AI for Health Research

Updates

• [Event] AI for Health Symposium (Video), October 29, 2026

• [Talk] AI for Health: Translating Wearable Data into Clinical Impact (Slides), Keynote, MobiDx, October 30, 2026

• [Talk] AI for Mental Health Care: Early Detection, Personalized Treatment, and Trustworthy Chatbots, WashU Psychiatry Grand Rounds, October 20, 2026

• [News] PhD student Ziqi Xu selected for Anthropic AI for Science program, October 20, 2026

• [Course] ACS Clinical Congress: SC04 AI Skills: What Can I Do Now and What’s Coming in the Future?, September 27, 2026

• [Event] AIHealth Venture Day, September 23, 2026

• [Talk] Foundation Models Across Health Data Modalities, WashU AI Tools and Foundations Symposium, September 22, 2026

• [Talk] AI Across Medicine: Opportunities for Oncology (slides), WashU Oncology Grand Rounds, September 11, 2026

• [Talk] OTM Office Hours: From Research to Reality: Founders and Faculty Shaping the Future of Health IT (video), September 3, 2026

• [Course] CSE 4109: Introduction to AI for Health [Announcement]

• [Website] SurgicalPLAN: Predicting Postoperative Complications Using Clinical Notes

• [Website] Smart Kitchen: AI-Enabled Cognitive Support for Everyday Tasks

• [Talk] Predicting Postoperative Complications from Clinical Notes, SRS 2026

• [News] Newsletter of the AI for Health Institute (Summer 2026)

• [News] WashU AI for Health Institute Emphasizes Multidisciplinary Approach [Healthcare Innovation]

• [News] AI for Health Seed Grant Program Awards Nearly $300,000 to Advance AI-Driven Health Research [Press Release]

• [News] Clinical AI That Is More Honest About What It Doesn't Know [Press Release]

• [Paper] CURA: Clinical Uncertainty Risk Alignment for Language Model–Based Risk Prediction [ACL 2026]

• [News] Engineering Public Health (featured in the cover story of Momentum)

• [News] PhD student Ruiqi Wang featured in the WashU Class Acts. Congratulations, Dr. Wang!

• [News] Clinically Informed AI Outperforms Foundation Models in Spinal Cord Disease Prediction [Press Release]

• [Paper] Clinically-Guided Models or Foundation Models? Predicting Cervical Spondylotic Myelopathy From Electronic Health Records (Editorial) [npj Digital Medicine]

• [News] Surgical AI Adapts to Changing Patients [Press Release]

• [Paper] Addressing Cohort Variability with Adaptive Fusion of Wearable and Clinical Data: A Case Study in Predicting Pancreatic Surgery Outcomes [HEALTH]

• [News] Newsletter of the AI for Health Institute (Fall 2025)

• [News] AI 'CHEF' Could Help Those With Cognitive Declines Complete Home Tasks [Press Release]

• [Paper] CHEF-VL: Detecting Cognitive Sequencing Errors in Cooking with Vision-language Models [IMWUT]

• [Talk] AI and Poverty: Harnessing Data to Understand Health Risks and Inequities, Keynote at NYU AI and Poverty Mini-Conference

• [News] PhD student Claire Najjuuko received the Provost's Research Excellence Award

• [News] PhD student Ruiqi Wang received Google PhD Fellowship

• [Event] Panel: AI's Impact on Healthcare (WashU Law Legal Tech Week)

• [Event] AI for Health Symposium (Recording) on October 22, 2025

• [News] Chenyang Lu joined the inaugural AI Advisory Board of WashU Law

• [Course] CSE 4109: Introduction to AI for Health [AIHealth Announcement]

• [News] Data Science Uncovers Patterns in Health Service Use Linked to Child Mortality [Press Release]

• [Paper] Patterns of Maternal and Child Health Services Utilization and Associated Socioeconomic Disparities in Sub-Saharan Africa. [Nature Communications]

• [Paper] Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection [ACL Findings]

• [News] Sleep Data from Wearable Device May Help Predict Preterm Birth [Press Release]

• [Paper] Validation of Sleep-based Actigraphy Machine Learning Models for Prediction of Preterm Birth [npj Women's Health]

• [News] Predicting Pain with Machine Learning [Press Release]

• [Paper] Incorporating Uncertainty in Predictive Models Using Mobile Sensing and Clinical Data: A Case Study on Persistent Post-surgical Pain [UbiComp'25]

• [News] Inaugural AI for Health Seed Funding Program Awards Nearly $300,000

• [Talk] Phenotype Chiari Malformation with Artificial Intelligence (Slides), Annual Scientific Update of the Redefining Chiari Malformation project

• [News] Newsletter of the AI for Health Institute (Spring 2025)

• [News] Foundation AI Model Predicts Postoperative Risks from Clinical Notes [Press Release]

• [Paper] The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes [npj Digital Medicine]

• [News] Machine Learning Could Help Predict Adherence to HIV Treatment in Adolescents [Press Release]

• [Paper] Using Machine Learning to Predict Poor Adherence to Antiretroviral Therapy Among Adolescents Living with HIV in Low Resource Settings [AIDS]

• [Podcast] Bridging AI and Healthcare: An In-depth Conversation with Dr. Chenyang Lu (Apple Podcasts, Spotify) [Gateway to Informatics]

• [Radio] WashU Institute Studying the Use of AI in Healthcare Fields [KMOX Radio]

• [Video] Fireside Chat about AI-driven Phenotyping of Chiari Malformation [Park-Reeves Chiari & Syringomyelia Center]

• [Podcast] Engineering AI for Health with Chenyang Lu (YouTube) (Spotify) [Engineering the Future]

• [Talk] Advancing Children and Youth Health with Artificial Intelligence (Flyer), Children and Youth Collaborative Network

• [News] $5 million NIH Grant to Find Causes of Chronic Pain After Surgery [Press Release]

• [News] Researchers Define New Subtypes of Common Brain Disorder [Press Release]

• [Paper] Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia [Neurosurgery]

• [Paper] Performance of Risk Models for Antimicrobial Resistance in Adult Patients With Sepsis [JAMA Netw Open]

• [News] AI for Health Seed Funding Program announced!

