Brizzi Giulia, Pupillo Chiara, Sajno Elena, Boltri Margherita, Brusa Federico, Scarpina Federica, Mendolicchio Leonardo, Riva Giuseppe
Department of Psychology, Università Cattolica del Sacro Cuore, Largo Gemelli, 20121, Milan, Italy.
Humane Technology Laboratory, Università Cattolica del Sacro Cuore, Largo Gemelli, 20121, Milan, Italy.
J Eat Disord. 2025 Jun 2;13(1):97. doi: 10.1186/s40337-025-01265-3.
Anorexia nervosa (AN) is a psychopathology with an alarmingly high mortality rate. The growing number of individuals seeking help, coupled with the limited resources of clinics, highlights the critical need to identify factors that can predict treatment efficacy. Machine learning (ML) techniques hold great promise in this regard. This data-driven approach offers an unbiased means to uncover predictors of specific outcomes, advancing the understanding and management of this challenging condition.
Six supervised ML algorithms (e.g., Decision Tree and Random Forest) were applied to develop a binary classification model predicting short-term weight recovery/stabilization in AN inpatients and identify the most critical factors influencing this outcome.
Change in Body Mass Index (BMI) from admission to discharge (ΔBMI) was used as the outcome, allowing to classify patients into "improved" (BMI stability or increase) and "aggravation" (BMI decrease). Predictors included clinically relevant psychological tests and physical parameters. Scikit-learn features importance, and SHAP (SHapley Additive exPlanations) analyses were used to investigate predictor importance.
The Random Forest model achieved an accuracy of 0.77, an AUC-ROC of 0.72, and a PR curve score of 0.88. Body Uneasiness, Personal Alienation, and Interpersonal Problems subscales emerged as best predictors. SHAP analysis confirmed these results at the individual prediction level.
Results encouraged interventions focused on body-self experience in addition to interpersonal relationships, including body-swapping experiences and metaverse activities, respectively. This could maximize treatment efficacy, effectively allocating limited resources to achieve clinically relevant outcomes.
神经性厌食症（AN）是一种死亡率高得惊人的精神病理学疾病。寻求帮助的人数不断增加，再加上诊所资源有限，凸显了识别可预测治疗效果的因素的迫切需求。机器学习（ML）技术在这方面具有巨大潜力。这种数据驱动的方法提供了一种无偏见的手段来揭示特定结果的预测因素，促进对这种具有挑战性疾病的理解和管理。
应用六种监督式机器学习算法（例如决策树和随机森林）来开发一个二元分类模型，预测神经性厌食症住院患者的短期体重恢复/稳定情况，并识别影响这一结果的最关键因素。
将入院到出院时体重指数（BMI）的变化（ΔBMI）用作结果指标，据此将患者分为“改善”（BMI稳定或增加）和“恶化”（BMI下降）两类。预测因素包括临床相关的心理测试和身体参数。使用Scikit-learn的特征重要性以及SHAP（Shapley值相加解释）分析来研究预测因素的重要性。
随机森林模型的准确率为0.77，曲线下面积（AUC-ROC）为0.72，精确率-召回率曲线（PR曲线）分数为0.88。身体不适感、个人疏离感和人际关系问题分量表成为最佳预测因素。SHAP分析在个体预测层面证实了这些结果。
研究结果鼓励除人际关系干预外，还应着重于身体自我体验的干预措施，分别包括身体互换体验和元宇宙活动。这可以最大限度地提高治疗效果，有效地分配有限资源以实现临床相关结果。
