[HTML][HTML] A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities
Administrative and medical processes of the healthcare organizations are rapidly changing
because of the use of artificial intelligence (AI) systems. This change demonstrates the …
because of the use of artificial intelligence (AI) systems. This change demonstrates the …
[HTML][HTML] Reinforcement learning for clinical decision support in critical care: comprehensive review
Background Decision support systems based on reinforcement learning (RL) have been
implemented to facilitate the delivery of personalized care. This paper aimed to provide a …
implemented to facilitate the delivery of personalized care. This paper aimed to provide a …
Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation
Dynamic treatment recommendation systems based on large-scale electronic health records
(EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant …
(EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant …
Conditional generation net for medication recommendation
Medication recommendation targets to provide a proper set of medicines according to
patients' diagnoses, which is a critical task in clinics. Currently, the recommendation is …
patients' diagnoses, which is a critical task in clinics. Currently, the recommendation is …
SMR: medical knowledge graph embedding for safe medicine recommendation
Most of the existing medicine recommendation systems that are mainly based on electronic
medical records (EMRs) are significantly assisting doctors to make better clinical decisions …
medical records (EMRs) are significantly assisting doctors to make better clinical decisions …
Effective deep Q-networks (EDQN) strategy for resource allocation based on optimized reinforcement learning algorithm
FM Talaat - Multimedia Tools and Applications, 2022 - Springer
The healthcare industry has always been an early adopter of new technology and a big
benefactor of it. The use of reinforcement learning in the healthcare system has repeatedly …
benefactor of it. The use of reinforcement learning in the healthcare system has repeatedly …
Personalizing medication recommendation with a graph-based approach
The broad adoption of electronic health records (EHRs) has led to vast amounts of data
being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances …
being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances …
Knowledge-enhanced attributed multi-task learning for medicine recommendation
Medicine recommendation systems target to recommend a set of medicines given a set of
symptoms which play a crucial role in assisting doctors in their daily clinics. Existing …
symptoms which play a crucial role in assisting doctors in their daily clinics. Existing …
Change matters: Medication change prediction with recurrent residual networks
Deep learning is revolutionizing predictive healthcare, including recommending medications
to patients with complex health conditions. Existing approaches focus on predicting all …
to patients with complex health conditions. Existing approaches focus on predicting all …
Justifications for goal-directed constraint answer set programming
Ethical and legal concerns make it necessary for programs that may directly influence the life
of people (via, eg, legal or health counseling) to justify in human-understandable terms the …
of people (via, eg, legal or health counseling) to justify in human-understandable terms the …