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Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring

Tzu-Hao Wang^1,2, Chih-Chin Kao^3,4,5, Tzu-Hao Chang^2,6

¹Division of General Medicine, Department of Medical Education, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, Republic of China.

Journal of Multidisciplinary Healthcare

|April 17, 2024

Summary

Machine learning accurately predicts 90-day prognosis for patients with acute kidney injury requiring dialysis (AKI-D), identifying pre-dialysis creatinine as a key factor for improved patient outcomes and clinical decision-making.

Keywords:

AKI-D dialysis prognosis ensemble machine learning prediction models risk factors

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Area of Science:

Nephrology
Artificial Intelligence
Clinical Informatics

Background:

Acute kidney injury requiring dialysis (AKI-D) presents significant challenges in predicting patient prognosis.
Real-world clinical data offers a rich source for developing predictive models for AKI-D outcomes.
Ensemble machine learning algorithms show promise in analyzing complex clinical datasets.

Purpose of the Study:

To employ ensemble machine learning algorithms to predict 90-day prognosis in hospitalized patients with AKI-D.
To identify significant factors influencing dialysis dependence and mortality post-initial dialysis.
To develop a validated prediction model for enhanced clinical decision-making in AKI-D patient care.

Main Methods:

Utilized real-world clinical data from the Taipei Medical University Clinical Research Database (TMUCRD) (2008-2020).
Developed ensemble machine learning models, including feedforward neural networks and gradient-boosted decision trees, on the Google Cloud Platform.
Analyzed data from 1080 patients for dialysis dependence and 2358 patients for survival outcomes.

Main Results:

Ensemble models achieved high performance: AUROC of 0.846 for dialysis dependence and 0.865 for survival.
Baseline creatinine value, assessed at least 90 days before initial dialysis, was identified as the most crucial predictive factor.
The models demonstrated superior predictive capabilities compared to traditional logistic regression.

Conclusions:

Ensemble machine learning models effectively predict 90-day prognosis for AKI-D patients.
Pre-dialysis creatinine levels are significant indicators of overall patient prognosis.
The validated prediction model can aid healthcare providers in improving clinical decision-making and patient care for high-risk AKI-D populations.