DEEP REINFORCEMENT LEARNING FOR DYNAMIC TREATMENT REGIMES

Abstract

Aim: To explore the interactions between machine learning, healthcare, and the IoT . They study the intersection of these three disciplines and the related methods, difficulties, and opportunities. Studying the ways that data-driven insights and technological advancements are combining to create improved health is the aim of this article in order to deliver better health and to rethink healthcare.

Background: Traditional healthcare systems offer an attainable level of performance when it faces numerous patient data volumes.

Contribution: Internet of Things (IoT) provides a seamless data collection and the selection of correction application for data is considered challenge in creation of a precise health forecasts.

Methodology:  Complex algorithms are essential for extraction of relevant details from the patient data and this includes medical IDs, pulse rates, medical reports, and symptoms. Hence, dependable and accurate estimates are needed for ensuring the health quality of the patients. The proposed work provides the development of an IoT healthcare prediction using Generative Adversarial Networks (GANs) based Dynamic Reinforcement Learning (DRL). These features patterns including medical reports, the sequences, and the trends are noticed in pulse rates. The network initially learns to maintain connections among patterns in pulse rates and its associated symptoms in medical data to generate accurate forecasts.

Findings: The experimental results are based on training the RNN model with historical patient data and validate it using DRL. The trained RNN-DRL model may identify likely medical issues if consistent real-time patient data is fed to it.

Recommendation for Researchers: Furthermore, in terms of computing efficiency and accuracy of prediction, our method beat those of previous methods. Furthermore improved interpretability of the learnt characteristics gave important new perspectives on the underlying patterns in dynamic treatment methods.

Future Research: In future research, this work can be enhanced using several deep learning algorithms for achieving better accuracy and performance.