• [News] AI for Health Symposium [10/18/2024]

• [News] Newsletter of the AI for Health Institute (Fall 2024)

• [Talk] AI for Health with Wearables (Slides) (Video), UIUC

• [News] What Your Fitness Tracker Says About Your Mental Health [IEEE Transmitter]

• [News] Artificial Intelligence in Medicine (WashU video featuring our AI for Health research and students)

• [News] Machine Learning Tool Accurately Predicts Spine Surgery Outcomes [Health IT Analytics]

• [News] New Machine Learning Method Can Better Predict Spine Surgery Outcomes [Press Release]

• [News] How AI Can Boost Cancer, Depression and Perioperative Care [Healthcare IT News]

• [Talk] AI for Health with Wearables (Slides), CPS-IoT Week Keynote

• [Event] NEW YORK: AI in Healthcare [WashU Healthcare Network]

• [Talk] AI for Health with Wearables (Slides) (Video), University of Cambridge

• [News] UVA Computer Science Alum Chenyang Lu Named Director of Washington University's AI for Health Institute [UVA]

• [News] Inaugural Edition of the Newsletter of the AI for Health Institute (AIHealth)

• [News] Bluetooth System an Effective Tool for Health Care Worker COVID-19 Contact Tracing [Healio]

• [News] AI for Health research featured in the WashU video on AI and Medicine and the cover article in the Momentum magazine.

• [News] Wearable Tech for Contact Tracing Developed [Press Release]

• [News] AI for Health Institute Launches to Promote Growing Intersection of Artificial Intelligence, Health [Press Release]

• [News] AI for Health Institute launched at the AI and Digital Health Summit on October 18-19. [More information]

• [News] Google Fitbits May Be Able to Predict Complications in Pancreatic Cancer Surgeries [The Messenger]

• [News] Interdisciplinary Team Wins Award for Paper on Predicting Postoperative Complications with Wearables, Artificial Intelligence [Distinguished Paper Award]

• [News] AI for Health Institute is now the AI core of the Institute of Clinical and Translational Sciences (ICTS).

• [Course] CSE 521S Artificial Intelligence and Internet of Things for Medicine

• [News] Artificial Intelligence May Assist Decisions on Which Patients Should Get Critical Life Support [Press Release]

• [Talk] Internet of Medical Things: Precision Medicine with AI and Wearables, CHASE'23 Keynote

• [News] Data from Wearables Could Be a Boon to Mental Health Diagnosis [The Source]

• [News] Wireless Clinical Monitoring Paper Received SenSys Test of Time Award [Test of Time Award]

• [Talk] Reliable Clinical Monitoring with Wireless Sensor Networks, SenSys'22 Test of Time Award Speech
  

• [Talk] Artificial Intelligence and Internet of Medical Things for Healthcare (Slides) (Video), Medicine Grand Rounds
  

• [News] Personalized Prediction of Depression Treatment Outcomes with Wearables [Press Release]
  

• [Talk] Internet of Medical Things: Predicting Clinical Outcomes with Wearables (Slides) (Video), Brown School Open Classroom

• [News] Machine-Learning Tools Predict Post-Op Complications, Surgery Duration [Health IT Analytics]
  

• [News] AI Model Explores EHR Data to Predict Physician Burnout [Health Exec]
  

• [News] Deep-Learning Model Predicts Physician Burnout Using EHR Logs [Health IT Analytics]
  

• [News] Lu Studies Potential Benefits of AI in Health Care [The Source]
  

• [News] Learning Physician Burnout from Electronic Health Record Activities [Press Release]
  

• [News] Predicting Surgical Outcomes with Machine Learning [Press Release]
  

• [Talk] Internet of Medical Things: Predicting Clinical Outcomes with Wearables (Slides) (Video), MobiSys'22 IoT Day Keynote
  

• [Paper] Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery [UbiComp'22]
  

• [News] Model Predicts Deterioration of Hospitalized Patients with Cancer (Healio)
  

• [News] That new Fitbit does more than count steps. It may save your life one day. (Diagnostic and Interventional Cardiology)
  

• [News] Research using Fitbit and machine learning to predict surgical outcomes featured in the Next Byte podcast
  

• [News] Washington People: Chenyang Lu (profile of our work on healthcare using wearables and machine learning)
  

• [News] Wearable Fitness Trackers Help Physicians Track Patient Health
  

• [News] Early Warning System Model Predicts Deterioration of Hospitalized Cancer Patients Based on Clinical Data
  

Team

Faculty: Chenyang Lu

Current PhD Students

Peiqi Gao, Hangyue Li, Patrick Lynch, Claire Najjuuko, Sizhe Wang, Zichen Wang, Ben Warner, Jiezhong Xiao, Ziqi Xu, Daoyi Zhu

