Personalized Treatment Recommendations for Mental Health Disorders Using AI and Big Healthcare Data

The advancement of AI and the availability of massive healthcare datasets have led to significant advancements in the field of mental healthcare. Multiple types of learning are incorporated into the suggested tactic. These include deep learning, collaborative filtering, and reinforcement learning. The goal of Deep Learning for Diagnosis, or DL-D for short, is to improve the precision with which mental health issues are identified by analyzing data from several sources. Examples of such data sources are fmri scans and genetic profiles. Patients' medical records might be combined using the innovative CF-TR algorithm to provide customized therapy recommendations. To improve the effectiveness of pharmaceutical regimens, RL-MRO (Reinforcement Learning for Pharmaceutical Regimen Optimization) was developed by researchers in the area. This innovation automatically modifies dosing plans in response to patient feedback and probable adverse events. To gauge the strategy's efficacy, we compared it to six other options, each of which was predicated on a different group of metrics. The results show that the proposed method outperforms the standard method in terms of accuracy, precision, and memory. How well this technology can individualize treatment programs and simplify dosage schedules for individuals will determine the health outcomes that may be expected. The suggested method of providing mental health care is thorough and customized to meet the unique needs of each patient. This assists persons with mental health concerns in the long run as well as increasing the success of treatment.