Systematic View and Impact of Machine Learning in Healthcare Systems

2021 ◽  
pp. 23-40
Author(s):  
Neethu Narayanan ◽  
K. P. Arjun ◽  
N. M. Sreenarayanan ◽  
C. M. Deepa
Keyword(s):  
Machine Learning ◽  
Healthcare Systems

scholarly journals HealthTEC Innovation Design - a proposal for a novel Master degree program based on Unmet Clinical Need, global Healthcare Challenges, and 21st century skills

2020 ◽  
Vol 6 (3) ◽  
pp. 599-603
Author(s):  
Michael Friebe
Keyword(s):  
Machine Learning ◽  
21St Century ◽  
21St Century Skills ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Data Gathering ◽  
Healthcare Systems ◽  
Disruptive Innovation ◽  
Incremental Innovation ◽  
Healthcare Provision

AbstractThe effectiveness, efficiency, availability, agility, and equality of global healthcare systems are in question. The COVID-19 pandemic have further highlighted some of these issues and also shown that healthcare provision is in many parts of the world paternalistic, nimble, and often governed too extensively by revenue and profit motivations. The 4th industrial revolution - the machine learning age - with data gathering, analysis, optimisation, and delivery changes has not yet reached Healthcare / Health provision. We are still treating patients when they are sick rather then to use advanced sensors, data analytics, machine learning, genetic information, and other exponential technologies to prevent people from becoming patients or to help and support a clinicians decision. We are trying to optimise and improve traditional medicine (incremental innovation) rather than to use technologies to find new medical and clinical approaches (disruptive innovation). Education of future stakeholders from the clinical and from the technology side has not been updated to Health 4.0 demands and the needed 21st century skills. This paper presents a novel proposal for a university and innovation lab based interdisciplinary Master education of HealthTEC innovation designers.

Architecting IoT based Healthcare Systems Using Machine Learning Algorithms

2020 ◽  
pp. 40-66
Author(s):  
G. S. Karthick ◽  
P. B. Pankajavalli
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Healthcare Systems ◽  
Machine Learning Algorithms ◽  
Daily Activities ◽  
Sensor Data ◽  
Time Data ◽  
Sensory Data ◽  
Hybrid Classification ◽  
Smart Data

The rapid innovations in technologies endorsed the emergence of sensory equipment's connection to the Internet for acquiring data from the environment. The increased number of devices generates the enormous amount of sensor data from diversified applications of Internet of things (IoT). The generation of data may be a fast or real-time data stream which depends on the nature of applications. Applying analytics and intelligent processing over the data streams discovers the useful information and predicts the insights. Decision-making is a prominent process which makes the IoT paradigm qualified. This chapter provides an overview of architecting IoT-based healthcare systems with different machine learning algorithms. This chapter elaborates the smart data characteristics and design considerations for efficient adoption of machine learning algorithms into IoT applications. In addition, various existing and hybrid classification algorithms are applied to sensory data for identifying falls from other daily activities.

Healthy and wellbeing activities’ promotion using a Big Data approach

2016 ◽  
Vol 24 (2) ◽  
pp. 125-135 ◽  
Author(s):  
Diego Gachet Páez ◽  
Manuel de Buenaga Rodríguez ◽  
Enrique Puertas Sánz ◽  
María Teresa Villalba ◽  
Rafael Muñoz Gil
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Economic Crisis ◽  
Healthy Lifestyle ◽  
Developed Countries ◽  
Healthcare Systems ◽  
Machine Learning Algorithms ◽  
Public And Private ◽  
Private Healthcare

The aging population and economic crisis specially in developed countries have as a consequence the reduction in funds dedicated to health care; it is then desirable to optimize the costs of public and private healthcare systems, reducing the affluence of chronic and dependent people to care centers; promoting healthy lifestyle and activities can allow people to avoid chronic diseases as for example hypertension. In this article, we describe a system for promoting an active and healthy lifestyle for people and to recommend with guidelines and valuable information about their habits. The proposed system is being developed around the Big Data paradigm using bio-signal sensors and machine-learning algorithms for recommendations.

scholarly journals Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic

2021 ◽  
Vol 4 ◽  
Author(s):  
Jay Carriere ◽  
Hareem Shafi ◽  
Katelyn Brehon ◽  
Kiran Pohar Manhas ◽  
Katie Churchill ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Language Processing ◽  
Chronic Conditions ◽  
Healthcare Delivery ◽  
Occupational Therapists ◽  
Healthcare Systems ◽  
Research Development ◽  
Policy Makers ◽  
Neurological Conditions

The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.

