The Significance of Machine Learning in Healthcare Data Management (original) (raw)

Machine Learning in Healthcare Data Analysis: A Survey

Journal of Biology and Today`s World, 2019

In recent years, healthcare data analysis is becoming one of the most promising research areas. Healthcare includes data in various types such as clinical data, Omics data, and Sensor data. Clinical data includes electronic health records which store patient records collected during ongoing treatment. Omics data is one of the high dimensional data comprising genome, transcriptome and proteome data types. Sensor data is collected from various wearable and wireless sensor devices. To handle this raw data manually is very difficult. For analysis of data, machine learning is emerged as a significant tool. Machine learning uses various statistical techniques and advanced algorithms to predict the results of healthcare data more precisely. In machine learning different types of algorithms like supervised, unsupervised and reinforcement are used for analysis. In this paper, different types of machine learning algorithms are described. Then use of machine learning algorithms for analyzing v...

From EHRs to Insights: How Machine Learning is Transforming Healthcare Data Management

International Journal for Multidisciplinary Research (IJFMR), 2024

The care industry is in the middle of a transformation because of the adoption of EHRs and the integration of ML into the framework of healthcare. This article also has the intention of discussing how ML assists in changing the management of healthcare information and, as such, indicates how the raw EHR data can be utilized. Some of the traditional challenges associated with handling immensely large, diverse and geographically distributed healthcare data have been solved by employments of Maxims and the use of intelligence algorisms to encompass data manipulation, pattern recognition and Information content anticipation. First of all, they have not only used the new opportunities to provide better, improved and more efficient services to the patients but also in the area of cost-cutting and introduction of the system of personalized medicine. In this paper, the author explored the place that ML occupies in consideration of the management of healthcare data with reference to techniques such as supervised learning, unsupervised learning, NLP and deep learning. They are then presented with regard to uses such as patient risk assessment, clinical decision-making, and population health. Furthermore, the paper also reveals that the challenges of 'bringing' ML into healthcare include the issues of data privacy and ethical questions regarding the data governance efforts needed. The study also proceeds further to talk about the future of ML in healthcare with regard to predictive and precision medicine. Some of the other interdisciplinary integration of ML is a combination of the technology with other currently dominant technologies like blockchain or IoT, where the integration of these two with ML is demonstrated, and other possibilities in the management of healthcare data are explored. In support of the said arguments the article gives instances of cases of the effective application of ML in the health sector as well as giving out tables and figures. To that end, it is important to assert that more research should be done. More monetary investment is made in the development of ML technologies so that these concepts can be better implemented and optimized. These two observations can become more fully realized in their potential to revolutionize the ways in which healthcare information is managed by healthcare providers, technologists and policymakers in the future and now.

The Future of Health care: Machine Learning

International Journal of Engineering & Technology

Machine learning (ML) is a rising field. Machine learning is to find patterns automatically and reason about data.ML enables personalized care called precision medicine. Machine learning methods have made advances in healthcare domain. This paper discuss about application of machine learning in health care. Machine learning will change health care within a few years. In future ML and AI will transform health care, but quality ML and AI decision support systems (DSS) Should Require to address the problems faced by patients and physicians in effective diagnosis.

Machine learning and artificial intelligence: applications in healthcare epidemiology

Antimicrobial Stewardship & Healthcare Epidemiology, 2021

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing powe...

Systematic Review of the Literature on Machine Learning Techniques Employed in Real-World Data Analysis for Patient-Provider Decision Making

IJRASET, 2021

The Industrial Revolution 4.0 has flooded the virtual world with data, which includes Internet of Things (IoT) data, mobile data, cybersecurity data, business data, social networks, including health data. To analyse this data efficiently and create related efficient and streamlined applications, expertise in artificial intelligence specifically machine learning (ML), is required. This field makes use of a variety of machine learning methods, including supervised, unsupervised, semi-supervised, and reinforcement. Additionally, deep learning, which is a subset of a larger range of machine learning techniques, is capable of effectively analysing vast amounts of data. Machine learning is a broad term that encompasses a number of methods used to extract information from data. These methods may allow the rapid translation of massive real-world information into applications that assist patients and providers in making decisions. The objective of this literature review was to find observational studies that utilised machine learning to enhance patient-provider decision-making utilising secondary data.

Burgeoning of Machine Learning in the field of Medical & Health Sciences

2021

In recent years, there has been a significant improvement in medical science and its related equipment, especially to diagnose a particular disease in the early stage. Use of technology helps in early diagnoses which leads to early treatment and recovery from disease. If the person does not receive proper treatment in accordance to the diagnosis the disease might get worse which results in increase morbidity and mortality rate. In short, early diagnoses and right treatment is the best remedy against any particular disease. Due to this fact, there is a need to analyze complex medical data, medical reports and medical images that could provide mechanisms to help the health care professional with more precision. In the field of medical science there is a need to devise standardized mechanism to analyze complex medical data. Introduction of machine learning and artificial intelligence provide ease to examine medical reports and images that aids healthcare professionals with greater accu...

Exploring the Possibilities of Using Machine Learning in Health Care

2021

In current days, professional trying to use machine learning in finding solutions of problems from almost every domain. The proposed study is conducted to check the feasibility of using machine learning in health care domain. We introduce the core concepts of machine learning with available types of algorithms. The study also consider the challenges that may encounter while integrating machine learning with clinical processes. A part of the study focusses on some of recent works on using machine learning as a solution of some health care problems. At the end, we discusses the future possibilities of machine learning in health care domain.

Machine Learning in Healthcare Industry: Tools and Techniques

From last two decades, technology evolving very quickly and changed the view nearly all aspects of modern life. There is a lot of development made in the area of machine learning. It is the subset of Artificial Intelligence that gives machine the possibility to determine and make decision without using explicit instructions. Today, ML has a tremendous impact in across all over the world and benefited various areas like medical diagnostics, fraud detection, driverless vehicles and security surveillance etc. Machine learning layout an inspiring set of technologies that consist of pragmatic tools for reviewing data and making predictions with the latest headway in artificial intelligence. The major theme of the paper is focused on the various tools and techniques of machine learning and its application in healthcare industry.

Effect of Machine Learning in Healthcare Industry with reference to Artificial Intelligence

International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2019

The Healthcare industry has seen immense amount of growth in recent times. It can be either in introducing new technology to detect diseases or new and efficient ways of treating a disease. Although the developments in this field differs, one parameter can be found in common viz. data. This data in healthcare typically is heterogenous, diverse, redundant and incomplete. The rapidly growing field of bigdata analytics has helped in understanding and improving the healthcare system. This opportunity of the majority of data being present in the healthcare sector can be tapped using both prescriptive and predictive analytics. Implementation of these areas of study can give us more insights on the industry, and understand its needs. This will also aid us in improving the steps that are carried out while processing the query of a patient.