Current Grants

• Redefining Chiari Type I Malformation and Its Impact on Brain Development (MPI: Limbrick/Lu), NIH P01
• IMPETUS: Integrated Mechanisms, Phenotypes, and Translational Underpinnings of Chronic Pain after Surgery (MPI: Creed/Haroutounian/Lu/Rodebaugh/Shepherd/Sinha), NIH RM1
• UNITED: Understanding Neurocognitive Outcomes in Type 1 Diabetes: Modifiable Risk and Protective Factors in Diverse Youth (MPI: Arbelaez/Hershey), NIH U01
• Graph Neural Networks and Longitudinal Mediation Analysis for Hospital-Onset Sepsis to Improve Early Detection and Uncover Modifiable Risk Factors (PI: Vazquez Guillamet), NIH R01
• Prenatal Cannabis Exposure (PreCE) and Immune-Endocannabinoid Pathways to Development of Offspring Brain and Behavior up to Age 6 (MPI: Rogers/Agrawal/Bogdan), NIH R01
• Clinical Implementation of Molecular Phenotypes of Critical Illness (PI: Sinha), NIH R01
• Longitudinal Biomechanics and Patient-Reported Outcomes after Periacetabular Osteotomy for Developmental Dysplasia of the Hip (PI: Harris), NIH R01
• Massively Parallel Functional Interrogation of Genetic Variation in CMD-Associated Alpha-Dystroglycan Glycosylating Enzymes (PI: Haller), NIH R01
• Project on EHR-Integrated Lifestyle Interventions for Adults Aged Fifty and Older (PIVOT) (MPI: Kannampallil/Ma/Venditti), NIH R01
• EnhanCed HandOff (PI: Abraham), AHRQ R01
• Integrating Real-Time Clinical Activity and Behavioral Responses for Characterizing Cognitive Load and Errors (IGNITE) (PI: Kannampallil), AHRQ R01
• Addressing the Research Capacity Gap in Global Child and Adolescent Health Disparities Utilizing Implementation and Data Sciences among Vulnerable Populations in Resource-Limited Settings (ACHIEVE) (MPI: Ssewamala/McKay), NIH D43
• Using Biomedical Informatics and Artificial Intelligence to Improve Detection of Degenerative Cervical Myelopathy (PI: Greenberg), DoD
• A Machine Learning-Based Approach to Identify Dysplasia in Blood and Bone Marrow Specimens (PI: Duncavage), American Cancer Society
• Linking Teens to Mental Health Care Through Digital Innovations (PI: Cavazos-Rehg), Klingenstein Third Generation Foundation

Talks

• AI for Health: Translating Wearable Data into Clinical Impact (Slides), Keynote, MobiDx, 1st Workshop on Hot Topics in Mobile and Wireless Systems for Health (MobiDx), October 30, 2026.

• AI for Mental Health Care: Early Detection, Personalized Treatment, and Trustworthy Chatbots, Psychiatry Grand Rounds, Washington University School of Medicine, October 20, 2026.

• ACS Clinical Congress: SC04 AI Skills: What Can I Do Now and What’s Coming in the Future?, September 27, 2026.
  Section taught: SurgicalPLAN: Predicting Postoperative Complications Using Clinical Notes.

• AI Across Medicine: Opportunities for Oncology (Slides), Oncology Grand Rounds, Washington University School of Medicine, September 11, 2026.

• Foundation Models Across Health Data Modalities, WashU AI Tools and Foundations Symposium, September 22, 2026.

• From Research to Reality: Founders and Faculty Shaping the Future of Health IT (Video), OTM Office Hours, September 3, 2026.

• Predicting Postoperative Complications from Clinical Notes, Society of Robotic Surgery (SRS) Annual Meeting, July 2026.

• AI for Surgery: From Multimodal Data to Clinical Decisions, Department of Neurosurgery, Virginia Commonwealth University, April 2026.

• AI for Health: Translating Data into Impact, Schmidt Distinguished Talk, William & Mary, April 2026.

• AI and Poverty: Harnessing Data to Understand Health Risks and Inequities, Keynote, Mini-Conference on AI and Poverty, New York University, November 2025.

• AI Across Medicine: Opportunities for Radiation Oncology, Grand Rounds, Department of Radiation Oncology, Washington University School of Medicine, October 2025.

• Phenotype Chiari Malformation with Artificial Intelligence (Slides), Annual Scientific Update, Redefining Chiari Type I Malformation and Its Impact on Brain Development, February 2025

• AI for Health with Wearables (Slides), Keynote, Cyber-Physical Systems and Internet-of-Things Week (CPS-IoT Week), May 2024.

• AI for Health with Wearables (Slides) (Video), Mobile and Wearable Health Seminar Series, University of Cambridge, February 2024.

• Internet of Medical Things: Precision Medicine with AI and Wearables, Keynote, IEEE International Conference on Embedded and Real-Time Computing Systems and Applications(RTCSA’23), September 2023.

• Internet of Medical Things: Precision Medicine with AI and Wearables, Keynote, IEEE/ACM international conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE'23), June 2023.

• Detecting Mental Disorders with Wearables: A Large Cohort Study, ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI'23), May 2023.

• AI for Neurosurgery, Henry G. Schwartz Grand Rounds, Department of Neurosurgery, Washington University School of Medicine, May 2023.

• Artificial Intelligence and Internet of Medical Things for Healthcare (Slides) (Video), Grand Rounds, Department of Medicine, Washington University School of Medicine, September 2022.

• Reliable Clinical Monitoring with Wireless Sensor Networks, Test of Time Award Speech, ACM Conference on Embedded Networked Sensor Systems (SenSys'22), November 2022.

• Internet of Medical Things: Predicting Clinical Outcomes with Wearables (Slides) (Video), Brown School Open Classroom, Washington University, October 2022.

• Internet of Medical Things: Predicting Clinical Outcomes with Wearables (Slides) (Video), IoT Day Keynote, ACM International Conference on Mobile Systems, Applications, and Services (MobiSys'22), June 2022.

• Internet of Medical Things: Predicting Clinical Outcomes and Digital Phenotyping with Wearables and Machine Learning, Washington University School of Medicine, 2019.

• Predicting Clinical Outcomes with Wearables: Machine Learning from Small Data, mHealth Research Core, 2019.

• Machine Learning for Healthcare: From Wearables to Electronic Health Record, AI in Health, Washington University, 2019.

• Wireless Clinical Monitoring at Scale, Distinguished Lecture, DGIST Global Innovation Festival (DGIF), November 2013.

• Toward a Two-Tier Clinical Warning System for Hospitalized Patients, American Medical Informatics Association Annual Symposium (AMIA'11), October 2011.

• Reliable Clinical Monitoring using Wireless Sensor Networks: Experience in a Step-down Hospital Unit, ACM Conference on Embedded Networked Sensor Systems (SenSys'10), November 2010.

• Toward Wireless Clinical Monitoring in General Hospital Units, PRECISE Seminar, University of Pennsylvania, October 2010.

