Machine Learning-Based Cocoa E-Health System

2020 ◽  
Author(s):  
Albert Gyamfi ◽  
Sibdow Abdul-Jalil Iddrisu ◽  
Oluwatobi Adegbola
Keyword(s):  
Machine Learning ◽  
Health System

scholarly journals Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

10.2196/15182 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e15182 ◽  
Author(s):  
Mark P Sendak ◽  
William Ratliff ◽  
Dina Sarro ◽  
Elizabeth Alderton ◽  
Joseph Futoma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Improvement ◽  
Deep Learning ◽  
Health System ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Quality Improvement Effort ◽  
Management Platform ◽  
Routine Clinical Care ◽  
Improvement Effort

Background Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. Objective This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. Methods In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. Results Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. Conclusions Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

scholarly journals Review on Smart Health Care Monitoring Systems

2021 ◽  
pp. 418-422
Author(s):  
Vasishth V. Katre ◽  
Dr. P. N. Chatur
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health System ◽  
Monitoring Systems ◽  
Learning Approach ◽  
Health Issues ◽  
Medical Field ◽  
Machine Learning Approach ◽  
Smart Health ◽  
Health Care Monitoring

Document IoT is leading in smart health care system. Using different sensors it's possible to monitor the patients healthcare remotely. This is unimagined and leads to a spatial longitude amalgamated with machine learning approach. Leading to smart health care, and headway in medical field. It may lead to know severe health issues ahead of time which would be tranquil to the health system. Which would benefit the hospital administration and management. This paper elucidates on the distinct sort of IoT based health care monitoring systems. The aim is to juxtapose the present health care IoT systems.

scholarly journals S237 Validation of Machine Learning Tool to Determine Post-Polypectomy Surveillance Intervals After Screening Colonoscopy in a Large Health System

2021 ◽  
Vol 116 (1) ◽  
pp. S105-S105
Author(s):  
Camille Soroudi ◽  
Artin Galoosian ◽  
Sartajdeep Kahlon ◽  
Shailavi Jain ◽  
Alex N. Kokaly ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health System ◽  
Screening Colonoscopy ◽  
Learning Tool ◽  
Machine Learning Tool

scholarly journals COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population

2020 ◽  
Vol 10 (11) ◽  
pp. 3880 ◽  
Author(s):  
Vasilis Papastefanopoulos ◽  
Pantelis Linardatos ◽  
Sotiris Kotsiantis
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Time Series ◽  
Health System ◽  
Total Population ◽  
Outbreak Detection ◽  
Time Series Modeling ◽  
Learning Time ◽  
Raising Awareness ◽  
The Impact

The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future.

An accurate and dynamic predictive model for a smart M-Health system using machine learning

2020 ◽  
Vol 538 ◽  
pp. 486-502 ◽  
Author(s):  
Kashif Naseer Qureshi ◽  
Sadia Din ◽  
Gwanggil Jeon ◽  
Francesco Piccialli
Keyword(s):  
Machine Learning ◽  
Health System ◽  
Predictive Model

scholarly journals Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study (Preprint)

2019 ◽  
Author(s):  
Mark P Sendak ◽  
William Ratliff ◽  
Dina Sarro ◽  
Elizabeth Alderton ◽  
Joseph Futoma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Improvement ◽  
Deep Learning ◽  
Health System ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Quality Improvement Effort ◽  
Management Platform ◽  
Routine Clinical Care ◽  
Improvement Effort

BACKGROUND Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. OBJECTIVE This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. METHODS In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. RESULTS Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. CONCLUSIONS Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

scholarly journals Voice Disorder Detection via an m-Health System: Design and Results of a Clinical Study to Evaluate Vox4Health

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ugo Cesari ◽  
Giuseppe De Pietro ◽  
Elio Marciano ◽  
Ciro Niri ◽  
Giovanna Sannino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Clinical Study ◽  
Health System ◽  
Acoustic Analysis ◽  
Machine Learning Techniques ◽  
Voice Disorder ◽  
Rule Based ◽  
Learning Techniques ◽  
The Voice

Objectives. The current study presents a clinical evaluation of Vox4Health, an m-health system able to estimate the possible presence of a voice disorder by calculating and analyzing the main acoustic measures required for the acoustic analysis, namely, the Fundamental Frequency, jitter, shimmer, and Harmonic to Noise Ratio. The acoustic analysis is an objective, effective, and noninvasive tool used in clinical practice to perform a quantitative evaluation of voice quality. Materials and Methods. A clinical study was carried out in collaboration with medical staff of the University of Naples Federico II. 208 volunteers were recruited (mean age, 44.2 ± 13.9 years), 58 healthy subjects (mean age, 36.7 ± 13.3 years) and 150 pathological ones (mean age, 47 ± 13.1 years). The evaluation of Vox4Health was made in terms of classification performance, i.e., sensitivity, specificity, and accuracy, by using a rule-based algorithm that considers the most characteristic acoustic parameters to classify if the voice is healthy or pathological. The performance has been compared with that achieved by using Praat, one of the most commonly used tools in clinical practice. Results. Using a rule-based algorithm, the best accuracy in the detection of voice disorders, 72.6%, was obtained by using the jitter or shimmer value. Moreover, the best sensitivity is about 96% and it was always obtained by using jitter. Finally, the best specificity was achieved by using the Fundamental Frequency and it is equal to 56.9%. Additionally, in order to improve the classification accuracy of the next version of the Vox4Health app, an evaluation by using machine learning techniques was conducted. We performed some preliminary tests adopting different machine learning techniques able to classify the voice as healthy or pathological. The best accuracy (77.4%) was obtained by the Logistic Model Tree algorithm, while the best sensitivity (99.3%) was achieved using the Support Vector Machine. Finally, Instance-based Learning performed the best specificity (36.2%). Conclusions. Considering the achieved accuracy, Vox4Health has been considered by the medical experts as a “good screening tool” for the detection of voice disorders in its current version. However, this accuracy is improved when machine learning classifiers are considered rather than the rule-based algorithm.

scholarly journals Designing a Smart Mobile Health System for Ecological Frailty Assessment in Elderly

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 41 ◽  
Author(s):  
Francisco M. García-Moreno ◽  
Estefanía Rodríguez-García ◽  
María José Rodríguez-Fórtiz ◽  
José Luis Garrido ◽  
María Bermúdez-Edo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health System ◽  
Mobile Health ◽  
Wearable Sensors ◽  
Machine Learning Algorithms ◽  
Intelligence Analysis ◽  
Automatic Data ◽  
Frailty Assessment ◽  
Technical Validation ◽  
Smart Mobile

The increasing adoption of mobile computing technology in the health and social domains offers new possibilities, for instance, promoting active aging. Health deterioration in elderly people could be successfully assessed by monitoring activities of daily living (ADLs) through mobile technology. In particular, frailty affects several dimensions (physical, psychological, and social) of human functioning, which are required to perform instrumental ADLs (IADLs). Starting from the definition of a model, this paper proposes the design of an intelligent mobile health system to assess frailty in an ecological way: to automatize the frailty assessment through wearable sensors, unobtrusively in free-living environments, and using machine learning in order to reduce the traditional efforts of clinicians assessing frailty. It supports automatic data collection from sensors and artificial intelligence analysis during the performance of real IADLs by elderly. The proposed system uses mobile/wearable devices, follows a microservices software architecture, and implements machine learning algorithms. A technical validation of the proposal is shown.

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