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Supervised Machine Learning-Based Prediction of In-Hospital Mortality Following Hip Fracture in Older Adults.

Eduardo Guzmán-Muñoz^1,2, Manuel Vásquez-Muñoz^3,4, Yeny Concha-Cisternas⁵

¹Escuela de Kinesiología, Facultad de Salud, Universidad Santo Tomás, Talca 3460000, Chile.

Diagnostics (Basel, Switzerland)

|February 27, 2026

Summary

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Actuarial Approach

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Machine learning models accurately predict in-hospital mortality for older adults with hip fractures using administrative data. These models can aid early risk stratification and improve clinical decisions in healthcare.

Area of Science:

Geriatric Medicine
Health Informatics
Data Science

Background:

Hip fractures in older adults lead to significant morbidity, functional decline, and mortality.
Early identification of high-risk patients is crucial for clinical decision-making and resource allocation.

Purpose of the Study:

Develop and validate supervised machine learning models to predict in-hospital mortality in older adults with hip fractures.
Utilize nationwide administrative data from Chile for model development and validation.

Main Methods:

Retrospective cohort study using hospital discharge records (2019-2024) from 72 public hospitals.
Trained and evaluated multiple supervised machine learning algorithms using stratified train-test partitioning.
Assessed model performance with AUC-ROC, precision, recall, F1-score, and explored interpretability with SHAP.

Keywords:

Gradient Boosting SHAP analysis explainable AI hip fracture in-hospital mortality machine learning older adults predictive modeling

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Main Results:

Analyzed 40,253 hospitalization episodes.
Gradient Boosting model achieved the highest performance (AUC-ROC: 0.885).
SHAP analysis identified age, comorbidity, and surgical treatment as key predictors of mortality risk.

Conclusions:

Supervised machine learning models effectively predict in-hospital mortality after hip fracture using administrative data.
Interpretable models can support early risk stratification and clinical decision-making at a national level.