A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudinal socio-demographic data. In doing so we show that data such as housing status, and the details of the family environment, can provide cues for predicting future psychiatric disorders. To this end, we introduce a novel deep multi-task recurrent neural network to learn time-dependent depression cues. The depression prediction task is jointly optimized with two auxiliary anomaly ranking tasks, including contrastive one-class feature ranking and deviation ranking. The auxiliary tasks address two key challenges of the problem: 1) the high within class variance of depression samples: they enable the learning of representations that are robust to highly variant in-class distribution of the depression samples; and 2) the small labeled data volume: they significantly enhance the sample efficiency of the prediction model, which reduces the reliance on large depression-labeled datasets that are difficult to collect in practice. Extensive empirical results on large-scale child depression data show that our model is sample-efficient and can accurately predict depression 2-4 years before the illness occurs, substantially outperforming eight representative comparators.
Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
University of Augsburg（奥格斯堡大学）；Imperial College London（帝国理工学院）；GET.ON Institut für Online Gesundheitstrainings GmbH/HelloBetter（GET.ON在线健康培训研究所有限公司/HelloBetter）；Technical University Munich（慕尼黑工业大学）
Machine Learning for Depression Screening and Intervention: an Original Circadian Rhythm Score-based Methodology
Ocean University of China（中国海洋大学）；The Affiliated Hospital of Qingdao University（青岛大学附属医院）；Chengdu University of Traditional Chinese Medicine（成都中医药大学）；Southwest Jiaotong University（西南交通大学）
Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study
McGill University（麦吉尔大学）；Aifred Health（Aifred Health公司）；Queens College, CUNY（纽约市立大学皇后学院）；University of Michigan（密歇根大学）；University of Arizona（亚利桑那大学）；Google（谷歌公司）
Longitudinal modeling of MS patient trajectories improves predictions of disability progression
KU Leuven（鲁汶大学）；Hasselt University（哈瑟尔特大学）；Charles University in Prague（布拉格查理大学）；General University Hospital（综合大学医院）；University of Bari（巴里大学）；Hospital Universitario Virgen Macarena（马卡雷纳圣母大学医院）；Dokuz Eylul University（多库兹埃卢尔大学）；University G. d’Annunzio（邓南遮大学）；University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）
Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study
King's College London（伦敦国王学院）；University College London（伦敦大学学院）；South London and Maudsley National Health Services Foundation Trust（南伦敦和莫兹利国家医疗服务基金会信托）；Amsterdam University Medical Centre（阿姆斯特丹大学医学中心）；Vrije Universiteit（自由大学）；Parc Sanitari Sant Joan de Déu（圣琼德杜保健公园）；Universitat de Barcelona（巴塞罗那大学）；Katholieke Universiteit Leuven（鲁汶大学）；LAB University of Applied Sciences（LAB应用科学大学）；Northwestern University（西北大学）；Janssen Research and Development LLC（杨森研发有限责任公司）；H. Lundbeck A/S
A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
Singapore University of Technology and Design（新加坡科技设计大学）；Tianqiao and Chrissy Chen Institute（田桥和克里斯西·陈研究所）；Nanyang Technological University（南洋理工大学）；Purdue University（普渡大学）
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
Department of Computer Science and Engineering, Mepco Schlenk Engineering College（梅科斯伦克工程学院计算机科学与工程系）；Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College（梅科斯伦克工程学院电子与通信工程系）；School of Electronics Engineering (SENSE), VIT-AP University（VIT-AP大学电子工程学院（SENSE））；The Institute for Mental and Physical Health and Clinical Translation (IMPACT), School of Medicine, Deakin University（德金大学医学院心理健康与身体健康及临床转化研究所（IMPACT））；Biostatistics Unit, Faculty of Health, Deakin University（德金大学健康学院生物统计学单位）；School of Medicine, Deakin University（德金大学医学院）
Nationwide EHR-Based Chronic Rhinosinusitis Prediction Using Demographic-Stratified Models
Electrical and Computer Engineering（电气与计算机工程）；University of Houston（休斯顿大学）；Otolaryngology（耳鼻喉科）；Houston Methodist Hospital（休斯顿方法医院）；Information Science Technology（信息科学与技术）
