Remote Patient Monitoring

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9 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

AI-based detection of worsening heart failure from low-resolution telemonitoring data
Sep 24, 2026Erik Aerts, Yinan Yu, Annika Rosengren +5Remote Patient Monitoring Time Series Transformers
Department of Computer Science and Engineering, Chalmers Universiy of Technology, Gothenburg, Sweden · Department of Computer Science and Engineering, University of Gothenburg, Gothenburg, Sweden · Center for Digital Health, Sahlgrenska University Hospital, Gothenburg, Sweden +3
Establishing the Minimal Clinically Important Difference (MCID) for Smartphone-Derived Gait Measures in Multiple Sclerosis
Jun 26, 2026Mike D Rinderknecht, Bernhard Fehlmann, Dimitar Stanev +7Gait Analysis Remote Patient Monitoring
F.Hoffmann-La Roche Ltd., Basel, Switzerland · Faculty of Medicine, University Vita-Salute San Raffaele, Milan, Italy · Scientific Institute IRCCS San Raffaele, Milan, Italy +3
AI-driven Optimisation of Quality of Recovery (QoR) in Remote Patient Monitoring
Jun 22, 2026Yansong Liu, Li-Hsi, Lin +4Clinical Outcome Prediction Remote Patient Monitoring
Sonny · University College London, London, United Kingdom · Ethera Health Ltd, London, United Kingdom
Clinical Assistant for Remote Engagement Link (CARE-link): A Web-Based Electronic Health Records Software for Managing Diabetes
Jun 3, 2026Prince Ebenezer Adjei, Joshua Teye Tettey, Toufiq Musah +2Clinical Decision Support Remote Patient Monitoring
Global One Health Research Group, Bernhard Nocht Institute of Tropical Medicine, Bernhard-Nocht-Str. 74, 20359 Hamburg, Germany · Global Health and Infectious Diseases Research Group, Kumasi Centre for Collaborative Research in Tropical Medicine, P.O. Box 1936, Kumasi, Ghana · Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana +1
Veritas-RPM: Provenance-Guided Multi-Agent False Positive Suppression for Remote Patient Monitoring
Apr 17, 2026Aswini Misro, Vikash Sharma, Shreyank N GowdaHealthcare Remote Patient Monitoring
YouDiagnose Limited / NHS, London, UK · YouDiagnose Limited, London, UK · School of Computer Science, University of Nottingham, Nottingham, UK
From Days to Minutes: An Autonomous AI Agent Achieves Reliable Clinical Triage in Remote Patient Monitoring
Mar 10, 2026Seunghwan Kim, Tiffany H. Kung, Heena Verma +8Remote Patient Monitoring Clinical Triage
AnsibleHealth Inc., San Francisco, USA · AnsibleHealth Inc., San Francisco, CA, USA · Stanford School of Medicine, Stanford, USA +3
Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics
Feb 23, 2026Yue Pan, Xingyao Wang, Hanyue Zhang +6Remote Patient Monitoring Clinical Speech Processing
School of Information Science and Technology, Southeast University, Nanjing, China · Department of Cardiology, Chongqing Hospital of Jiangsu Province Hospital, Chongqing, China · Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China +3
Non-Contact Physiological Monitoring in Pediatric Intensive Care Units via Adaptive Masking and Self-Supervised Learning
Feb 17, 2026Mohamed Khalil Ben Salah, Philippe Jouvet, Rita NoumeirRemote Photoplethysmography Heart Rate Estimation
Biomedical Information Processing Laboratory, École de Technologie Supérieure, University of Quebec, Montreal, QC, Canada · Pediatric Intensive Care Unit, CHU Sainte-Justine, Montreal, QC, Canada
WoundAIssist: Development and Evaluation of an AI-Based Mobile Application for Remote Chronic Wound Care in Elderly Patients
Jun 6, 2025Vanessa Borst, Anna Riedmann, Tassilo Dege +4Remote Patient Monitoring Human-Centered AI
Institute of Computer Science, University of Würzburg, Germany · University Hospital of Würzburg, Germany

Sep 24, 2026J/K move · Enter open · S save

AI-based detection of worsening heart failure from low-resolution telemonitoring data

Erik Aerts, Yinan Yu, Annika Rosengren, Michael Fu+4

Department of Computer Science and Engineering, Chalmers Universiy of Technology, Gothenburg, Sweden · Department of Computer Science and Engineering, University of Gothenburg, Gothenburg, Sweden · Center for Digital Health, Sahlgrenska University Hospital, Gothenburg, Sweden+3

Remote Patient Monitoring Time Series Transformers Anomaly Detection

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Objective: Heart failure (HF) presents a healthcare challenge due to its high comorbidity burden, aging patient population and frequent hospitalizations. Remote monitoring offers a promising approach to managing HF patients by early detection of health deterioration. Developing autonomous systems to detect signs of worsening in telemonitoring data is of interest to reduce the workload of healthcare personnel. Methods: We propose the TRACER model, a Transformer with Contrastive Event Representation, designed to predict timelines leading to rare hospitalization events in low-resolution and irregularly sampled telemonitoring data. TRACER incorporates time-aware embeddings for each biomarker, contrastive pre-training to enhance anomaly detection via representation learning, and independent binary classifiers for detection. We used measurement data containing remotely recorded biomarker sequences from 276 HF patients segmented into overlapping windows based on temporal rules, and labeled the windows based on the occurrence of HF relevant hospitalizations at the latter edge of the window. Results: TRACER was able to correctly predict 66.7% timelines leading up to HF hospitalizations in the highly imbalanced real-world dataset with an overestimation of 7.9%. Reformulating the training of TRACER as an event detection problem improved the predictive performance compared with training directly on forecasting windows, enabling more effective use of the limited hospitalization events. Conclusion: TRACER demonstrated superior performance in detecting signs of worsening status in real-world telemonitoring data compared to the other tested models. Significance: TRACER shows promise in identifying signs of clinical deterioration that allow for alerts to be generated to provide counteractive treatment in patients with HF.