Improving post-operative discharge destination prediction of geriatric patients with generative data augmentation

TL;DR

This study demonstrates that generative data augmentation can improve predictive models for post-operative discharge in geriatric patients, especially simpler models, addressing data scarcity in healthcare.

Contribution

It introduces generative machine learning techniques, including adversarial random forests, to augment limited geriatric surgical data for better discharge prediction.

Findings

Random forest and TabPFN achieved high accuracy (~0.84) and AUC (~0.94) unaffected by augmentation.

Logistic regression's performance improved significantly with augmented data.

Generative data augmentation is effective for enhancing simple predictive models in geriatric care.

Abstract

Data scarcity challenges the development and implementation of innovative healthcare solutions. In geriatrics, fall-related injuries are a major cause of hospitalization, functional decline, and mortality in older adults. Optimizing post-operative discharge planning can mitigate these outcomes, but limited data hinders predictive model development. Here, we explored generative machine learning approaches to augment data from the SURGE-Ahead project (Supporting SURgery with Geriatric Co-Management and AI), an initiative addressing geriatric perioperative care. Data from the German geriatric trauma register (AltersTraumaZentrum; ATZ) were incorporated using two strategies: (i) combining SURGE-Ahead and ATZ register data with imputation (ComImp) and (ii) generating synthetic data from SURGE-Ahead alone or combined SURGE-Ahead and the ATZ register datasets with Adversarial random forests (ARF). Predictive models, including multinomial logistic regression, random forest, and a prior-fitted transformer (TabPFN), were trained and evaluated using standard performance metrics: accuracy, area under the receiver operating characteristic curve (ROC AUC), Brier score, and the logistic loss. Random forest and TabPFN performed well (accuracy around 0.84 and AUC around 0.94) and were largely unaffected by augmentation. Logistic regression benefited from augmented data, with predictive performance improving from 0.70 to 0.81 for accuracy and 0.85 to 0.92 for AUC. These results highlight generative data augmentation as a viable approach to enhance simpler predictive models in geriatric care and emphasize the importance of method selection when addressing data scarcity in heterogeneous clinical populations.