Media

• WashU AI for Health Institute Emphasizes Multidisciplinary Approach [Healthcare Innovation]

• AI for Health Seed Grant Program Awards Nearly $300,000 to Advance AI-Driven Health Research [Press Release]

• Clinical AI That Is More Honest About What It Doesn't Know [Press Release]

• Clinically Informed AI Outperforms Foundation Models in Spinal Cord Disease Prediction [Press Release]

• Surgical AI Adapts to Changing Patients [Press Release]

• Newsletter of the AI for Health Institute (Summer 2026)

• Newsletter of the AI for Health Institute (Fall 2025)

• AI 'CHEF' Could Help Those With Cognitive Declines Complete Home Tasks [Press Release]

• PhD student Claire Najjuuko received the Provost's Research Excellence Award

• PhD student Ruiqi Wang received Google PhD Fellowship

• Chenyang Lu joined the inaugural AI Advisory Board of WashU Law

• Data Science Uncovers Patterns in Health Service Use Linked to Child Mortality [Press Release]

• Sleep Data from Wearable Device May Help Predict Preterm Birth [Press Release]

• Predicting Pain with Machine Learning [Press Release]

• Inaugural AI for Health Seed Funding Program Awards Nearly $300,000

• Machine Learning Could Help Predict Adherence to HIV Treatment in Adolescents [Press Release]

• Foundation AI Model Predicts Postoperative Risks from Clinical Notes [Press Release]

• Bridging AI and Healthcare: An In-depth Conversation with Dr. Chenyang Lu (Apple Podcasts, Spotify) [Gateway to Informatics]

• WashU Institute Studying the Use of AI in Healthcare Fields [KMOX Radio]

• Fireside Chat about AI-driven Phenotyping of Chiari Malformation [Park-Reeves Chiari & Syringomyelia Center]

• Engineering AI for Health with Chenyang Lu (YouTube) (Spotify) [Engineering the Future]

• $5 million NIH Grant to Find Causes of Chronic Pain After Surgery [Press Release]

• Researchers Define New Subtypes of Common Brain Disorder [Press Release]

• What Your Fitness Tracker Says About Your Mental Health [IEEE Transmitter]

• Artificial Intelligence in Medicine (WashU video featuring our AI for Health research and students)

• Machine Learning Tool Accurately Predicts Spine Surgery Outcomes [Health IT Analytics]

• New Machine Learning Method Can Better Predict Spine Surgery Outcomes [Press Release]

• How AI Can Boost Cancer, Depression and Perioperative Care [Healthcare IT News]

• UVA Computer Science Alum Chenyang Lu Named Director of Washington University's AI for Health Institute [UVA]

• Bluetooth System an Effective Tool for Health Care Worker COVID-19 Contact Tracing [Healio]

• AI for Health research featured in the WashU video on AI and Medicine and the cover article in the Momentum magazine.

• Wearable Tech for Contact Tracing Developed [Press Release]

• AI for Health Institute Launches to Promote Growing Intersection of Artificial Intelligence, Health [Press Release]

• Google Fitbits May Be Able to Predict Complications in Pancreatic Cancer Surgeries [The Messenger]

• Interdisciplinary Team Wins Award for Paper on Predicting Postoperative Complications with Wearables, Artificial Intelligence [Press Release]

• Artificial Intelligence May Assist Decisions on Which Patients Should Get Critical Life Support [Press Release]

• Data from Wearables Could Be a Boon to Mental Health Diagnosis [The Source]

• Wireless Clinical Monitoring Paper Received Test 0f Time Award [Press Release]

• Personalized Prediction of Depression Treatment Outcomes with Wearables [The Source]
  

• Machine-Learning Tools Predict Post-Op Complications, Surgery Duration [Health IT Analytics]
  

• AI Model Explores EHR Data to Predict Physician Burnout [Health Exec]
  

• Deep-Learning Model Predicts Physician Burnout Using EHR Logs [Health IT Analytics]
  

• Lu Studies Potential Benefits of AI in Health Care [The Source]
  

• Learning Physician Burnout from Electronic Health Record Activities [Press Release]
  

• Predicting Surgical Outcomes with Machine Learning [Press Release]
  

• Model Predicts Deterioration of Hospitalized Patients with Cancer (Healio)
  

• That new Fitbit does more than count steps. It may save your life one day. (Diagnostic and Interventional Cardiology)
  

• Research using Fitbit and machine learning to predict surgical outcomes featured in the Next Byte podcast
  

• Washington People: Chenyang Lu (profile of our work on healthcare using wearables and machine learning), November 2021.
  

• Wearable Fitness Trackers Help Physicians Track Patient Health, November 2021.
  

• Early Warning System Model Predicts Deterioration of Hospitalized Cancer Patients Based on Clinical Data, November 2021.
  

• Wireless Sensors Relay Medical Insight to Patients and Caregivers, IEEE Signal Processing Magazine, 29(3): 8-12, May 2012.
  

• Hospital Tests Wireless Patient Monitoring, UPI, August 2011.
  

• Wireless Network in Hospital Monitors Vital Signs, August 2011.

Papers

• Alaverdyan H, Holzer KJ, Creed MC, Shepherd AJ, Rodebaugh TL, Sinha P, Lu C, Karlsson P, Haroutounian S. IMPETUS project protocol: a translational team science approach to tackle chronic postsurgical pain. Neurobiology of Pain. 2026:100237. doi:10.1016/j.ynpai.2026.100237.

• Gilbert A, Gacad A, Parrish L, Nguyen O, Turner K, Tarver W, Jonnalagadda P, Tsagli D, Wu B, Lu C, Sachs R, Evbuomwan O, Kepper M. AI-Enabled Digital Health Tool Implementation in Clinical Practice Settings: Umbrella Review. JMIR AI 2026;5:e101509. DOI: 10.2196/101509.

• Calderon A, Zhu D, Zainal NH, Lu C, Fitzsimmons-Craft EE, Wilfley DE, Eisenberg D, Taylor CB, Newman MG. Baseline prediction of two-year prevention and remission of anxiety, depression, and eating disorders in college students receiving a digital guided self-help intervention: derivation and external validation of a machine learning algorithm across 26 population-based cohorts. Psychotherapy and Psychosomatics. 2026. doi:10.1159/pps/aemag007.

