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.

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.

Advances of flexible pressure sensors toward artificial intelligence and health care applications

2015 ◽  
Vol 2 (2) ◽  
pp. 140-156 ◽  
Author(s):  
Yaping Zang ◽  
Fengjiao Zhang ◽  
Chong-an Di ◽  
Daoben Zhu
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Electronic Devices ◽  
Pressure Sensors ◽  
Health Care Industry ◽  
Organic Electronic Devices ◽  
Organic Electronic ◽  
Care Industry ◽  
Health Care Applications ◽  
Flexible Pressure Sensors

Incorporating flexible pressure sensors with organic electronic devices allows their promising applications in artificial intelligence and the health care industry.

Total Quality in Information Systems Management: Issues for the Health Care Industry

1998 ◽  
Vol 37 (02) ◽  
pp. 156-160
Author(s):  
K. J. Leonard
Keyword(s):  
Health Care ◽  
Health Care Industry ◽  
Relevant Information ◽  
Care Patient ◽  
Total Quality ◽  
Systems Management ◽  
Information Systems Management ◽  
Care Industry ◽  
Efficiency And Effectiveness ◽  
Management Issues

AbstractFaced with rising costs, growing demand and declining funding, hospitals and others must either cut services or improve the efficiency and effectiveness of what they do. Neither solution can be implemented without adequate relevant information. Without understanding which services are providing the most value to its customers, sensible cutbacks will be difficult to make. Improving efficiency requires a knowledge of where there are inefficiencies, and improving effectiveness requires an understanding of what the outcomes of health care are. The solution, as many have documented, is to create, as a first step, a database containing detailed health care patient data. In this paper, we present continuous improvement techniques as a requirement for the design and development ofthis much needed database.

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.

Freedom of conscience: a benefit to health care worker and patient alike

2018 ◽  
Vol 11 (2) ◽  
pp. 144-152
Author(s):  
Roger Kiska
Keyword(s):  
Health Care ◽  
Health Care Industry ◽  
Care Patient ◽  
Freedom Of Conscience ◽  
Philosophical Debate ◽  
Content Type ◽  
Policy Practice ◽  
Care Industry ◽  
The Right ◽  
Care Worker

Purpose The purpose of this paper is to determine the appropriate legal balance and framework whereby issues of health care, patient access and rights of conscience can be best accommodated. Design/methodology/approach A review of existing case law, statutes and conscience clauses as applied to the philosophical debate surrounding conscience in health care. Findings Freedom of conscience is strongly anchored in British law and policy. Practice within the health care industry, however, has been slow and resistant to rights of conscience. Respecting the right of health care workers to exercise that right, benefits the health care industry at large, and patients themselves. Originality/value This debate, particularly since the so-called “Scottish mid-wives case” and the recent General Pharmaceutical Council consultation on religion and personal values, has come to the forefront of bio-ethical discourse in recent months. As such, this treatment provides a valuable legal tool to answering the various positions involved in the debate.

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