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.

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

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

2020 ◽  
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. CLINICALTRIAL INTERNATIONAL REGISTERED REPORT RR2-10.2196/resprot.5039

Dancing With Death in the Dust of Coronavirus: The Lived Experience of Iranian Nurses

2020 ◽  
Vol 26 (4) ◽  
pp. e82-e89
Author(s):  
Fatemeh Bahramnezhad ◽  
Parvaneh Asgari
Keyword(s):  
Health Care ◽  
Lived Experience ◽  
Health Care Providers ◽  
Health Care Systems ◽  
Phenomenological Approach ◽  
Care Providers ◽  
Care Systems ◽  
Interpretative Phenomenological Approach ◽  
Communication Challenges ◽  
Novel Coronavirus

The novel coronavirus disease (COVID-19) pandemic as a public health emergency poses dramatic challenges for health-care systems. The experiences of health-care workers are important in planning for future outbreaks of infectious diseases. This study explored the lived experiences of 14 nurses in Tehran, Iran caring for coronavirus patients using an interpretative phenomenological approach as described by Van Manen. In-depth interviews were audio-recorded between March 10 and May 5, 2020. The essence of the nurses' experiences caring for patients with COVID-19 was categorized as three themes and eight subthemes: (a) Strong pressure because of coronavirus: initial fear, loneliness, communication challenges, exhaustion. (b) Turn threats into opportunities: improvement of nursing image, professional development. (c) Nurses' expectations: expectations of people, expectations of government. The findings of this study showed that identifying the challenges and needs of health-care providers is necessary to create a safe health-care system and to prepare nurses and expand their knowledge and attitudes to care for patients in new crises in the future.

scholarly journals Social distancing and dangers of access block to health care services during COVID-19 pandemic

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Mor Saban ◽  
Tal Shachar
Keyword(s):  
Health Care ◽  
Health Care Services ◽  
Health Care Systems ◽  
Chronically Ill ◽  
Access To Health Services ◽  
Isolated Populations ◽  
Social Distancing ◽  
Care Services ◽  
Care Systems ◽  
Novel Coronavirus

An outbreak of the novel coronavirus (COVID-19) that started in Wuhan, China, has spread quickly, with cases confirmed in 180 countries with broad impact on all health care systems. Currently, the absence of a COVID-19 vaccine or any definitive medication has led to increased use of non-pharmaceutical interventions, aimed at reducing contact rates in the population and thereby transmission of the virus, especially social distancing. These social distancing guidelines indirectly create two isolated populations at high-risk: the chronically ill and voluntary isolated persons who had contact with a verified patient or person returning from abroad. In this concept paper we describe the potential risk of these populations leading to an 80% reduction in total Emergency Department (ED) visits, including patients with an acute condition. In conclusion, alternative medical examination solutions so far do not provide adequate response to the at-risk population. The healthcare system must develop and offer complementary solutions that will enable access to health services even during these difficult times.

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

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 Diagnostics for COVID-19: A case for field-deployable, rapid molecular tests for community surveillance

2020 ◽  
Vol 54 (4s) ◽  
pp. 71-76
Author(s):  
Michael Frimpong ◽  
Yaw A. Amoako ◽  
Kwadwo B. Anim ◽  
Hubert S. Ahor ◽  
Richmond Yeboah ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Systems ◽  
Absolute Number ◽  
Contact Tracing ◽  
Infected People ◽  
Delay In Diagnosis ◽  
Slowing Down ◽  
Molecular Tests ◽  
Care Systems ◽  
Novel Coronavirus

Across the globe, the outbreak of the COVID-19 pandemic is causing distress with governments doing everything in their power to contain the spread of the novel coronavirus (SARS-CoV-2) to prevent morbidity and mortality. Actions are being implemented to keep health care systems from being overstretched and to curb the outbreak. Any policy responses aimed at slowing down the spread of the virus and mitigating its immediate effects on health care systems require a firm basis of information about the absolute number of currently infected people, growth rates, and locations/hotspots of infections. The only way to obtain this base of information is by conducting numerous tests in a targeted way. Currently, in Ghana, there is a centralized testing approach, that takes 4-5 days for samples to be shipped and tested at central reference laboratories with results communicated to the district, regional and nationalstakeholders. This delay in diagnosis increases the risk of ongoing transmission in communities and vulnerable institutions. We have validated, evaluated and deployed an innovative diagnostic tool on a mobile laboratory platform to accelerate the COVID-19 testing. A preliminary result of 74 samples from COVID-19 suspected cases has a positivity rate of 12% with a turn-around time of fewer than 3 hours from sample taking to reporting of results, significantly reducing the waiting time from days to hours, enabling expedient response by the health system for contact tracing to reduce transmission and additionally improving case management.

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