• Yakdan S, Xu Z, Zhang J, Arkam F, Rodebaugh TL, Goodin BR, Neuman BJ, Steinmetz MP, Kelly MP, Bess S, Yoon J, Ray WZ, Lu C, Frumkin MR, Greenberg JK. Real-time symptom and wearable monitoring may improve prediction of postoperative recovery in lumbar spine surgery beyond standard measures. Neurosurgery. 2026. doi:10.1227/neu.0000000000004195.

• Roland JL, Meehan T, Gabliani A, Marek S, Dosenbach N, Shimony JS, Jakati N, Li Y, Sugrue L, Strahle JM, Lu C, Limbrick DD, Haller G. Chiari malformation type 1 is associated with a smaller fourth ventricle volume - a multi-cohort replication study. Scientific Reports. 2026. doi:10.1038/s41598-026-56778-x.

• Nordman J, Najjuuko C, Jeschke N, Snyders RE, Vazquez Guillamet MC, Shapiro J, Leone C, Dethloff M, Babcock H, Reich P, Whalen K, Zhang L, Luong L, Lu C, Atkinson A, Marschall J, Sung A. The best of both worlds: how combining a large language model and a rules-based algorithm makes CAUTI surveillance more efficient. Clinical Infectious Diseases. 2026;ciag301. doi:10.1093/cid/ciag301.

• Frumkin MR, Messner GR, Holzer KJ, Xu Z, Rodebaugh TL, Bernstein H, Frey K, Ahuja S, Hanson J, Lu C, Haroutounian S. Remote perioperative symptom monitoring through a smartphone is feasible: evidence from the Personalised Prediction of Persistent Postsurgical Pain (P5) study. British Journal of Anaesthesia. 2026;137(1):371–374. doi:10.1016/j.bja.2026.03.066.

• Wang S, Xu Z, Najjuuko C, Alba C, Lu C. CURA: Clinical Uncertainty Risk Alignment for Language Model–Based Risk Prediction. In: Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers); 2026 Jul 2–7; San Diego, CA. pp. 34000–34018. doi:10.18653/v1/2026.acl-long.1567.

• Gupta VP, Xu Z, Greenberg JK, Strahle JM, Haller G, Meehan T, Roberts A, Limbrick DD Jr, Lu C. Letter: Using artificial intelligence to identify three presenting phenotypes of Chiari type 1 malformation and syringomyelia. Neurosurgery. 2026 May;98(5):e62–e64. doi:10.1227/neu.0000000000003985.

• Ssentumbwe V, Matovu F, Namatovu P, Najjuuko C, Kizito S, Nabayinda J, Nartey P, Namuwonge F, Nabunya P, Lu C, Mutumba M, Ssewamala FM. Using machine learning to predict depression among adolescents living with HIV in Uganda. Global Social Welfare. 2026. doi:10.1007/s40609-026-00456-3.

• Holzer KJ, Alaverdyan H, Xu Z, Frumkin MR, Frey KA, Gregory SH, Rodebaugh TL, Lu C, King CR, Head D, Kannampallil T, Haroutounian S. Protocol for personalised prediction of persistent postsurgical pain. BMJ Open. 2026;16:e107055. doi:10.1136/bmjopen-2025-107055.

• Yakdan S, Warner BC, Ghogawala Z, Ray WZ, Bydon M, Steinmetz MP, Griffey RT, Foraker R, Wilcox A, Lu C, Greenberg JK. Clinically-guided models or foundation models? Predicting cervical spondylotic myelopathy from electronic health records. npj Digital Medicine. 2026;9:153. doi:10.1038/s41746-026-02337-7.

• Zhang J, Wang R, Xu Z, Liu H, Rodriguez J, Cos H, Srivastava R, Raper L, Sanford D, Hammill C, Lu C. Addressing cohort variability with adaptive fusion of wearable and clinical data: A case study in predicting pancreatic surgery outcomes. ACM Transactions on Computing for Healthcare. 2026. doi:10.1145/3788681.

• Wang R, Gao P, Lynch P, Liu T, Lee Y, Baum CM, Connor LT, Lu C. CHEF-VL: Detecting cognitive sequencing errors in cooking with vision-language models. 2025;9(4):220. doi:10.1145/3770714.

• Najjuuko C, Xu Z, Kizito S, Lu C, Ssewamala FM. Patterns of maternal and child health services utilization and associated socioeconomic disparities in sub-Saharan Africa. Nature Communications. 2025; 16:7840. https://doi.org/10.1038/s41467-025-61350-8

• Bhan R, Yakdan S, Diaz C, Zhang J, Xu Z, Joseph K, Plog B, Yahanda AT, Zhang JK, Javeed S, Ray WZ, Gupta M, Lu C, Kelly MP, Greenberg JK, Neuman B. Association between preoperative activity levels and postoperative complications in adult spinal deformity surgery. Spine Deformity. 2025; doi:10.1007/s43390-025-01158-0. PMID: 40790108.

• Yakdan S, Zhang J, Botterbush K, Kaleem MI, Benedict B, Xu Z, Frumkin M, Javeed S, Zhang JK, Ogunlade J, Lu C, Neuman B, Steinmetz M, Ray WZ, Gupta M, Greenberg JK. Sleep physiology in spine disease: A comparative study of cervical and lumbar patients. Clinical Spine Surgery. 2025 Sep 1. doi:10.1097/BSD.0000000000001926.

• Najjuuko C, Brathwaite R, Mutumba M, Childress S, Nannono S, Namatovu P, Lu C, Ssewamala FM. Identifying predictors of problematic substance use among youth living with HIV in Uganda: A machine learning approach. AIDS and Behavior. Accepted.

