Navigating the Intersection of Machine Learning and Healthcare: A Review of Current Applications (original) (raw)
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Descriptive Analysis of Machine Learning and Its Application in Healthcare
International Journal of Computer Science Trends and Technology, 2020
The dynamic world of big data in the healthcare sector characterized by huge numbers, complexity, and speeds is also not suited to conventional research methods. Methods are especially required that can efficiently estimate models across comprehensive datasets of medical usage data, clinical data, personal computer data, and many other sources. While these data sets are quite large, they may also be very sparse (e.g., system data may only be accessible for a small subset of people), creating difficulties with conventional regression models. Most machine learning approaches successfully overcome these limitations but still are subject to the standard triggers of partiality that are typical in observatory studies. The models should be tested by standard design tests for researchers using machine learning techniques like a lasso or ridge regression.
Machine Learning in Healthcare, Introduction and Real World Application Considerations
International Journal of Reliable and Quality E-Healthcare, 2018
Machine Learning, closely related to Artificial Intelligence and standing at the intersection of Computer Science and Mathematical Statistical Theory, comes in handy when the truth is hiding in a place that the human brain has no access to. Given any prediction or assessment problem, the more complicated this issue is, based on the difficulty of the human mind to understand the inherent causalities/patterns and apply conventional methods towards an acceptable solution, Machine Learning can find a fertile field of application. This article's purpose is to give a general non-technical definition of Machine Learning, provide a review of its latest implementations in the Healthcare domain and add to the ongoing discussion on this subject. It suggests the active involvement of entities beyond the already active academic community in the quest for solutions that “exploit” existing datasets and can be applied in the daily practice, embedded inside the software processes that are already in use.
Trends and Focus of Machine Learning Applications for Health Research
JAMA Network Open
IMPORTANCE The use of machine learning applications related to health is rapidly increasing and may have the potential to profoundly affect the field of health care. OBJECTIVE To analyze submissions to a popular machine learning for health venue to assess the current state of research, including areas of methodologic and clinical focus, limitations, and underexplored areas. DESIGN, SETTING, AND PARTICIPANTS In this data-driven qualitative analysis, 166 accepted manuscript submissions to the Third Annual Machine Learning for Health workshop at the 32nd Conference on Neural Information Processing Systems on December 8, 2018, were analyzed to understand research focus, progress, and trends. Experts reviewed each submission against a rubric to identify key data points, statistical modeling and analysis of submitting authors was performed, and research topics were quantitatively modeled. Finally, an iterative discussion of topics common in submissions and invited speakers at the workshop was held to identify key trends. MAIN OUTCOMES AND MEASURES Frequency and statistical measures of methods, topics, goals, and author attributes were derived from an expert review of submissions guided by a rubric. RESULTS Of the 166 accepted submissions, 58 (34.9%) had clinician involvement and 83 submissions (50.0%) that focused on clinical practice included clinical collaborators. A total of 97 data sets (58.4%) used in submissions were publicly available or required a standard registration process. Clinical practice was the most common application area (70 manuscripts [42.2%]), with brain and mental health (25 [15.1%]), oncology (21 [12.7%]), and cardiovascular (19 [11.4%]) being the most common specialties. CONCLUSIONS AND RELEVANCE Trends in machine learning for health research indicate the importance of well-annotated, easily accessed data and the benefit from greater clinician involvement in the development of translational applications.
Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities
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Examples of fully integrated machine learning models that drive clinical care are rare. Despite major advances in the development of methodologies that outperform clinical experts and growing prominence of machine learning in mainstream medical literature, major challenges remain. At Duke Health, we are in our fourth year developing, piloting, and implementing machine learning technologies in clinical care. To advance the translation of machine learning into clinical care, health system leaders must address barriers to progress and make strategic investments necessary to bring health care into a new digital age. Machine learning can improve clinical workflows in subtle ways that are distinct from how statistics has shaped medicine. However, most machine learning research occurs in siloes, and there are important, unresolved questions about how to retrain and validate models post-deployment. Academic medical centers that cultivate and value transdisciplinary collaboration are ideally...
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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...
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.
The Significance of Machine Learning in Healthcare Data Management
Healthcare data management in machine learning is the application of different machine learning techniques to analyse and interpret large amounts of healthcare data, including patient records, administrative data, clinical data, and much more. The history of machine learning as well as some fundamental knowledge of these methods, and the various roles it has played in healthcare data management are briefly explored in this paper. The impact of machine learning on data collection, analysis, predictive modelling, decision-making procedures, and overall healthcare improvement is given special consideration. The healthcare sector has benefited greatly from machine learning since it can process a variety of datasets and provide early disease diagnosis, individualized treatment plans, disease prevention, and anomaly detection. To predict this disease, various supervised machine learning algorithms are used.