Decision Making with Machine Learning in Our Modern, Data-Rich Health-Care Industry

2017 ◽  
pp. 277-289 ◽  
Author(s):  
Nick Dadson ◽  
Lisa Pinheiro ◽  
Jimmy Royer
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Decision Making ◽  
Health Care Industry ◽  
Care Industry

scholarly journals Machine learning based outlier detection for medical data

2021 ◽  
Vol 24 (1) ◽  
pp. 564
Author(s):  
R. Vijaya Kumar Reddy ◽  
Shaik Subhani ◽  
B. Srinivasa Rao ◽  
N. Lakshmipathi Anantha
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Outlier Detection ◽  
Health Care Industry ◽  
Work Load ◽  
Medical Data ◽  
The Novel ◽  
Cleaning Process ◽  
Care Industry ◽  
Global Data

<p>The concept of machine learning generate best results in health care data, it also reduce the work load of health care industry. This algorithm potentially overcome the issues and find out the novel knowledge for development of medical date in health care industry. In this paper propose a new algorithm for finding the outliers using different datasets. Considering that medical data are analytic of mutually health problems and an activity. The proposed algorithm is working based on supervised and unsupervised learning. This algorithm detects the outliers in medical data. The effectiveness of local and global data factor for outlier detection for medical data in real time. Whatever, the model used in this scenario from their training and testing of medical data. The cleaning process based on the complete attributes of dataset of similarity operations. Experiments are conducted in built in various medical datasets. The statistical outcome describe that the machine learning based outlier finding algorithm given that best accurateness.</p>

scholarly journals Machine Learning Approaches over Big Data for Health Care Systems: A review

2021 ◽  
pp. 191-198
Author(s):  
Pracheta J. Raut ◽  
Prof. Avantika Mahadik
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Big Data ◽  
Ensemble Learning ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Digital Data ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Care Industry

Today the digital data that world produces is unseen and spectacular. The data from social media, e-commerce and Internet of things generate approximately 2.5 quintillion of bytes per day. This amount is equals 100 million Blu-ray discs or almost 30,000 GB per second. Till today data is growing and will continue to grow in future. In the field of Health care industry, big data has opened new ways to acquire intelligence and data analysis. Collected records from patient, hospital, doctors, medical treatment is known as health care big data. Big data by machine learning are assembled and evaluates the large amount of data in health care. Analytic process and business intelligence (BI) is growing up day by day, as it acquires knowledge and makes right decision. As it is vast and complex growing data, it is very difficult to store. The tradition method of handling big data is incapable to manage and process big data. Hence to resolve this difficulty, some machine learning tools are applied on large amount of data using big data analytics framework. Researchers have proposed some machine learning approaches to improve the accuracy of analytics. Each technique is applied, and their results are compared. And this concluded that we get accurate result from one machine learning approach are called as Ensemble Learning. The final result observed that ensemble learning can obtain high accuracy. In this paper we shall study about various methods to process big data for machine learning and its statistic approaches. Further we study various tools for storing of big data, its advantages, and disadvantages in the field of health care industry.

scholarly journals Measuring Service Quality For Strategic Planning And Analysis In Service Firms

2011 ◽  
Vol 10 (4) ◽  
pp. 24 ◽  
Author(s):  
Masoud Jemmasi ◽  
Kelly C. Strong ◽  
Steven A. Taylor
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Performance Analysis ◽  
Service Quality ◽  
Health Care Industry ◽  
Strategic Decision ◽  
Management Tool ◽  
Strategic Decision Making ◽  
Service Firms ◽  
Care Industry

<span>The following study demonstrates that service quality assessment using importance-performance analysis may be a more useful strategic management tool than the gap measures recommended by the authors of the SERVQUAL scale. An example from the health care industry highlights the utility of the proposed method for purposes of strategic decision making.</span>

scholarly journals Artificial Intelligence Can Improve the Healthcare System

2019 ◽  
pp. 37-39
Author(s):  
Anchana Kuganesan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Health Care Industry ◽  
Data Interpretation ◽  
Care Patient ◽  
Physical Aspect ◽  
Preventative Care ◽  
Care Industry ◽  
Physical Components

Artificial intelligence (AI) is a computer system used to model human cognitive functions, intelligence, and behaviour. Components include both, a virtual and a physical aspect. Virtual aspects of AI include algorithms and neural networks instilled within the system to execute its assignments. Physical components include the entity in conjunction with a code. 1 AI is currently being developed by Nvidia Corporation, Alphabet, Twilio, Amazon, Micron Technology, Microsoft Corp., Baidu, Intel Corp., Facebook, and Tencent. 2 Expanding AI into the health care system can be beneficial for preventative care, patient safety, and reducing treatment costs for families. AI has proven to be useful in machine learning, thus, it can be programmed to complete specific tasks. By performing tasks such as data interpretation, the amount of time that it takes for a physician to consult patients regarding their results will be reduced. In addition, AI is capable of analyzing medical images to identify tumours and it has previously been used in various other branches of medicine such as neurology and cardiology. Overall, AI has great potential to improve the health care industry in North America and worldwide. However, potential violations while utilizing personal patient data must be addressed whilst modifying this technology.