• Vazquez-Guillamet MC, Zhang J, Bewley A, Atkinson A, Holtz H, Wang Z, Brougham N, Lu C, Kollef MH, Payne P, Warren DK, Fraser VJ. Integrating nonindividual patient features in machine learning models of hospital-onset bacteremia. JAMA Network Open. 2025; 8(7):e2518815. doi:10.1001/jamanetworkopen.2025.18815

• Warner BC, Zhao P, Herzog ED, Frolova AI, England SK, Lu C. Validation of sleep-based actigraphy machine learning models for prediction of preterm birth. npj Women’s Health. 2025; 3:40. https://doi.org/10.1038/s44294-025-00082-y

• Xu Z, Zhang J, Haroutounian S, Liu H, Cao Z, Messner GR, Alaverdyan HB, Ahuja S, Koshy R, Hanns J, Frumkin M, Rodebaugh TL, Lu C. Incorporating uncertainty in predictive models using mobile sensing and clinical data: A case study on persistent post-surgical pain. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2025; 9(2): Article 58, 33 pages. https://doi.org/10.1145/3729488

• Warner BC, Xu Z, Haroutounian S, Kannampallil T, Lu C. Utilizing semantic textual similarity for clinical survey data feature selection. Findings of the Association for Computational Linguistics: ACL 2025. 2025:502–520. Association for Computational Linguistics; Vienna, Austria.

• Yakdan S, Zhang J, Benedict B, Xu Z, Javeed S, Zhang JK, Steel BA, Gupta VP, Botterbush K, Piccirillo JF, Rodebaugh TL, Goodin BR, Buchowski JM, Neuman B, Hafez D, Kelly M, Ray WZ, Lu C, Frumkin M, Greenberg JK. Multi-domain postoperative recovery trajectories after lumbar and thoracolumbar spine surgery. Spine J. 2025. doi:10.1016/j.spinee.2025.05.017.

• Liao J, Adolphson M, Li H, Sikder DK, Lu C, Yang L. Whispering-gallery-mode resonators for detection and classification of free-flowing nanoparticles and cells through photoacoustic signatures. Light: Science & Applications [Forthcoming].

• Najjuuko C, Brathwaite R, Xu Z, Kizito S, Lu C, Ssewamala FM. Using machine learning to predict poor adherence to antiretroviral therapy among adolescents living with HIV in low-resource settings. AIDS. 2025 Feb 25. doi: 10.1097/QAD.0000000000004163.

• Alba C, Xue B, Abraham J, Kannampallil T, Lu C. The foundational capabilities of large language models in predicting postoperative risks using clinical notes. npj Digital Medicine. 2025;8:95. doi:10.1038/s41746-025-01489-2.

• Zhu D, Xue B, Shah N, Payne PRO, Lu C, Said AS. Multi-modal prediction of extracorporeal support—a resource-intensive therapy, utilizing a large national database. JAMIA Open. 2025 Feb;8(1):ooae158. doi: 10.1093/jamiaopen/ooae158.

• Shen J, Xue B, Kannampallil T, Lu C, Abraham J. A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients. J Am Med Inform Assoc. 2024;[Epub ahead of print]. doi:10.1093/jamia/ocae316.

• Gupta VP, Xu Z, Greenberg JK, Strahle JM, Haller G, Meehan T, Roberts A, Limbrick DD Jr, Lu C. Using artificial intelligence to identify three presenting phenotypes of Chiari type-1 malformation and syringomyelia. Neurosurgery. 2024 Nov 18. doi: 10.1227/neu.0000000000003249.

• Vazquez Guillamet MC, Liu H, Atkinson A, Fraser VJ, Lu C, Kollef MH. Performance of Risk Models for Antimicrobial Resistance in Adult Patients With Sepsis. JAMA Netw Open. 2024;7(11):e2443658. doi:10.1001/jamanetworkopen.2024.43658

• Z. Xu, J. Zhang, J. Greenberg, M. Frumkin, S. Javeed, J. Zhang, B. Benedict, K. Botterbush, T. Rodebaugh, W. Ray, C. Lu, Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing: A Case Study with Patients Undergoing Lumbar Spine Surgery, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'24), 8(2), June 2024.

• J.K. Greenberg, M. Frumkin, Z. Xu, J. Zhang, S. Javeed, J. Zhang, B. Benedict, K. Botterbush, S. Yakdan, C.A. Molina, B.H. Pennicooke, D. Hafez, J.I. Ogunlade, N. Pallotta, M.C. Gupta, J.M. Buchowski, B. Neuman, M. Steinmetz, Z. Ghogawala, M.P. Kelly, B.R. Goodin, J.F. Piccirillo, T.L. Rodebaugh, C. Lu, W.Z. Ray, Preoperative Mobile Health Data Improve Predictions of Recovery from Lumbar Spine Surgery, Neurosurgery, 2024.

• M. C. Vazquez Guillamet, A. Rjob, J. Zhang, R. Dai, R. Wang, C. Damulira, R. Hamauon, J. Candell, J. H. Kwon, H. Babcock, T. C. Bailey, C. Lu, V. Fraser, Leveraging Bluetooth Low-Energy Technology to Improve Contact Tracing Among Healthcare Personnel in Hospital Setting During the Coronavirus Disease 2019 (COVID-19) Pandemic, Infection Control & Hospital Epidemiology, pp. 1–3, 2023.

• J. Zhang, R. Dai, A. Rjob, R. Wang, R. Hamauon, J. Candell, T. Bailey, V.J. Fraser, M.C. Vazquez Guillamet, C. Lu, Contact Tracing for Healthcare Workers in an Intensive Care Unit, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'23), 7(3), Article 141, September 2023.

• J.G.Z. Rodriguez, H. Cos, R. Srivastava, A. Bewley, L. Raper, D. Li, R. Dai, G.A. Williams, R.C. Fields, W.G. Hawkins, C. Lu, D.E. Sanford, C.W. Hammill, Preoperative Levels of Physical Activity Can Be Increased in Pancreatectomy Patients ` via a Remotely Monitored, Telephone-based Intervention: a Randomized Trial, ` Surgery in Practice and Science, Volume 15, 2023.

• B. Xue, A. Said, Z. Xu, H. Liu, N. Shah, H. Yang, P. Payne, C. Lu, ISARIC Clinical Characterisation Group, Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation, ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD’23), August 2023.

