{"title":"评估使用身体质量指数变化作为神经性厌食症恢复的代理:机器学习的角度。","authors":"Tianfei Yu, Haolan Zhang, Yunhan Zhang, Ming Li","doi":"10.1186/s40337-025-01416-6","DOIUrl":null,"url":null,"abstract":"<p><p>This paper critically examines the study by Brizzi et al., which applied explainable machine learning to predict short-term treatment outcomes in patients hospitalized for anorexia nervosa (AN). While the study presents an innovative and promising methodological framework, important conceptual and practical issues warrant further scrutiny. Chief among these is the reliance on body mass index (BMI) change as the sole proxy for treatment efficacy. This unidimensional metric, though pragmatic in acute inpatient settings, fails to capture the broader psychological and behavioral dimensions integral to AN recovery. The paper also interrogates the clinical applicability of machine learning tools, emphasizing both their potential to illuminate complex predictive patterns and the challenges they pose in terms of data sufficiency, interpretability, and real-world integration. Moreover, the identification of body uneasiness, interpersonal difficulties, and personal alienation as key predictive factors aligns with established theoretical models of AN, reinforcing the need for targeted psychotherapeutic interventions. However, further research is needed to explore how such predictors interact with specific treatment modalities and influence long-term outcomes. Overall, this paper underscores the value of integrating psychological variables into predictive modeling while cautioning against reductive interpretations of recovery in complex psychiatric disorders.</p>","PeriodicalId":48605,"journal":{"name":"Journal of Eating Disorders","volume":"13 1","pages":"212"},"PeriodicalIF":4.5000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476044/pdf/","citationCount":"0","resultStr":"{\"title\":\"Evaluating the use of body mass index change as a proxy for anorexia nervosa recovery: a machine learning perspective.\",\"authors\":\"Tianfei Yu, Haolan Zhang, Yunhan Zhang, Ming Li\",\"doi\":\"10.1186/s40337-025-01416-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper critically examines the study by Brizzi et al., which applied explainable machine learning to predict short-term treatment outcomes in patients hospitalized for anorexia nervosa (AN). While the study presents an innovative and promising methodological framework, important conceptual and practical issues warrant further scrutiny. Chief among these is the reliance on body mass index (BMI) change as the sole proxy for treatment efficacy. This unidimensional metric, though pragmatic in acute inpatient settings, fails to capture the broader psychological and behavioral dimensions integral to AN recovery. The paper also interrogates the clinical applicability of machine learning tools, emphasizing both their potential to illuminate complex predictive patterns and the challenges they pose in terms of data sufficiency, interpretability, and real-world integration. Moreover, the identification of body uneasiness, interpersonal difficulties, and personal alienation as key predictive factors aligns with established theoretical models of AN, reinforcing the need for targeted psychotherapeutic interventions. However, further research is needed to explore how such predictors interact with specific treatment modalities and influence long-term outcomes. Overall, this paper underscores the value of integrating psychological variables into predictive modeling while cautioning against reductive interpretations of recovery in complex psychiatric disorders.</p>\",\"PeriodicalId\":48605,\"journal\":{\"name\":\"Journal of Eating Disorders\",\"volume\":\"13 1\",\"pages\":\"212\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476044/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Eating Disorders\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s40337-025-01416-6\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"NUTRITION & DIETETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Eating Disorders","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s40337-025-01416-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
Evaluating the use of body mass index change as a proxy for anorexia nervosa recovery: a machine learning perspective.
This paper critically examines the study by Brizzi et al., which applied explainable machine learning to predict short-term treatment outcomes in patients hospitalized for anorexia nervosa (AN). While the study presents an innovative and promising methodological framework, important conceptual and practical issues warrant further scrutiny. Chief among these is the reliance on body mass index (BMI) change as the sole proxy for treatment efficacy. This unidimensional metric, though pragmatic in acute inpatient settings, fails to capture the broader psychological and behavioral dimensions integral to AN recovery. The paper also interrogates the clinical applicability of machine learning tools, emphasizing both their potential to illuminate complex predictive patterns and the challenges they pose in terms of data sufficiency, interpretability, and real-world integration. Moreover, the identification of body uneasiness, interpersonal difficulties, and personal alienation as key predictive factors aligns with established theoretical models of AN, reinforcing the need for targeted psychotherapeutic interventions. However, further research is needed to explore how such predictors interact with specific treatment modalities and influence long-term outcomes. Overall, this paper underscores the value of integrating psychological variables into predictive modeling while cautioning against reductive interpretations of recovery in complex psychiatric disorders.
期刊介绍:
Journal of Eating Disorders is the first open access, peer-reviewed journal publishing leading research in the science and clinical practice of eating disorders. It disseminates research that provides answers to the important issues and key challenges in the field of eating disorders and to facilitate translation of evidence into practice.
The journal publishes research on all aspects of eating disorders namely their epidemiology, nature, determinants, neurobiology, prevention, treatment and outcomes. The scope includes, but is not limited to anorexia nervosa, bulimia nervosa, binge eating disorder and other eating disorders. Related areas such as important co-morbidities, obesity, body image, appetite, food and eating are also included. Articles about research methodology and assessment are welcomed where they advance the field of eating disorders.