CP: Clinical parameter; DALEX: Descriptive machine learning explanations; LIME: Local interpretable model-agnostic explanations; SHAP: Shapley additive explanations; XAI: Explainable artificial intelligence.
Figure 2
Figure 2
Possible avenues for tailor-made sepsis therapy.
Figure 3
Figure 3
Data share for popularly available data sources. GEO: Gene expression omnibus; ICU: Intensive care unit; ZG: Zigong.
Figure 4
Figure 4
Potential reasons for under-performance of predictive models.
See this image and copyright information in PMC

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