• R. Dai, T Kannampallil, S. Kim, V. Thornton, L. Bierut, C. Lu, Detecting Mental Disorders with Wearables: A Large Cohort Study, ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI'23), May 2023. Best Paper Award for IoT Data Analytics

• N. Shah, B. Xue, Z. Xu, H. Yang, E. Marwali, H. Dalton, P.P.R. Payne, C. Lu, A.S. Said, ISARIC Clinical Characterisation Group, Validation of Extracorporeal Membrane Oxygenation Mortality Prediction & Severity of Illness Scores in an International COVID-19 Cohort, Artificial Organs, 2023.

• B. Xue, N. Shah, H. Yang, T. Kannampallil, P.R.O. Payne, C. Lu, A.S. Said, Multi-horizon Predictive Models for Guiding Extracorporeal Resource Allocation in Critically Ill COVID-19 Patients, Journal of the American Medical Informatics Association, 30(4):656–667, April 2023.

• J.K. Greenberg, S. Javeed, J.K Zhang, B. Benedict, M. Frumkin, Z. Xu, J. Zhang, T. Rodebaugh, C. Lu, M. Steinmetz, Z. Ghogawala, M. Bydon, W.Z. Ray, Current and Future Applications of Mobile Health Technology for Evaluating Spine Surgery Patients: A Review, Journal of Neurosurgery: Spine, 2023.

• J.K. Greenberg, M.R. Frumkin, S. Javeed, J. Zhang, R. Dai, C.A. Molina, B.H. Pennicooke, N. Agarwal, P. Santiago, M.L. Goodwin, D. Jain, N. Pallotta, M.C. Gupta, J.M. Buchowski, E.C. Leuthardt, Z. Ghogawala, M.P. Kelly, B.L. Hall, J.F. Piccirillo, C. Lu, T.L. Rodebaugh, and W.Z. Ray, Feasibility and Acceptability of a Preoperative Multimodal Mobile Health Assessment in Spine Surgery Candidates, Neurosurgery, 92(3):538-546, March 2023.

• S.S Lou, H. Liu, D. Harford, C. Lu, and T. Kannampallil, Characterizing the Macrostructure of EHR Work Using Raw Audit Logs: an Unsupervised Action Embeddings Approach, Journal of the American Medical Informatics Association, 30(3):539–544, March 2023.

• J. Abraham, B. Bartek, A. Meng, C. Ryan King, B. Xue, C. Lu, M. Avidan, Integrating Machine Learning Predictions for Perioperative Risk Management: Towards an Empirical Design of a Flexible-Standardized Risk Assessment Tool, Journal of Biomedical Informatics, Volume 137, 2023.

• H. Cos, J.G.Z. Rodríguez, R. Srivastava, A. Bewley, L. Raper, D. Li, R. Dai, G.A. Williams, R.C. Fields, W.G. Hawkins, C. Lu, D.E. Sanford, and C.W. Hammill, 4,300 Steps per Day Prior to Surgery Are Associated with Improved Outcomes After Pancreatectomy, HPB, 25(1):91-99, January 2023.

• D. Li, B. Xue, C. King, B. Fritz, M. Avidan, J. Abraham, and C. Lu, Self-explaining Hierarchical Model for Intraoperative Time Series, IEEE International Conference on Data Mining (ICDM’22), November 2022.

• H. Liu, M. Montana, D. Li, C. Renfroe, T. Kannampallil, and C. Lu, Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks, ACM International Conference on Information and Knowledge Management (CIKM'22), October 2022.

• R. Dai, T. Kannampallil, J. Zhang, N. Lv, J. Ma, and C. Lu, Multi-Task Learning for Randomized Controlled Trials: A Case Study on Predicting Depression with Wearable Data, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'22), 6(2), Article 50, July 2022.

• J. Zhang, D. Li, R. Dai, H. Cos, G.A. Williams, L. Raper, C.W. Hammill, and C. Lu, Predicting Post-Operative Complications with Wearables: A Case Study with Patients Undergoing Pancreatic Surgery, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (UbiComp'22), 6(2), Article 87, July 2022. Distinguished Paper Award

• B. Xue, Y. Jiao, T. Kannampallil, B. Fritz, C. King, J. Abraham, M. Avidan, and C. Lu, Perioperative Predictions with Interpretable Latent Representation, ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD’22), August 2022.

• H. Liu, S.S. Lou, B. Warner, D.R. Harford, T. Kannampallil, and C. Lu, HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records, ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD’22), August 2022.

• S.S. Lou, H. Liu, C. Lu, T.S. Wildes, B.L. Hall, and T. Kannampallil, Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders, Anesthesiology, Vol. 137, 55–66, July 2022.

• S.S. Lou, D. Lew, D.R. Harford, C. Lu, B.A. Evanoff, J.G. Duncan, and T. Kannampallil, Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study, Journal of General Internal Medicine, June 2022.

• T. Kannampallil, R. Dai, N. Lv, L. Xiao, C. Lu, O.A. Ajilore, M.B. Snowden, E.M. Venditti, L.M. Williams, E.A. Kringle, and J. Ma, Cross-trial Prediction of Depression Remission Using Problem-solving Therapy: A Machine Learning Approach, Journal of Affective Disorders, Volume 308, Pages 89-97, 2022.

• S.S. Lou, H. Liu, B.C. Warner, D. Harford, C. Lu, and T. Kannampallil, Predicting Physician Burnout using Clinical Activity Logs: Model Performance and Lessons Learned, Journal of Biomedical Informatics, Volume 127, 2022.

• Y. Jiao, B. Xue, C. Lu, M.S. Avidan, and T. Kannampallil, Continuous Real-time Prediction of Surgical Case Duration Using a Modular Artificial Neural Network, British Journal of Anaesthesia, 128(5):829-837, May 2022.

• D. Li, P. Lyons, J. Klaus, B. Gage, M. Kollef, and C. Lu, Integrating Static and Time-Series Data in Deep Recurrent Models for Oncology Early Warning Systems, ACM International Conference on Information and Knowledge Management (CIKM'21), November 2021.

• R. Dai, C. Lu, L. Yun, E. Lenze, M. Avidan, and T. Kannampallil, Comparing Stress Prediction Models Using Smartwatch Physiological Signals and Participant Self-Reports, Computer Methods and Programs in Biomedicine, volume 208, 2021.

