Contents I. Introduction
Personalized medicine has catalyzed a transformative shift in healthcare by changing the traditional one-size-fits-all model, to an individual approach to reflect each patient’s genetics, environment, and lifestyle. This new approach, called a guided therapy plan in practice achieves optimization of therapeutic outcomes by providing only the necessary treatment for an individual at their specific trait forcing efficiency with lesser adverse effects. Here, this paper provides a novel approach that utilizes the hybrid Reinforcement Learning-Convolutional Neural Network (RL-CNN) model to develop personalized treatment plans in drug dosage and schedule optimization at the patient-specific level. Current prescribing techniques tend to use population averages where standard doses are recommended leading to patients getting a substandard output because different people respond differently but at the same treatment. These responses are affected significantly by factors like genetic variations, the age range of the individual, body weight type 2 diabetes-associated comorbidities lifestyle, etc., thereby creating a challenge for healthcare providers to decide on the best treatment strategies considering each patient. More advanced techniques are required to cope with this variability and give personalized treatment recommendations. Reinforcement Learning(RL) - machine learning technique used for sequential decision-making tasks, which learns through repeated trial and error interactions with an environment to determine the optimal course of action in each step. It has already demonstrated significant success across other sectors such as game playing, robotics, and now healthcare. However, Convolutional Neural Networks (CNNs) are extremely efficient at dealing with large-dimensional data like images and signals due to their capacity to capture spatial hierarchies and complex patterns [1]. The proposed RL-CNN model synergizes these two powerful methods, taking advantage of the strengths of both to create a solid foundation for personalized medicine. The main aim of our study is the development and validation of an RL-CNN model capable of accurately predicting medication doses and dosing regimens tailored for a given individual, from other patient data collected in complex ICU practice. This model will be based on an extensive dataset comprising patient demographics, medical history, genomics, and treatment response [2]. The RL-CNN model is built to predict how different patients will react when given various doses and schedules of medication, identify the best dosages and routines that serve therapeutic benefits while minimizing side effects for specific types of medications (drug responses), continually adjust treatments based on continued patient outcomes (or/and additional data) such that prescribed plans remain effective over time; combining Reinforcement Learning’s capacity to learn from experience in choosing optimal strategies with Convolutional Neural Networks’ aptitude at distilling salient features from complex input. The importance of this is that it has the potential to change how personalized medicine works. The RL-CNN can provide a data-driven approach for adaptation-based treatment planning, and reduce the risk of side effects that ultimately enhance patient outcomes through delivering personalized treatments to individual needs. Personalized treatment strategies are also believed to help save healthcare resources by eliminating unnecessary trial-and-error prescriptions leading to lesser health costs [3]. In addition, these insights stimulated through the RL-CNN model might potentially help in understanding aspects of patient-specific drug responses that lead to further innovations and developments contributing toward personalized medicine [4]. In conclusion, the main goal of this research is to produce a comprehensive tool for personalized treatment planning exploiting RL and CNN. We have used these advanced machine learning techniques to develop the RL-CNN model, which is a novel computational method that serves as an attractive solution in individualized medicine aimed at improving therapeutic results, reducing costs related to health, and subjecting medical to research protection [5].
