Multimodal machine learning in precision health: A sco** review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Challenges and opportunities beyond structured data in analysis of electronic health records
Electronic health records (EHR) contain a lot of valuable information about individual
patients and the whole population. Besides structured data, unstructured data in EHRs can …
patients and the whole population. Besides structured data, unstructured data in EHRs can …
Machine learning prediction in cardiovascular diseases: a meta-analysis
Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular
disease prediction. We aim to assess and summarize the overall predictive ability of ML …
disease prediction. We aim to assess and summarize the overall predictive ability of ML …
Artificial intelligence in cardiology: Hope for the future and power for the present
Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With
the pressures for improved care and translation of the latest medical advances and …
the pressures for improved care and translation of the latest medical advances and …
Artificial intelligence in cardiology: present and future
Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of
various types but most often to deep neural networks. Cardiology is at the forefront of AI in …
various types but most often to deep neural networks. Cardiology is at the forefront of AI in …
Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality
Aims This study aimed to review the performance of machine learning (ML) methods
compared with conventional statistical models (CSMs) for predicting readmission and …
compared with conventional statistical models (CSMs) for predicting readmission and …
Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure
Abstract Machine learning and artificial intelligence are generating significant attention in
the scientific community and media. Such algorithms have great potential in medicine for …
the scientific community and media. Such algorithms have great potential in medicine for …
[HTML][HTML] The economic impact of artificial intelligence in health care: systematic review
J Wolff, J Pauling, A Keck, J Baumbach - Journal of medical Internet …, 2020 - jmir.org
Background Positive economic impact is a key decision factor in making the case for or
against investing in an artificial intelligence (AI) solution in the health care industry. It is most …
against investing in an artificial intelligence (AI) solution in the health care industry. It is most …
[HTML][HTML] Readmission prediction using deep learning on electronic health records
Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF)
patients that pose significant health risks and escalate care cost. In order to reduce …
patients that pose significant health risks and escalate care cost. In order to reduce …
Cost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy
Objective To assess the cost-effectiveness of artificial intelligence (AI) for supporting
clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology …
clinicians in detecting and grading diseases in dermatology, dentistry, and ophthalmology …