Bringing an entrepreneurial mindset to health-care: a new tool for better outcomes

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Laurence Murray Gillin ◽  
Lois Marjorie Hazelton
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Professional Practice ◽  
Health Care Industry ◽  
Care Staff ◽  
Health Care Staff ◽  
Content Type ◽  
Care Industry ◽  
Entrepreneurial Behaviour

Purpose The purpose of this paper is to consider the value of an industry ecosystem in providing context for both identifying and evaluating organisation opportunities and related entrepreneurial behaviour for future strategic growth by reference to a case study in the health-care industry. Using a validated entrepreneurship mindset audit instrument, an assessment is made of the leadership, decision-making, behaviour and awareness dimensions of professional practice health-care staff to create the internal culture that fosters an entrepreneurial orientated organisation that can deliver effective innovation for satisfied users of health-care services. Design/methodology/approach This case study examines the distinctive dimensions of entrepreneurial mindset – leadership, decision-making, behaviour and awareness – within a practice-based health-care (nursing) ecosystem and how these dimensions impact organisation performance throughout the health-care industry. Findings This study validates research findings that entrepreneurial leadership encourages entrepreneurial behaviour and an entrepreneurial culture supports the development of innovations. Opportunities for such cultural behaviour are best understood by measuring the staff’s and leaders’ “entrepreneurial mindset”. Research limitations/implications Generalising results from this case study requires caution. The positive outcome from the professional practice examples, and their strong association with impactful entrepreneurial mindset values on service delivery, requires further evaluation. Practical implications Using an entrepreneurial mindset audit to assess organisation’s cultural behaviour enables management to identify factors fostering or inhibiting entrepreneurial activity and to devise interventions to improve strategic direction. Originality/value Entrepreneurial mindset is not a new concept, but adding the critical significance of spiritual awareness to creative entrepreneur behaviour, together with a visioning map, adds both value and understanding to enhance organisation performance.

scholarly journals Effective Heart Disease Prediction Model Through Voting Technique

2020 ◽  
Vol 4 (5) ◽  
pp. 10-13
Author(s):  
Usha Sri B
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Heart Disease ◽  
Good Accuracy ◽  
Health Care Industry ◽  
Ensemble Classification ◽  
Effective Technique ◽  
Practical Applications ◽  
Care Industry ◽  
Common Application

Machine learning has various practical applications that solves many issues relating to various domains .One among such domain is the health care domain and the most common application of machine learning is the prediction of an outcome based upon existing data in health care industry. Machine learning is shown as an effective technique in assisting the health care industry to make intelligent and effective decisions. The model tries to learn pattern from the existing dataset and later on it is applied to the unknown dataset for effectively predicting the outcome. Classification is the most effective technique for prediction of outcome. There are many classification algorithms which are used for prediction but only few algorithms predict with good accuracy whereas remaining algorithms predict with less accuracy. So to improve the accuracy of weak algorithms this paper presented a new method called ensemble classification ,where the accuracy is enhanced by combining multiple classifiers and later prediction is done by voting technique. So, experiments were done on a heart disease dataset, through ensemble approach the accuracy was enhanced and along with that a GUI was developed where the user himself can check whether he has probability of getting heart disease or not. The results of the study showed that ensemble method such as voting technique played a key role in improving the accuracy prediction of weak classifiers and also identified risk factors for occurrence of heart disease. An accuracy of 90% was achieved with voting technique and the performance of the process was further enhanced with a feature selection implementation, and the results showed significant improvement in prediction accuracy.

A Study On Performance Appraisal of Employees in Health Care Industry in a Private Multi-Speciality Organization

2011 ◽  
Vol 1 (9) ◽  
pp. 125-126
Author(s):  
Dr. C. Swarnalatha Dr. C. Swarnalatha ◽  
◽  
T.S. Prasanna T.S. Prasanna
Keyword(s):  
Health Care ◽  
Performance Appraisal ◽  
Health Care Industry ◽  
Care Industry

Canadian legislatures and the regulation of the private health-care industry

2018 ◽  
Vol 68 (2) ◽  
pp. 231-258
Author(s):  
Marie-Claude Prémont ◽  
Cory Verbauwhede
Keyword(s):  
Health Care ◽  
Health Care Industry ◽  
Private Health Care ◽  
Care Industry

scholarly journals Exploring the Social Networks’ Use in the Health-Care Industry: A Multi-Level Analysis

2021 ◽  
Vol 18 (14) ◽  
pp. 7295
Author(s):  
Tommasina Pianese ◽  
Patrizia Belfiore
Keyword(s):  
Health Care ◽  
Social Networks ◽  
Health Care Providers ◽  
Health Care Industry ◽  
Future Research ◽  
Great Level ◽  
Care Providers ◽  
Care Industry ◽  
Multi Level ◽  
Video And Audio

The application of social networks in the health domain has become increasingly prevalent. They are web-based technologies which bring together a group of people and health-care providers having in common health-related interests, who share text, image, video and audio contents and interact with each other. This explains the increasing amount of attention paid to this topic by researchers who have investigated a variety of issues dealing with the specific applications in the health-care industry. The aim of this study is to systematize this fragmented body of literature, and provide a comprehensive and multi-level overview of the studies that has been carried out to date on social network uses in healthcare, taking into account the great level of diversity that characterizes this industry. To this end, we conduct a scoping review enabling to identify the major research streams, whose aggregate knowledge are discussed according to three levels of analysis that reflect the viewpoints of the major actors using social networks for health-care purposes, i.e., governments, health-care providers (including health-care organizations and professionals) and social networks’ users (including ill patients and general public). We conclude by proposing directions for future research.

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