ADAPTIVE HEALTHCARE DECISION SUPPORT SYSTEMS USING REINFORCEMENT LEARNING

Abstract

Aim: This work aims to develop a cloud-based planning system automating and optimizing nursing care planning with case-based reasoning (CBR) and Reinforcement Learning.

Background: The traditional methods have to overcome the considerable time required to create customized treatment plans and variation in the quality of treatment resulting from personal beliefs. Ensuring elderly individuals receive high-quality healthcare asks for simpler procedures and correct decision-making.

Contribution: Effective nursing care planning in nursing homes is essential to match senior patients' expectations and streamline healthcare procedures, thereby preserving high-quality services in view of the worldwide increasing aging population. Standard hand-written nursing care plans largely rely on professional experience and subjective assessment.

Methodology: This paper provides a cloud computing adaptive decision support system based on case-based reasoning (CBR) and Reinforcement Learning.

Findings: This technology develops treatment plans based on past like situations by means of real-time medical record gathering. Domain experts, who might not sufficiently meet geriatric demands, determine current CBR case adaption. Numerical data shows that service satisfaction increased by 25% while planning time fell by thirty percent.

Recommendation of Research: Pilot studies in a nursing home indicate that this approach reduces the time required to develop treatment programs and increases service satisfaction.

Future Research: Including text mining into CBR case adaptation process helps to gather current medical data from the Internet, so increasing efficiency.