Πίνακας περιεχομένων:

Αποθηκεύτηκε σε:

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας:	Ceperkovic, Branislav
Μορφή:	Recurso digital
Γλώσσα:	Αγγλικά
Έκδοση:	Zenodo 2026
Θέματα:	Reinforcement Learning, Deep Reinforcement Learning, Precision Oncology, Cancer Therapy Optimization, Computational Oncology, Genomic Data Integration, Phenotypic Data, Multi-modal Learning, Deep Q-Network, Markov Decision Process, Personalized Medicine, Adaptive Therapy Systems Markov Decision Process (MDP) Deep Q-Network (DQN) Hindsight Experience Replay Neural Architecture Search Sequential Decision Making Computational Oncology Reinforcement Learning Deep Reinforcement Learning Precision Oncology Cancer Therapy Optimization Cancer Treatment Planning Genomic Data Analysis Personalized Medicine Cancer Biomarkers Genomic Data Integration Phenotypic Data Modeling Multi-modal Learning Clinical Data Integration AI-driven Healthcare NeuroAI Decision Support Systems Computational Medicine Adaptive Therapy Systems AI in Oncology Machine Learning in Medicine Digital Health
Διαθέσιμο Online:	https://doi.org/10.5281/zenodo.19613095
Ετικέτες:	Προσθήκη ετικέτας Δεν υπάρχουν, Καταχωρήστε ετικέτα πρώτοι!

Πίνακας περιεχομένων:

<p>This white paper introduces a conceptual reinforcement learning framework for adaptive cancer therapy optimization, integrating genomic and phenotypic data within a unified computational model. The proposed approach formulates cancer treatment as a sequential decision-making problem under uncertainty, leveraging Deep Reinforcement Learning (DRL) methods such as Deep Q-Networks and Hindsight Experience Replay to optimize therapeutic strategies over time. By incorporating multi-modal patient representations, including genomic mutation profiles and clinical phenotypic features, the framework aims to enable personalized treatment planning and dynamic adjustment of therapy protocols. Although currently theoretical, the model provides a foundation for future computational oncology systems that may improve precision medicine through AI-driven decision support and adaptive optimization of cancer treatment pathways.</p>

Παρόμοια τεκμήρια