Machine Learning and Artificial Intelligence Techniques in Smart Health Care Systems

2021 ◽  
pp. 205-217
Author(s):  
K. Padmavathi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Health Care Systems ◽  
Artificial Intelligence Techniques ◽  
Care Systems ◽  
Smart Health

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

Machine Learning-Based Smart Health-care Systems

2021 ◽  
pp. 195-208
Author(s):  
Anil Kumar Swain ◽  
Bunil Kumar Balabantaray ◽  
Jitendra Kumar Rout
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Systems ◽  
Care Systems ◽  
Smart Health

Smart Health Care Chatbot for Prognosis of Treatments and Disease Diagnosis Using Machine Learning

2020 ◽  
Vol 17 (9) ◽  
pp. 3947-3951
Author(s):  
R. Vineeth ◽  
R. Rithish ◽  
D. V. S. N. Sai Varma ◽  
B. V. Ajay Prakash
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Health Professionals ◽  
Disease Diagnosis ◽  
Artificial Intelligence Techniques ◽  
Text Input ◽  
Smart Health ◽  
User Friendly ◽  
Present World

In this present world there are various diseases for which treatments and remedies are available abundantly. It is impossible for human to remember all the precautions and remedies to cure the disease. There is no relevant platform that could exhibit all the diseases and their respective remedies. Health professionals are not always available to users on all the time. Hence, the necessity of health care Chatbot plays a major role in this current world. In the proposed idea, we have created a HealthCare Chatbot with Artificial Intelligence techniques which can process the text input and predict the diseases associated with the symptoms given by the user. The HealthCare Chatbot implemented here is a user friendly platform which predicts the probable diseases and the home remedies, we can imply to cure based on the symptoms observed by the user in their knowledge.

scholarly journals Artificial Intelligence in the Fight Against COVID-19 in the Banking Sector

2020 ◽  
Vol 1 (1) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
High Risk ◽  
Health Care System ◽  
Banking Sector ◽  
Health Care Systems ◽  
Use Cases ◽  
Care Systems ◽  
Human Thinking

The COVID-19 pandemic has been causing a massive strain in different sectors around the globe, especially in the health care systems in many countries. Artificial Intelligence has found its way in the health care system in helping to find a cure or vaccine by screening out medicines that could be promising for cure. Not only that but by containing the virus and predicting highly effected areas and limiting the spread of the virus. Many use cases based on AI was successful to monitor the spread and lock areas that were predicted by AI algorithms to be at high risk. Broadly speaking, AI involves ‘the ability of machines to emulate human thinking, reasoning and decision - making.

scholarly journals Clinical Predictive Models for COVID-19: Systematic Study (Preprint)

2020 ◽  
Author(s):  
Patrick Schwab ◽  
August DuMont Schütte ◽  
Benedikt Dietz ◽  
Stefan Bauer
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Intensive Care ◽  
Clinical Data ◽  
Predictive Models ◽  
Health Care Systems ◽  
Gradient Boosting ◽  
Support Vector ◽  
Care Systems ◽  
To Receive

BACKGROUND COVID-19 is a rapidly emerging respiratory disease caused by SARS-CoV-2. Due to the rapid human-to-human transmission of SARS-CoV-2, many health care systems are at risk of exceeding their health care capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds, and mechanical ventilators. Predictive algorithms could potentially ease the strain on health care systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalized, or admitted to the ICU. OBJECTIVE The aim of this study is to develop, study, and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test or require hospitalization or intensive care. METHODS Using a systematic approach to model development and optimization, we trained and compared various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we performed a retrospective evaluation on demographic, clinical, and blood analysis data from a cohort of 5644 patients. In addition, we determined which clinical features were predictive to what degree for each of the aforementioned clinical tasks using causal explanations. RESULTS Our experimental results indicate that our predictive models identified patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% CI 67%-81%) and a specificity of 49% (95% CI 46%-51%), patients who are SARS-CoV-2 positive that require hospitalization with 0.92 area under the receiver operator characteristic curve (AUC; 95% CI 0.81-0.98), and patients who are SARS-CoV-2 positive that require critical care with 0.98 AUC (95% CI 0.95-1.00). CONCLUSIONS Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19 and, therefore, help inform care and prioritize resources.

scholarly journals Indexing and Optimization Techniques in Biomedical Industry

2019 ◽  
Vol 8 (6) ◽  
pp. 3484-3487
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Big Data ◽  
Health Care Systems ◽  
Optimization Techniques ◽  
Smart Phones ◽  
Health Care Information ◽  
Quality Health Care ◽  
Care Systems ◽  
Care Information

The unpredictable amount of data generated everyday by smart phones, social networks, health care systems etc. is really mind blowing. Smart phones alone generate 335exabytes of data ineveryyear that is really big data.Thus, the storage industry is facing several challenges in providing high magnitude of storage and retrieval devices at lowest costs which help to fulfill the requirements of big data and even technologies like de-duplication on storage devices are also becoming very important. Similarly, in recent days storing and retrieving the health care information in biomedical area is also becoming a great challenge in providing the best optimum data because of its huge amount of biomedical datasets. In order to achieve efficiency in providing highest quality health care information, an optimized index scheme is needed for big data which is based on accuracy and timelines. The existing indexing and optimization solutions are not enough to meet the emerging grow of index size and seek time. The objective of this paper is to identify better indexing solutions by investigating the basic big data requirements on indexing and optimization. This also includes a comparative study of various indexing and optimization techniques along with a taxonomy which contains Artificial Intelligence (AI) and Non Artificial Intelligence (NAI) based indexing techniques, optimization enhancement techniques which improves the performance efficiency of big data health care informatics.

scholarly journals Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis

10.2196/24153 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e24153
Author(s):  
Gang Luo ◽  
Claudia L Nau ◽  
William W Crawford ◽  
Michael Schatz ◽  
Robert S Zeiger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Systems ◽  
Asthma Management ◽  
Secondary Analysis ◽  
Emergency Department Visits ◽  
Learning Approaches ◽  
Care Systems ◽  
Hospital Visits ◽  
Intermountain Healthcare

Background Asthma exerts a substantial burden on patients and health care systems. To facilitate preventive care for asthma management and improve patient outcomes, we recently developed two machine learning models, one on Intermountain Healthcare data and the other on Kaiser Permanente Southern California (KPSC) data, to forecast asthma-related hospital visits, including emergency department visits and hospitalizations, in the succeeding 12 months among patients with asthma. As is typical for machine learning approaches, these two models do not explain their forecasting results. To address the interpretability issue of black-box models, we designed an automatic method to offer rule format explanations for the forecasting results of any machine learning model on imbalanced tabular data and to suggest customized interventions with no accuracy loss. Our method worked well for explaining the forecasting results of our Intermountain Healthcare model, but its generalizability to other health care systems remains unknown. Objective The objective of this study is to evaluate the generalizability of our automatic explanation method to KPSC for forecasting asthma-related hospital visits. Methods Through a secondary analysis of 987,506 data instances from 2012 to 2017 at KPSC, we used our method to explain the forecasting results of our KPSC model and to suggest customized interventions. The patient cohort covered a random sample of 70% of patients with asthma who had a KPSC health plan for any period between 2015 and 2018. Results Our method explained the forecasting results for 97.57% (2204/2259) of the patients with asthma who were correctly forecasted to undergo asthma-related hospital visits in the succeeding 12 months. Conclusions For forecasting asthma-related hospital visits, our automatic explanation method exhibited an acceptable generalizability to KPSC. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039

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