• B. Xue, D. Li, C. Lu, C.R. King, T. Wildes, M.S. Avidan, T. Kannampallil, and J. Abraham, Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications, JAMA Network Open, 4(3):e212240, 2021.

• H. Cos, D. Li, G. Williams, J. Chininis, R. Dai, J. Zhang, R. Srivastava, L. Raper, D. Sanford, W. Hawkins, C. Lu, and C.W. Hammill, Predicting Outcomes in Patients Undergoing Pancreatectomy Using Wearable Technology and Machine Learning: Prospective Cohort Study, Journal of Medical Internet Research, 23(3):e23595, 2021.

• R. Dai, C. Lu, M. Avidan, and T. Kannampallil, RespWatch: Robust Measurement of Respiratory Rate on Smartwatches with Photoplethysmography, ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI'21), May 2021.

• D. Li, J. Vaidya, M. Wang, B. Bush, C. Lu, M. Kollef, and T. Bailey, Feasibility Study of Monitoring Deterioration of Outpatients Using Multi-modal Data Collected by Wearables, ACM Transactions on Computing for Healthcare, 1(1), Article 5, March 2020. Inaugural Issue

• D. Li, P. Lyons, C. Lu and M. Kollef, DeepAlerts: Deep Learning Based Multi-horizon Alerts for Clinical Deterioration on Oncology Hospital Wards, AAAI Conference on Artificial Intelligence (AAAI-20), February 2020.

• J.P. Burnham, C. Lu, L.H. Yaeger, T.C. Bailey and M.H. Kollef, Using Wearable Technology to Predict Health Outcomes: a Literature Review, Journal of the American Medical Informatics Association, 25(9): 1221-1227, September 2018. Editor's Choice

• X. Hu, R. Dor, S.Bosch, A. Khoong, J. Li, S. Stark, and C. Lu, Challenges in Studying Falls of Community-dwelling Older Adults in the Real World, IEEE International Conference on Smart Computing (SMARTCOMP'17), May 2017. (Invited Paper)

• D. Picker, M. Dans, K. Heard, T. Bailey, Y. Chen, C. Lu, and M.H Kollef, A Randomized Trial of Palliative Care Discussions Linked to an Automated Early Warning System Alert, Critical Care Medicine, 45(2): 234-240, February 2017.

• M.H. Kollef, K. Heard, Y. Chen, C. Lu, N. Martin and T. Bailey, Mortality and Length of Stay Trends Following Implementation of a Rapid Response System and Real-Time Automated Clinical Deterioration Alerts, American Journal of Medical Quality, 32(1): 12-18, January/February 2017.

• S.T. Micek, M. Samant, T. Bailey, Y. Chen, C. Lu, K. Heard and M.H. Kollef, Real-Time Automated Clinical Deterioration Alerts Predict Thirty-Day Hospital Readmission, Journal of Hospital Medicine, 11(11): 768-772, November 2016.

• Y. Wang, W. Chen, K. Heard, M. Kollef, T. Bailey, Z. Cui, Y. He, C. Lu, and Y. Chen, Mortality Prediction in ICUs Using a Novel Time-Slicing Cox Regression Method, American Medical Informatics Association Annual Symposium (AMIA), November 2015. Distinguished Paper Award

• M.H. Kollef, Y. Chen, K. Heard, G.N. LaRossa, C. Lu, N.R. Martin, N. Martin, S.T. Micek, and T. Bailey, A Randomized Trial of Real-Time Automated Clinical Deterioration Alerts Sent to a Rapid Response Team, Journal of Hospital Medicine, 9(7):424-429, July 2014.

• T. Bailey, Y. Chen, Y. Mao, C. Lu, G. Hackmann, S.T. Micek, K. Heard, K. Faulkner, and M.H. Kollef, A Trial of a Real-Time Alert for Clinical Deterioration in Patients Hospitalized on General Medical Wards, Journal of Hospital Medicine, 8(5): 236-242, May 2013.

• R. Dor, G. Hackmann, Z. Yang, C. Lu, Y. Chen, M. Kollef and T.C. Bailey, Experiences with an End-To-End Wireless Clinical Monitoring System, Conference on Wireless Health (WH'12), October 2012.

• Y. Mao, W. Chen, Y. Chen, C. Lu, M. Kollef and T.C. Bailey, An Integrated Data Mining Approach to Real-time Clinical Monitoring and Deterioration Warning, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'12), August 2012.

• Y. Mao, Y. Chen, G. Hackmann, M. Chen, C. Lu, M. Kollef, and T.C. Bailey, Early Deterioration Warning for Hospitalized Patients by Mining Clinical Data, International Journal of Knowledge Discovery in Bioinformatics, 2(3):1-20, 2012. (Extended version of the BioDM'11 paper)

• Y. Mao, Y. Chen, G. Hackmann, M. Chen, C. Lu, M. Kollef and T.C. Bailey, Medical Data Mining for Early Deterioration Warning in General Hospital Wards, ICDM Workshop on Biological Data Mining and its Applications in Healthcare (BioDM'11), December 2011.

• G. Hackmann, M. Chen, O. Chipara, C. Lu, Y. Chen, M. Kollef, and T.C. Bailey, Toward a Two-Tier Clinical Warning System for Hospitalized Patients, American Medical Informatics Association Annual Symposium (AMIA), October 2011.

• O. Chipara, C. Lu, T.C. Bailey and G.-C. Roman, Reliable Clinical Monitoring using Wireless Sensor Networks: Experience in a Step-down Hospital Unit, ACM Conference on Embedded Networked Sensor Systems (SenSys'10), November 2010. Test-of-Time Award

• J. Ko, C. Lu, M.B. Srivastava, J.A. Stankovic, A. Terzis, and M. Welsh, Wireless Sensor Networks for Healthcare, Proceedings of IEEE, 98(11):1947-1960, November 2010.

• O. Chipara, C. Brooks, S. Bhattacharya, C. Lu, R.D. Chamberlain, G.-C. Roman, and T.C. Bailey, Reliable Real-time Clinical Monitoring Using Sensor Network Technology, American Medical Informatics Association Annual Symposium (AMIA), November 2009.