[HTML][HTML] A survey of internet of medical things: technology, application and future directions

P He, D Huang, D Wu, H He, Y Wei, Y Cui… - Digital Communications …, 2024 - Elsevier
As the healthcare industry continues to embrace digital transformation, the Internet of
Medical Things (IoMT) emerges as a key technology. IoMT plays a critical role in …

XAI Unveiled: Revealing the Potential of Explainable AI in Medicine-A Systematic Review

N Scarpato, P Ferroni, F Guadagni - IEEE Access, 2024 - ieeexplore.ieee.org
Nowadays, artificial intelligence in medicine plays a leading role. This necessitates the need
to ensure that artificial intelligence systems are not only high-performing but also …

An interpretable neural network for outcome prediction in traumatic brain injury

C Minoccheri, CA Williamson, M Hemmila… - BMC Medical Informatics …, 2022 - Springer
Abstract Background Traumatic Brain Injury (TBI) is a common condition with potentially
severe long-term complications, the prediction of which remains challenging. Machine …

A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction

M Hodgman, C Minoccheri, M Mathis, E Wittrup… - …, 2024 - pmc.ncbi.nlm.nih.gov
Background: Acute myocardial infarctions are deadly to patients and burdensome to
healthcare systems. Most recorded infarctions are patients' first, occur out of the hospital …

Predicting need for heart failure advanced therapies using an interpretable tropical geometry-based fuzzy neural network

Y Zhang, KD Aaronson, J Gryak, E Wittrup… - Plos one, 2023 - journals.plos.org
Background Timely referral for advanced therapies (ie, heart transplantation, left ventricular
assist device) is critical for ensuring optimal outcomes for heart failure patients. Using …

Learning Physiological Mechanisms that Predict Adverse Cardiovascular Events in Intensive Care Patients with Chronic Heart Disease

M Hodgman, E Wittrup… - 2024 46th Annual …, 2024 - ieeexplore.ieee.org
Chronic heart disease is a burdensome, complex, and fatal condition. Learning the
mechanisms driving the development of heart disease is key to early risk assessment and …

CoxFNN: Interpretable machine learning method for survival analysis

Y Zhang, E Wittrup, K Najarian - 2024 46th Annual International …, 2024 - ieeexplore.ieee.org
Survival analysis plays a pivotal role in healthcare, particularly in analyzing time-to-event
data such as in disease progression, treatment efficacy, and drug development. Traditional …

Survival analysis of heart failure patients using advanced machine learning techniques

P Makam, G Janardhan - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
According to World health organization, the death rate due to cardiovascular Disease (CVD)
globally touches an estimate of 17.9 million every year. It became very significant for the …

EvolveFNN: An interpretable framework for early detection using longitudinal electronic health record data

Y Zhang, E Wittrup, K Najarian, M Mathis - 2023 - researchsquare.com
The extensive adoption of artificial intelligence in clinical decision support systems
necessitates a significant presence of ML models that clinicians can easily interpret …

[PDF][PDF] A Machine Learning-Driven Approach to Comprehensive Cardiac Assessment in Health Monitoring

CP Kagita - foundryjournal.net
Comprehensive Cardiac Assessment in Health Monitoring project introduces an innovative
health monitoring application designed for continuous heartbeat analysis using machine …