ArticleIEEE journal of translational engineering in health and medicine2022
A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning.
Article in IEEE journal of translational engineering in health and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 5 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 5 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- AI and Internet of Things for Chronic Obstructive Pulmonary Disease Remote Monitoring: Systematic Review of Exacerbation Prediction and Key Physiological Variables.JMIR medical informatics · 2026Pooled it
- Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review.BMC medical informatics and decision making · 2025Pooled it
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Artificial intelligence techniques applied to anxiety disorders recognition: a systematic review.Frontiers in digital health · 2025Pooled it
- Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.Journal of medical Internet research · 2024Pooled it
- Wearable bioelectronics enabled by conductive hydrogels: From materials innovation to clinical translation.Materials today. Bio · 2026Review
- Integrated Clinical-Molecular Risk Stratification in Diffuse Large B-Cell Lymphoma: Machine Learning Survival Analysis.JMIR medical informatics · 2026Article
- Short-term prediction of COPD exacerbations based on wearable vital sign monitoring.PLOS digital health · 2026Article
- AI and Big Data in Oncology: A Physician-Centered Perspective on Emerging Clinical and Research Applications.Cancer innovation · 2026Review
- Association of Mobile-Enhanced Remote Patient Monitoring with Blood Pressure Control in Hypertensive Patients with Comorbidities: A Multicenter Pre-Post Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Reforming disease prognosis and treatment prediction for palliative care with hybrid metaheuristic deep neural architectures in IoT healthcare ecosystems.Scientific reports · 2025Article
- A scoping review of adult NCD-relevant phenotypes measured in today's large child cohort studies.Pediatric research · 2025Article
- Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study.JMIR medical informatics · 2025Article
- Precision management in chronic disease: An AI empowered perspective on medicine-engineering crossover.iScience · 2025Review
- The future of multimorbidity management in the older adults: transforming AI-enabled precision medicine.Frontiers in public health · 2025Review
- Understanding Genetic Screening: Harnessing Health Information to Prevent Disease Risks.International journal of medical sciences · 2025Review
- IoT-enabled biosensors for real-time monitoring and early detection of chronic diseases.Physical activity and nutrition · 2024Article
- A systematic review on artificial intelligence approaches for smart health devices.PeerJ. Computer science · 2024Review
- Application of wearable devices for monitoring cardiometabolic dysfunction under the exposome paradigm.Chronic diseases and translational medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents an integrated and scalable precision health service for health promotion and chronic disease prevention. Continuous real-time monitoring of lifestyle and environmental factors is implemented by integrating wearable devices, open environmental data, indoor air quality sensing devices, a location-based smartphone app, and an AI-assisted telecare platform. The AI-assisted telecare platform provided comprehensive insight into patients' clinical, lifestyle, and environmental data, and generated reliable predictions of future acute exacerbation events. All data from 1,667 patients were collected prospectively during a 24-month follow-up period, resulting in the detection of 386 abnormal episodes. Machine learning algorithms and deep learning algorithms were used to train modular chronic disease models. The modular chronic disease prediction models that have passed external validation include obesity, panic disorder, and chronic obstructive pulmonary disease, with an average accuracy of 88.46%, a sensitivity of 75.6%, a specificity of 93.0%, and an F1 score of 79.8%. Compared with previous studies, we establish an effective way to collect lifestyle, life trajectory, and symptom records, as well as environmental factors, and improve the performance of the prediction model by adding objective comprehensive data and feature selection. Our results also demonstrate that lifestyle and environmental factors are highly correlated with patient health and have the potential to predict future abnormal events better than using only questionnaire data. Furthermore, we have constructed a cost-effective model that needs only a few features to support the prediction task, which is helpful for deploying real-world modular prediction models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.