Revolutionizing Genetic Therapy Delivery: A Comprehensive Study on AI Neural Networks for Predictive Patient Support Systems in Rare Disease Management
Keywords:
Genetic Therapies, Gene Therapies, Rare Diseases, Predictive Patient Support Systems, AI Neural Networks, AI Algorithms, Deep Learning, Genetic Therapy Delivery, Disrupted Genetic Material,, Patient Response Prediction, Biophysical Patient Response, Rare Disease Management, Neural Network Generalization, Machine Learning Algorithms, Drug Eligibility Prediction, Preclinical Healthcare, Patient Viability, Treatment Classification, Experimental Data, Clinical DataAbstract
Genetic therapies and gene therapies hold promise for advancing medicine and curing diseases, but significant advancements are
needed in terms of delivery systems. This study suggests a framework and reviews previous advancements in predictive patient
support system development, which integrates AI neural networks, AI algorithms, and deep learning to increase the efficacy of
genetic therapy delivery to patients. Rare diseases due to disrupted cellular genetic material are of serious concern in the medical
community due to a lack of medical prescriptions and treatments. We show a novel method to improve genetic therapy delivery by
predicting an ideal subset of patients to treat, localized in the space of diseases. We show substantially advanced viability in our
predictive patient support system, capturing the top 40th percentile of candidates using five distinct neural networks trained to
predict biophysical patient response.
The method described has the distinct potential to revolutionize rare disease management. Neural networks integrating all three
layers considerably improved generalization performance. Two-thirds of autonomous patient support systems recipe drug eligibility
in the top 40th percentile, meaning a top performance increase viability of about 1400%, and shows superior generalization
performance over other neural networks. Genetic therapies generally aim to combat rare diseases that are caused by disrupted
cellular genetic material. In a laboratory and preclinical healthcare setting, a patient's response to a pool of drugs and genetic
therapies is predicted via machine learning algorithms trained on experimental and/or clinical data to carry out a specific decision,
such as the classification of positive "yes" treatment or negative "no" eligibility labels. In the context of this study, neural networks
are trained to predict rare patient response data and thus carry the capacity to predict which patients are viable candidates for the
drugs and genetic therapies passed through preclinical trials and are close to commercialization.
