Deep Learning-based Smart IoT Health System for Blindness Detection using Retina Images

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.

Download Full-text

Multi Deep Learning to Diagnose COVID-19 in Lung X-Ray Images with Majority Vote Technique

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2020.1231.49 ◽

2020 ◽

Vol 13 (6) ◽

pp. 560-568

Author(s):

Ike Fibriani ◽

◽

Widjonarko Widjonarko ◽

Aris Prasetyo ◽

Angga Raharjo ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Health System ◽

Majority Vote ◽

Learning System ◽

Final Decision ◽

Initial Screening ◽

X Ray ◽

Rate Of Spread ◽

Proposed Model

The COVID-19 pandemic has become the focus of world problems that need to be resolved. This is because the rate of spread is speedy and able to take down the world's health system. Therefore, many researchers are focusing their research on solving this problem by doing an initial screening on the X-Ray image of the subject's lungs. One of them is by using Deep Learning. Several articles that talk about implemented Deep Learning for classifying X-Ray images have been published. But most of them are comparing different architecture CNN (Convolutional Neural Network). In this study, the authors try to create a multi-classifier Deep Learning system that consists of nine different CNN architectures and combined with three different Majority Vote techniques. The target of this research is to maximize the performance of classification and to minimize errors because the final decision is a compilation of decisions contained in each CNN architecture. Several models of CNN are tested in this study, both the model which used Majority Vote and Conventional CNN. The results show that the proposed model achieves an accuracy value average F1-Score 0.992 and Accuracy 0.993, according to 5 K-Fold test. The best model is CNN, which used Soft Majority Vote.

Download Full-text

Statistical and Deep Learning Models for Forecasting Drug Distribution in the Brazilian Public Health System

2019 8th Brazilian Conference on Intelligent Systems (BRACIS) ◽

10.1109/bracis.2019.00130 ◽

2019 ◽

Author(s):

Renan M. Sousa ◽

Skander Hannachi ◽

Guilherme N. Ramos

Keyword(s):

Public Health ◽

Deep Learning ◽

Health System ◽

Public Health System ◽

Drug Distribution ◽

Learning Models

Download Full-text

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

10.2196/preprints.15182 ◽

2019 ◽

Cited By ~ 1

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.

Download Full-text

A decision support healthcare system based on IoT and neural network technique

Journal of Engineering Design and Technology ◽

10.1108/jedt-08-2020-0317 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Khadeja Al_Sayed Fahmy ◽

Ahmed Yahya ◽

M. Zorkany

Keyword(s):

Neural Network ◽

Health Care ◽

Deep Learning ◽

Decision Support ◽

Health System ◽

Health Care System ◽

Virus Disease ◽

Activation Function ◽

Content Type ◽

Care System

Purpose The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital. Design/methodology/approach AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN. Findings Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN. Originality/value AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.

Download Full-text

A Deep Learning-Based Text Classification of Adverse Nursing Events

Journal of Healthcare Engineering ◽

10.1155/2021/9800114 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Wenjing Lu ◽

Wei Jiang ◽

Na Zhang ◽

Feng Xue

Keyword(s):

Deep Learning ◽

Health System ◽

Medical Care ◽

Text Classification ◽

Rapid Development ◽

Classification Model ◽

Diagnosis And Treatment ◽

Event Analysis ◽

Classification Methods

Adverse nursing events occur suddenly, unpredictably, or unexpectedly during course of clinical diagnosis and treatment processes in the hospitals. These events adversely affect the patient’s diagnosis and treatment results and even increase the patient’s pain and burden. Additionally, It is high likely to cause accidents and disputes and affect normal medical work and personnel safety and is not conducive to the development of the health system. Due to the rapid development of modern medicine, health and safety of patients have become the most concerned issue in society and patient safety is an important part of medical care management. Research and events have shown that classified management of adverse nursing events, event analysis, and improvement measures are beneficial, specifically to the health system, to continuously improve the quality of medical care and reduce the occurrence of adverse nursing events. In the management of adverse nursing events, it is very important to categorize the text reports of adverse nursing events and divide these into different categories and levels. Traditional reports of adverse nursing events are mostly unstructured and simple data, often relying on manual classification, which is difficult to analyze. Furthermore, data is relatively inaccurate and practical reference significance is not obvious. In this paper, we have extensively evaluated various deep learning-based classification methods which are specifically designed for the healthcare systems. It becomes possible with the development of science and technology; text classification methods based on deep learning are gradually entering people’s field of vision. Additionally, we have proposed a text classification model for adverse nursing events in the health system. Experiments and data comparison test of both the proposed deep learning-based method and existing methods in the text classification of nursing adverse events effect are performed. These results show the exceptional performance of the proposed mechanism in terms of various evaluation metrics.

Download Full-text