PMTRS: A Personalized Multimodal Treatment Response System Framework for Personalized Healthcare
Sajib Alam
Trine University
Abstract
Various types of data are used in clinical settings, such as imaging, textual, sequential, and, tabular data. Multimodal machine learning focuses on combining multiple modalities (types) to create an overall representation. This involves extracting features from each modality, combining them into a unified representation, and using these representations to enhance decision-making processes in AI applications. This research introduces the Personalized Multimodal Treatment Response System (PMTRS), a novel framework aimed at enhancing personalized treatment by utilizing multimodal machine learning to analyze various data types, including genetic information, medical imaging, and electronic health records. The proposed PMTRS is designed to predict and optimize individual treatment outcomes through a structured approach comprising several key components. First, the Data Collection and Preprocessing Module is responsible for gathering diverse patient data and preparing it for analysis through normalization and modality-specific processing techniques. The Feature Extraction and Integration Module then applies deep learning models, such as convolutional neural networks for imaging data and natural language processing for electronic health records, to extract relevant features and integrate them using fusion techniques. At the core of PMTRS is the Personalized Treatment Prediction Model, which employs a multimodal deep learning architecture capable of handling integrated features from various data types using supervised learning and incorporates transfer learning to predict treatment responses accurately. The Treatment Recommendation System uses these predictions to provide personalized treatment options, supported by an Explainability Module to ensure transparency and build trust in the system's decisions.
Author Biography
Sajib Alam, Trine University
Sajib Alam
Trine University
