Using machine learning approaches for multi-omics data analysis: A review
With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …
become essential for biomedical studies to undertake an integrative (combined) approach to …
Machine learning for healthcare wearable devices: the big picture
Using artificial intelligence and machine learning techniques in healthcare applications has
been actively researched over the last few years. It holds promising opportunities as it is …
been actively researched over the last few years. It holds promising opportunities as it is …
[HTML][HTML] Significance of machine learning in healthcare: Features, pillars and applications
Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …
AI in drug discovery and its clinical relevance
The COVID-19 pandemic has emphasized the need for novel drug discovery process.
However, the journey from conceptualizing a drug to its eventual implementation in clinical …
However, the journey from conceptualizing a drug to its eventual implementation in clinical …
Comparison of feature importance measures as explanations for classification models
M Saarela, S Jauhiainen - SN Applied Sciences, 2021 - Springer
Explainable artificial intelligence is an emerging research direction helping the user or
developer of machine learning models understand why models behave the way they do …
developer of machine learning models understand why models behave the way they do …
Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives
P Esmaeilzadeh - BMC medical informatics and decision making, 2020 - Springer
Background Several studies highlight the effects of artificial intelligence (AI) systems on
healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning …
healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning …
Review of ML and AutoML solutions to forecast time-series data
A Alsharef, K Aggarwal, Sonia, M Kumar… - … Methods in Engineering, 2022 - Springer
Time-series forecasting is a significant discipline of data modeling where past observations
of the same variable are analyzed to predict the future values of the time series. Its …
of the same variable are analyzed to predict the future values of the time series. Its …
Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review
Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
Accurate automated medical image recognition, including classification and segmentation,
is one of the most challenging tasks in medical image analysis. Recently, deep learning …
is one of the most challenging tasks in medical image analysis. Recently, deep learning …
The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies
F Cabitza, A Campagner - International Journal of Medical Informatics, 2021 - Elsevier
This editorial aims to contribute to the current debate about the quality of studies that apply
machine learning (ML) methodologies to medical data to extract value from them and …
machine learning (ML) methodologies to medical data to extract value from them and …