Faculty Opinions recommendation of Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging.

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.

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Current Applications of Machine Learning in Spine: From Clinical View

Global Spine Journal ◽

10.1177/21925682211035363 ◽

2021 ◽

pp. 219256822110353

Author(s):

GuanRui Ren ◽

Kun Yu ◽

ZhiYang Xie ◽

PeiYang Wang ◽

Wei Zhang ◽

...

Keyword(s):

Machine Learning ◽

Meta Analysis ◽

Clinical Applications ◽

Excellent Performance ◽

Randomized Controlled ◽

Decision Supporting ◽

Pubmed Search ◽

Surgery Outcomes ◽

Applications Of Machine Learning ◽

Immense Potential

Study Design: Narrative review. Objectives: This review aims to present current applications of machine learning (ML) in spine domain to clinicians. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. Results: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. Conclusions: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models’ assistance in real work.

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Applications of Machine Learning and Rule Induction,

10.21236/ada292607 ◽

1995 ◽

Cited By ~ 4

Author(s):

Pat Langley ◽

Herbert A. Simon

Keyword(s):

Machine Learning ◽

Rule Induction ◽

Applications Of Machine Learning

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Use of Machine Learning Techniques in Healthcare: A Brief Review of Cardiovascular Disease Classification

SSRN Electronic Journal ◽

10.2139/ssrn.3681833 ◽

2020 ◽

Author(s):

Suja Panicker ◽

Gayathri P

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Disease Classification ◽

Machine Learning Techniques ◽

Learning Techniques

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Exploring the Applications of Machine Learning in Healthcare

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666191220103417 ◽

2020 ◽

Vol 10 (4) ◽

pp. 458-472

Author(s):

Tausifa Jan Saleem ◽

Mohammad Ahsan Chishti

Keyword(s):

Machine Learning ◽

Disease Risk ◽

Disease Diagnosis ◽

Machine Intelligence ◽

Healthcare Applications ◽

Comprehensive Overview ◽

Machine Learning Applications ◽

Remote Healthcare ◽

Healthcare Monitoring ◽

Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

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