Securing Service in Remote Healthcare

2015 ◽  
pp. 65-84
Author(s):  
Tapalina Bhattasali ◽  
Rituparna Chaki ◽  
Nabendu Chaki ◽  
Khalid Saeed
Keyword(s):  
Remote Healthcare

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

scholarly journals An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care

2020 ◽  
Vol 13 (1) ◽  
pp. 6
Author(s):  
Rui Hu ◽  
Bruno Michel ◽  
Dario Russo ◽  
Niccolò Mora ◽  
Guido Matrella ◽  
...  
Keyword(s):  
Health Status ◽  
Healthcare Service ◽  
Behavioral Science ◽  
Elderly Care ◽  
Elderly Person ◽  
Real Life ◽  
Healthcare Services ◽  
Living Condition ◽  
Care Providers ◽  
Remote Healthcare

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort.

scholarly journals Ensemble Learning Based Postpartum Hemorrhage Diagnosis for 5G Remote Healthcare

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 18538-18548
Author(s):  
Yawei Zhang ◽  
Xin Wang ◽  
Ningyu Han ◽  
Rong Zhao
Keyword(s):  
Ensemble Learning ◽  
Postpartum Hemorrhage ◽  
Remote Healthcare

scholarly journals Remote monitoring in heart failure: current and emerging technologies in the context of the pandemic

Heart ◽  
2021 ◽  
Vol 107 (5) ◽  
pp. 366-372
Author(s):  
Donya Mohebali ◽  
Michelle M Kittleson
Keyword(s):  
Heart Failure ◽  
At Risk ◽  
Remote Monitoring ◽  
Reliable Method ◽  
Healthcare Delivery ◽  
Rapid Expansion ◽  
Monitoring Technology ◽  
Remote Healthcare ◽  
Monitoring Technologies

The incidence of heart failure (HF) remains high and patients with HF are at risk for frequent hospitalisations. Remote monitoring technologies may provide early indications of HF decompensation and potentially allow for optimisation of therapy to prevent HF hospitalisations. The need for reliable remote monitoring technology has never been greater as the COVID-19 pandemic has led to the rapid expansion of a new mode of healthcare delivery: the virtual visit. With the convergence of remote monitoring technologies and reliable method of remote healthcare delivery, an understanding of the role of both in the management of patients with HF is critical. In this review, we outline the evidence on current remote monitoring technologies in patients with HF and highlight how these advances may benefit patients in the context of the current pandemic.

scholarly journals Delivering healthcare remotely to cardiovascular patients during COVID-19

2020 ◽  
Vol 19 (6) ◽  
pp. 486-494 ◽  
Author(s):  
Lis Neubeck ◽  
Tina Hansen ◽  
Tiny Jaarsma ◽  
Leonie Klompstra ◽  
Robyn Gallagher
Keyword(s):  
Cardiovascular Disease ◽  
Usual Care ◽  
Mobile Apps ◽  
Digital Health ◽  
Well Being ◽  
Search Terms ◽  
Cardiovascular Patients ◽  
Cardiovascular Morbidity And Mortality ◽  
Remote Healthcare ◽  
Healthcare Interventions

Background Although attention is focused on addressing the acute situation created by the COVID-19 illness, it is imperative to continue our efforts to prevent cardiovascular morbidity and mortality, particularly during a period of prolonged social isolation which may limit physical activity, adversely affect mental health and reduce access to usual care. One option may be to deliver healthcare interventions remotely through digital healthcare solutions. Therefore, the aim of this paper is to bring together the evidence for remote healthcare during a quarantine situation period to support people living with cardiovascular disease during COVID-19 isolation. Methods The PubMed, CINAHL and Google Scholar were searched using telehealth OR digital health OR mHealth OR eHealth OR mobile apps AND COVID-19 OR quarantine search terms. We also searched for literature relating to cardiovascular disease AND quarantine. Results The literature search identified 45 potentially relevant publications, out of which nine articles were included. Three overarching themes emerged from this review: (1) preparing the workforce and ensuring reimbursement for remote healthcare, (2) supporting mental and physical health and (3) supporting usual care. Conclusion To support people living with cardiovascular disease during COVID-19 isolation and to mitigate the effects of quarantine and adverse effect on mental and physical well-being, we should offer remote healthcare and provide access to their usual care.

Development of an Ease-of-Use Remote Healthcare System Architecture Using RFID and Networking Technologies

2012 ◽  
Vol 36 (6) ◽  
pp. 3605-3619 ◽  
Author(s):  
Shih-Sung Lin ◽  
Min-Hsiung Hung ◽  
Chang-Lung Tsai ◽  
Li-Ping Chou
Keyword(s):  
Healthcare System ◽  
System Architecture ◽  
Ease Of Use ◽  
Remote Healthcare ◽  
Networking Technologies
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