Mobile Personal Health Care System for Patients with Diabetes

2010 ◽  
pp. 94-101 ◽  
Author(s):  
Fuchao Zhou ◽  
Hen-I Yang ◽  
José M. Reyes Álamo ◽  
Johnny S. Wong ◽  
Carl K. Chang
Keyword(s):  
Health Care ◽  
Health Care System ◽  
Personal Health ◽  
Patients With Diabetes ◽  
Care System

scholarly journals Patterns of engagement with the health care system and risk of subsequent hospitalization amongst patients with diabetes

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Paul E Ronksley ◽  
Pietro Ravani ◽  
Claudia Sanmartin ◽  
Hude Quan ◽  
Braden Manns ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care System ◽  
Patients With Diabetes ◽  
Care System

scholarly journals Integrated Health Care System Application in Patients With Hypertension and Diabetes: A Systematic Review and Meta-analysis

2021 ◽  
Author(s):  
Yan Zhao ◽  
Yue Ma ◽  
Chongbo Zhao ◽  
Jiahong Lu ◽  
Hong Jiang ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care System ◽  
Meta Analysis ◽  
Glycosylated Hemoglobin ◽  
Integrated Health Care ◽  
Controlled Trials ◽  
Integrated Health ◽  
Patients With Diabetes ◽  
Integrated Health Care System ◽  
Care System

Abstract 【Background】Integrated health care provide patients with comprehensive and continuous care, but it still no consequence whether it could improve the illness condition of patients who suffer from chronic disease, such as hypertension and diabetes. The objective of this study is to evaluate the effect of the integrated health care system in patients with hypertension and diabetes. 【Methods】Randomised controlled trials testing the effect of integrated health care system in patients with hypertension and diabetes were be included. We search in GIN, NICE, Cochrane, JBI, CINAHL, EMBASE, PUBMED, Web of Science, SINOMED, CNKI (Chinese database), WANFANG (Chinese database) and VIP (Chinese database) databases from the building date of database to 31/Oct/2020. The articles must meet the following inclusion criteria: research objects are patients who have been clearly diagnosed with hypertension and diabetes, all of the articles should use Integrated Health Care as intervention, the study type of article is randomized clinical trials, the articles should be published in Chinese or English. Risk of bias were assessed regarding randomisation, allocation sequence concealment, blinding, incomplete outcome data, selective outcome reporting, and other biases. 【Results】. 16 randomized controlled trials (seven hypertension, nine diabetes) involving 5,231 patients (2,593 with diabetes and 2,638 with hypertension) with intervention duration ranging from 6 to 24 months. Meta-analysis showed that integrated health care significantly improved systolic and diastolic blood pressure in patients with hypertension, it also significantly declined the level of glycosylated hemoglobin in diabetes. After using the integrated health care for 12 months, glycosylated hemoglobin was significantly decreased which compared to the 6-month intervention in patients with diabetes. 【Discussion】In this study, we undertook a meta-analysis of the published work that systematically evaluated the effects of integrated health care. Integrated Health Care could be beneficial to reduce glycosylated hemoglobin, and control blood pressure in patients with diabetes and hypertension. The diabetes management based on integrated health care is a long-term care process, and it would be effective over time.

scholarly journals Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study

10.2196/18012 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e18012
Author(s):  
Luis J Mena ◽  
Vanessa G Félix ◽  
Rodolfo Ostos ◽  
Armando J González ◽  
Rafael Martínez-Peláez ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Health Care ◽  
Health Care System ◽  
Blood Pressure Monitoring ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Personal Health ◽  
Usability Study ◽  
British Hypertension Society ◽  
Care System

Background Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. Objective This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people. Methods The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor. Results The employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds. Conclusions With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.

scholarly journals Towards for Designing Intelligent Health Care System Based on Machine Learning

2021 ◽  
Vol 17 (2) ◽  
pp. 120-128
Author(s):  
Nada Noori ◽  
Ali Yassin
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Health Care ◽  
Health Care System ◽  
Support Vector ◽  
Care Providers ◽  
Number Of Patients ◽  
Increased Risk ◽  
Patients With Diabetes ◽  
Care System

Health Information Technology (HIT) provides many opportunities for transforming and improving health care systems. HIT enhances the quality of health care delivery, reduces medical errors, increases patient safety, facilitates care coordination, monitors the updated data over time, improves clinical outcomes, and strengthens the interaction between patients and health care providers. Living in modern large cities has a significant negative impact on people’s health, for instance, the increased risk of chronic diseases such as diabetes. According to the rising morbidity in the last decade, the number of patients with diabetes worldwide will exceed 642 million in 2040, meaning that one in every ten adults will be affected. All the previous research on diabetes mellitus indicates that early diagnoses can reduce death rates and overcome many problems. In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people’s lives. In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies of patients. The proposed system uses five machine learning methods: K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM). The system selects the best classification method with high accuracy to optimize the diagnosis of patients with diabetes. The experimental results show that in the proposed system, the SVM classifier has the highest accuracy of 83%.

scholarly journals De-Identified Personal Health Care System Using Hadoop

2015 ◽  
Vol 5 (6) ◽  
pp. 1492 ◽  
Author(s):  
Dasari Madhavi ◽  
B.V. Ramana
Keyword(s):  
Health Care ◽  
Big Data ◽  
Health Information ◽  
Health Care System ◽  
Demographic Data ◽  
Data File ◽  
Personal Health Information ◽  
Map Reduce ◽  
Personal Health ◽  
Care System

Hadoop technology plays a vital role in improving the quality of healthcare by delivering right information to right people at right time and reduces its cost and time. Most properly health care functions like admission, discharge, and transfer patient data maintained in Computer based Patient Records (CPR), Personal Health Information (PHI), and Electronic Health Records (EHR). The use of medical Big Data is increasingly popular in health care services and clinical research. The biggest challenges in health care centers are the huge amount of data flows into the systems daily. Crunching this Big Data and de-identifying it in a traditional data mining tools had problems. Therefore to provide solution to the de-identifying personal health information, Map Reduce application uses jar files which contain a combination of MR code and PIG queries. This application also uses advanced mechanism of using UDF (User Data File) which is used to protect the health care dataset. De-identified personal health care system is using Map Reduce, Pig Queries which are needed to be executed on the health care dataset. The application input dataset that contains the information of patients and de-identifies their personal health care.  De-identification using Hadoop is also suitable for social and demographic data.

scholarly journals Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study (Preprint)

2020 ◽  
Author(s):  
Luis J Mena ◽  
Vanessa G Félix ◽  
Rodolfo Ostos ◽  
Armando J González ◽  
Rafael Martínez-Peláez ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Health Care ◽  
Health Care System ◽  
Blood Pressure Monitoring ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Personal Health ◽  
Usability Study ◽  
British Hypertension Society ◽  
Care System

BACKGROUND Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. OBJECTIVE This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people. METHODS The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor. RESULTS The employed artificial neural network model had good average accuracy (&gt;90%) and very strong correlation (&gt;0.90) (<i>P</i>&lt;.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds. CONCLUSIONS With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.

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