48 citations · 131 across the 13 of their papers we have counts for
15 papers
ChronoMedicalWorld: A Medical World Model for Learning Patient Trajectories from Longitudinal Care Data
Jiangyuan Wang, Xuyong Chen, Junwei He +3
Long-horizon clinical simulation -- predicting how a patient's physiology evolves over years under specified interventions -- is central to chronic-disease care, yet existing elect…
A Framework for Longitudinal Health AI Agents
Georgianna Lin, Rencong Jiang, Noémie Elhadad +1
Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management, behavior change, and patient su…
Towards a Science of Scaling Agent Systems
Yubin Kim, Ken Gu, Chanwoo Park +17
Agents, language model-based systems capable of reasoning, planning, and acting are widely adopted in real-world tasks, yet how their performance changes as these systems scale acr…
SensorLM: Learning the Language of Wearable Sensors
Yuwei Zhang, Kumar Ayush, Siyuan Qiao +17
We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language. Despite its pervasive nature, aligning and…
LSM-2: Learning from Incomplete Wearable Sensor Data
Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush +22
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffer…
RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu, Zhihan Zhang, Kate Lin +18
Language models (LMs) are increasingly being deployed to perform autonomous data analyses. However, their data awareness -- the ability to recognize, reason over, and appropriately…
Substance over Style: Evaluating Proactive Conversational Coaching Agents
Vidya Srinivas, Xuhai Xu, Xin Liu +5
While NLP research has made strides in conversational tasks, many approaches focus on single-turn responses with well-defined objectives or evaluation criteria. In contrast, coachi…
Medical Hallucinations in Foundation Models and Their Impact on Healthcare
Yubin Kim, Hyewon Jeong, Shan Chen +24
Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accuracy, fostering overconfidence an…
A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
Yubin Kim, Chanwoo Park, Hyewon Jeong +7
Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (…
Scaling Wearable Foundation Models
Girish Narayanswamy, Xin Liu, Kumar Ayush +15
Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large vo…
Can AI Relate: Testing Large Language Model Response for Mental Health Support
Saadia Gabriel, Isha Puri, Xuhai Xu +2
Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. A proposed deployment use case is psychothera…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making
Yubin Kim, Chanwoo Park, Hyewon Jeong +7
Foundation models are becoming valuable tools in medicine. Yet despite their promise, the best way to leverage Large Language Models (LLMs) in complex medical tasks remains an open…
Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data
Yubin Kim, Xuhai Xu, Daniel McDuff +2
Large language models (LLMs) are capable of many natural language tasks, yet they are far from perfect. In health applications, grounding and interpreting domain-specific and non-l…
From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models
Zachary Englhardt, Chengqian Ma, Margaret E. Morris +7
Passively collected behavioral health data from ubiquitous sensors holds significant promise to provide mental health professionals insights from patient's daily lives; however, de…