ML-Driven Predictive Analytics for Healthcare Management to Optimize Costs and Outcomes in Diverticulitis Treatment

Mohammed Quazi; Jennifer Mewshaw; Heather Carter‐Templeton; Amir Humza Sohail

doi:10.1609/aaaiss.v6i1.36031

August 1, 2025Proceedings of the AAAI Symposium Series

ML-Driven Predictive Analytics for Healthcare Management to Optimize Costs and Outcomes in Diverticulitis Treatment

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Key Points

Identifying key risk factors related to mortality improves healthcare management for diverticulitis patients.
The mortality rate observed among diverticulitis patients was 0.66%, highlighting the seriousness of complex cases.
Machine learning models like XGBoost were utilized to predict outcomes and optimize costs for healthcare operations.
Improving predictions of patient outcomes can lead to better allocation of resources in hospitals treating diverticulitis.

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Implication

Observational analysis evaluates mortality risk factors in diverticulitis patients, suggesting enhanced healthcare management through predictive analytics.

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Quazi et al. (2025) studied this question.

synapsesocial.com/papers/68c1a90554b1d3bfb60e1e39 https://doi.org/10.1609/aaaiss.v6i1.36031

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ML-Driven Predictive Analytics for Healthcare Management to Optimize Costs and Outcomes in Diverticulitis Treatment

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

ML-Driven Predictive Analytics for Healthcare Management to Optimize Costs and Outcomes in Diverticulitis Treatment

Key Points

No abstract yet

Ask about this paper

Implication

Discussion

Cite This Study

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