Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …
using machine learning techniques across all medical specialties. Design Systematic …
Precision medicine, AI, and the future of personalized health care
The convergence of artificial intelligence (AI) and precision medicine promises to
revolutionize health care. Precision medicine methods identify phenotypes of patients with …
revolutionize health care. Precision medicine methods identify phenotypes of patients with …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
A neural network ensemble with feature engineering for improved credit card fraud detection
Recent advancements in electronic commerce and communication systems have
significantly increased the use of credit cards for both online and regular transactions …
significantly increased the use of credit cards for both online and regular transactions …
Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions
The rapid growth of urban populations worldwide imposes new challenges on citizens' daily
lives, including environmental pollution, public security, road congestion, etc. New …
lives, including environmental pollution, public security, road congestion, etc. New …
Deep learning in clinical natural language processing: a methodical review
Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …
Deep learning for the industrial internet of things (iiot): A comprehensive survey of techniques, implementation frameworks, potential applications, and future directions
The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast
communication protocols, and efficient cybersecurity mechanisms to improve industrial …
communication protocols, and efficient cybersecurity mechanisms to improve industrial …
The secondary use of electronic health records for data mining: Data characteristics and challenges
The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …
management of patients' health-related information. However, these records have also been …
[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 …
Artificial intelligence and heart failure: A state‐of‐the‐art review
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals
globally. Despite recent advancements in understanding of the pathophysiology of HF, many …
globally. Despite recent advancements in understanding of the pathophysiology of HF, many …