A systematic review of time series classification techniques used in biomedical applications
Background: Digital clinical measures collected via various digital sensing technologies
such as smartphones, smartwatches, wearables, and ingestible and implantable sensors …
such as smartphones, smartwatches, wearables, and ingestible and implantable sensors …
A review of deep learning methods for irregularly sampled medical time series data
Irregularly sampled time series (ISTS) data has irregular temporal intervals between
observations and different sampling rates between sequences. ISTS commonly appears in …
observations and different sampling rates between sequences. ISTS commonly appears in …
Appositeness of optimized and reliable machine learning for healthcare: a survey
Abstract Machine Learning (ML) has been categorized as a branch of Artificial Intelligence
(AI) under the Computer Science domain wherein programmable machines imitate human …
(AI) under the Computer Science domain wherein programmable machines imitate human …
Correlation‐based ensemble feature selection using bioinspired algorithms and classification using backpropagation neural network
VR Elgin Christo, H Khanna Nehemiah… - … methods in medicine, 2019 - Wiley Online Library
A framework for clinical diagnosis which uses bioinspired algorithms for feature selection
and gradient descendant backpropagation neural network for classification has been …
and gradient descendant backpropagation neural network for classification has been …
Feature selection and classification of clinical datasets using bioinspired algorithms and super learner
S Murugesan, RS Bhuvaneswaran… - … Methods in Medicine, 2021 - Wiley Online Library
A computer‐aided diagnosis (CAD) system that employs a super learner to diagnose the
presence or absence of a disease has been developed. Each clinical dataset is …
presence or absence of a disease has been developed. Each clinical dataset is …
[HTML][HTML] Incorporating repeating temporal association rules in Naïve Bayes classifiers for coronary heart disease diagnosis
In this paper, we develop a Naïve Bayes classification model integrated with temporal
association rules (TARs). A temporal pattern mining algorithm is used to detect TARs by …
association rules (TARs). A temporal pattern mining algorithm is used to detect TARs by …
Temporal pattern mining for knowledge discovery in the early prediction of septic shock
Temporal pattern mining can be employed to detect patterns and trends in a patient's health
status as it evolves over time. However, these methods often produce an overwhelming …
status as it evolves over time. However, these methods often produce an overwhelming …
A python clustering analysis protocol of genes expression data sets
Gene expression and SNPs data hold great potential for a new understanding of disease
prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data …
prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data …
A systematic approach for evaluating artificial intelligence models in industrial settings
Artificial Intelligence (AI) is one of the hottest topics in our society, especially when it comes
to solving data-analysis problems. Industry are conducting their digital shifts, and AI is …
to solving data-analysis problems. Industry are conducting their digital shifts, and AI is …
Intelligent mining algorithm for complex medical data based on deep learning
X Li, D Li, Y Deng, J Xing - Journal of Ambient Intelligence and Humanized …, 2021 - Springer
In order to address the problems of low precision, long time-consuming and low recall rate in
mining complex attribute medical data in medical information, an intelligent mining algorithm …
mining complex attribute medical data in medical information, an intelligent mining algorithm …