Survey on categorical data for neural networks
JT Hancock, TM Khoshgoftaar - Journal of big data, 2020 - Springer
This survey investigates current techniques for representing qualitative data for use as input
to neural networks. Techniques for using qualitative data in neural networks are well known …
to neural networks. Techniques for using qualitative data in neural networks are well known …
Application of artificial intelligence in medicine: an overview
Artificial intelligence (AI) is a new technical discipline that uses computer technology to
research and develop the theory, method, technique, and application system for the …
research and develop the theory, method, technique, and application system for the …
Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques
IMK Ho, KY Cheong, A Weldon - Plos one, 2021 - journals.plos.org
Despite the wide adoption of emergency remote learning (ERL) in higher education during
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …
the COVID-19 pandemic, there is insufficient understanding of influencing factors predicting …
Artificial intelligence in the intensive care unit
G Gutierrez - Annual Update in Intensive Care and Emergency …, 2020 - Springer
The application of artificial intelligence (AI) techniques to the monitoring and treatment of
patients in the intensive care unit (ICU) is advancing rapidly from future possibility to …
patients in the intensive care unit (ICU) is advancing rapidly from future possibility to …
[HTML][HTML] Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …
secondary uses, such as clinical events prediction and chronic disease management …
Machine learning model to predict mental health crises from electronic health records
R Garriga, J Mas, S Abraha, J Nolan, O Harrison… - Nature medicine, 2022 - nature.com
The timely identification of patients who are at risk of a mental health crisis can lead to
improved outcomes and to the mitigation of burdens and costs. However, the high …
improved outcomes and to the mitigation of burdens and costs. However, the high …
Application of machine learning in predicting hospital readmissions: a scoping review of the literature
Background Advances in machine learning (ML) provide great opportunities in the
prediction of hospital readmission. This review synthesizes the literature on ML methods and …
prediction of hospital readmission. This review synthesizes the literature on ML methods and …
[HTML][HTML] State of the art of machine learning–enabled clinical decision support in intensive care units: literature review
N Hong, C Liu, J Gao, L Han, F Chang… - JMIR medical …, 2022 - medinform.jmir.org
Background Modern clinical care in intensive care units is full of rich data, and machine
learning has great potential to support clinical decision-making. The development of …
learning has great potential to support clinical decision-making. The development of …
Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study
The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is
a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients …
a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients …
Time series prediction using deep learning methods in healthcare
Traditional machine learning methods face unique challenges when applied to healthcare
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …