Observational studyJournal of medical systems2015
Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients.
Observational study in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- A Systematic Review of Fall Risk Factors in Stroke Survivors: Towards Improved Assessment Platforms and Protocols.Frontiers in bioengineering and biotechnology · 2022Pooled it
- A Systematic Review of Wearable Patient Monitoring Systems - Current Challenges and Opportunities for Clinical Adoption.Journal of medical systems · 2017Pooled it
- Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data.Nursing reports (Pavia, Italy) · 2026Article
- IFRA: A Machine Learning-Based Instrumented Fall Risk Assessment Scale Derived from an Instrumented Timed Up and Go Test in Stroke Patients.Healthcare (Basel, Switzerland) · 2026Article
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- A multiclass CNN cascade model for the clinical detection support of cardiac arrhythmia based on subject-exclusive ECG dataset.Biomedical engineering letters · 2022Article
- Technological Requirements and Challenges in Wireless Body Area Networks for Health Monitoring: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2022Review
- Mobile 5P-Medicine Approach for Cardiovascular Patients.Sensors (Basel, Switzerland) · 2021Article
- Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study.Journal of medical Internet research · 2020Article
- Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic.NAM perspectives · 2020Review
- Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning.Journal of medical systems · 2019Article
- Article
- Big data analytics to improve cardiovascular care: promise and challenges.Nature reviews. Cardiology · 2016Review
- A Computer-Aided Diagnosis System for Measuring Carotid Artery Intima-Media Thickness (IMT) Using Quaternion Vectors.Journal of medical systems · 2016Article
- A Visualization System for Interactive Exploration of the Cardiac Anatomy.Journal of medical systems · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.