Internet of Things in Healthcare

2020 ◽  
pp. 23-39
Author(s):  
Rajasekaran Thangaraj ◽  
Sivaramakrishnan Rajendar ◽  
Vidhya Kandasamy
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Deep Learning ◽  
Internet Of Things ◽  
Healthcare Systems ◽  
Wearable Devices ◽  
Healthcare Research ◽  
Learning Technologies ◽  
Learning Approaches ◽  
And Performance

Healthcare motoring has become a popular research in recent years. The evolution of electronic devices brings out numerous wearable devices that can be used for a variety of healthcare motoring systems. These devices measure the patient's health parameters and send them for further processing, where the acquired data is analyzed. The analysis provides the patients or their relatives with the medical support required or predictions based on the acquired data. Cloud computing, deep learning, and machine learning technologies play a prominent role in processing and analyzing the data respectively. This chapter aims to provide a detailed study of IoT-based healthcare systems, a variety of sensors used to measure parameters of health, and various deep learning and machine learning approaches introduced for the diagnosis of different diseases. The chapter also highlights the challenges, open issues, and performance considerations for future IoT-based healthcare research.

How to Improve Hospital Flows in the Context of the COVID Pandemic

2021 ◽  
Author(s):  
Paul-Eric Dossou ◽  
Luiza Foreste ◽  
Eric Misumi
Keyword(s):  
Machine Learning ◽  
Problem Solving ◽  
Literature Review ◽  
Lean Manufacturing ◽  
Industry 4.0 ◽  
New Technologies ◽  
Hybrid Approach ◽  
Real Data ◽  
Healthcare Systems ◽  
Improve Hospital

In healthcare systems, the adoption of logistics 4.0 main technologies in the processes flows is essential to avoid unnecessary movements and manual work performed by people who could be performing tasks that require logical reasoning. In the context of the COVID pandemic, the adoption of new technologies to replace people in manual processes had become even more usual. This paper aims to demonstrate through simulation, the opportunities of improvement with lean manufacturing concepts and industry 4.0 technologies the hospital flows. After describing the problem and the need of improvements in hospital logistics, a literature review with concepts of Industry 4.0, Lean Manufacturing, and Logistics 4.0 will be presented. The hybrid approach used in the development of a decision aid tool that combines real data and methods of machine learning and problem-solving will be then, an example will be given for illustrating the concepts and methods elaborated.

scholarly journals Simnovate: simulation, innovation and education for better healthcare

2017 ◽  
Vol 3 (Suppl 1) ◽  
pp. S1-S2 ◽  
Author(s):  
Rajesh Aggarwal
Keyword(s):  
Machine Learning ◽  
Medical Education ◽  
Patient Safety ◽  
Global Health ◽  
Visual Arts ◽  
Outcomes Research ◽  
Healthcare Systems ◽  
Medical Technologies ◽  
Healthcare Simulation ◽  
The World

Simulation has already transformed medical education, and holds the power to shape modern healthcare systems, communities and populations. Simnovate is a mission, a community and a partnership of passionate, driven and game-changing individuals, who wish to see the change we can make together, in the world, right now. Four domains were defined: patient safety, medical technologies, global health and pervasive learning, with domain group experts that span healthcare simulation, outcomes research, aviation, serious gaming, patient safety, economics, machine learning, biorobotics, implementation science, global health and the visual arts. Bringing together simulation, innovation and education, for better health and care.

scholarly journals A Survey on Recent Advances in Wearable Fall Detection Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Anita Ramachandran ◽  
Anupama Karuppiah
Keyword(s):  
Machine Learning ◽  
World Health Organization ◽  
Fall Detection ◽  
Healthcare Systems ◽  
World Health ◽  
The European Union ◽  
Human Beings ◽  
Average Life Expectancy ◽  
Detection Systems ◽  
Health Organization

With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

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