arXiv Machine Learning

A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention

arXiv:2607. 21403v1 Announce Type: new Abstract: Mobile-health interventions increasingly use online learning and decision making algorithms to personalize when to nudge users toward healthier behavior, but a poorly designed algorithm can burden and disengage participants.

arXiv AI

Jul 13

EHR-MPC: Inference-Time Control for Sepsis Treatment with Generative Patient Digital Twins

arXiv:2607. 08793v1 Announce Type: cross Abstract: Sepsis is a leading cause of mortality, yet optimal treatment policies remain contested.

By Joshua Pickard, Wei Qi, Na Li, Ann Woolley, Lisa Cosimi, Roy Kishony, Deborah Hung

reinforcement-learning

arXiv AI

Jun 15

A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health

arXiv:2606. 14604v1 Announce Type: cross Abstract: Wearable devices and smartphones generate rich behavioural time series that can support proactive health interventions, yet systematic comparisons of modern forecasting architectures for these data are lacking.

By Pavlos Nicolaou, Kleanthis Malialis, Artemis Kontou, Panayiotis Kolios

llms fine-tuning benchmarks

arXiv AI

Jun 17

Treatment Response Optimized Clinical Decision Support AI System via Digital Twin Simulation

arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.

By Xinyu Qin, Anil K. Sood, Ruiheng Yu, Sara Corvigno, Elaine Stur, Lu Wang

reinforcement-learning safety

arXiv AI

Aug 12

ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

arXiv:2608. 10915v1 Announce Type: new Abstract: After an older adult misses a medication dose, a software agent can send another reminder and an embodied agent can bring the medication.

By Qianggang Ding, Xingyao Wang, Rui Feng, Zhibin Wang, Feixiang Wang, Kelong Mao, Hao Sun, Zhiyao Luo, Jiankai Tang, Lei Li, Jiadong Guo, Minheng Ni, Weicong Lin, Chenxi Yang, Hongxiang Gao, Zhenghua Chen, Yang Bai, Min Wu, Jun Cheng, Huazhu Fu, Dacheng Tao, Bang Liu

agents robotics multimodal benchmarks safety

arXiv AI

Jun 16

Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies

arXiv:2606. 16721v1 Announce Type: new Abstract: Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes.

By Ke Liu, Mengxuan Li, Yanyi Bao, Tianyun Zhang, Chong Chu, Jiajun Bu, Haishuai Wang

reinforcement-learning

arXiv AI

Jul 16

A Self-Evolving Agent for Longitudinal Personal Health Management

arXiv:2607. 13940v1 Announce Type: new Abstract: Personal health management unfolds over repeated encounters, yet most health AI systems treat each request in isolation.

By Haoran Li, Jiebi Deng, Tong Jin, Jinghong Han, Yuxin Wang, Zexin Wang, Qingyi Si, Weikang Gong, Xiahai Zhuang, Jia You, Wei Cheng, Jianfeng Feng, Hongcheng Guo

llms agents benchmarks safety

arXiv Machine Learning

Jul 14

Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions

arXiv:2601. 15353v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has achieved remarkable success in real-world decision-making across diverse domains, including gaming, robotics, online advertising, public health, and natural language processing.

By Asim H. Gazi, Yongyi Guo, Daiqi Gao, Ziping Xu, Kelly W. Zhang, Susan A. Murphy

agents nlp reinforcement-learning robotics

Hugging Face Trending Papers

Jun 8

Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data

Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring. Existing machine learning approaches have improved AD prediction using multimodal data, yet often focus on static classification or cohort-level risk estimation, providing limited support for subject-specific modelling and uncertainty-aware reasoning.

multimodal

arXiv AI

Jun 3

Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection

arXiv:2606. 02812v1 Announce Type: new Abstract: Modeling patient trajectories from longitudinal electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences.

By Sihang Zeng, Matthew Thompson, Ruth Etzioni, Meliha Yetisgen

llms agents reinforcement-learning fine-tuning multimodal

arXiv AI

Jun 9

Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data

arXiv:2606. 09671v1 Announce Type: cross Abstract: Alzheimer's disease (AD) progression is highly heterogeneous and is typically observed through sparse and irregular longitudinal data, posing challenges for prediction and personalised monitoring.

By Yinyu Huang, Yilin Zhang, Sofia Michopoulou, Christopher Kipps, Rahman Attar

multimodal

arXiv Machine Learning

Jun 24

Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions

arXiv:2605. 19208v2 Announce Type: replace-cross Abstract: Physical activity (PA) plays an important role in maintaining and improving health.

By Gefei Lin, Rui Miao, Jennifer Sacheck, Xiaoke Zhang

reinforcement-learning

arXiv Machine Learning

Aug 4

Inferring Relative Consequences of Mechanical Ventilation from Observational Data Using Game-Based Comparisons

arXiv:2510. 15127v3 Announce Type: replace-cross Abstract: Identifying the effects of mechanical ventilation (MV) protocols in critical care requires analyzing data from heterogeneous patient-ventilator systems in the clinical decision-making environment.

By David J. Albers, Tell D. Bennett, Jana de Wiljes, George Hripcsak, Bradford J. Smith, Peter D. Sottile, J. N. Stroh

reinforcement-learning

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