Multimodal Clinical Prediction Models for Data Reconciliation
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Solution Overview
Problem
Existing solutions struggle to reconcile and analyze multimodal medical data effectively, leading to inaccurate clinical predictions due to differing data formats, incomplete datasets, and the need for manual intervention, which complicates the training of machine learning models for personalized treatment responses.
Innovation Solution
A system and method for ingesting, reconciling, and preprocessing multimodal medical data, including genomic, radiological, and clinical data, to train predictive models that generate clinical predictions by intelligently weighting features and accounting for intra-group variation, using a multimodal 'factory' system that automates data management and model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual intervention and separate tools are used for data preprocessing and model training, then data processing can be performed, but the system complexity increases and automation decreases
Solution Approach 1:
The patent merges multiple separate tools and manual processes into a single integrated multimodal factory system that automatically performs data ingestion, reconciliation, preprocessing, and model training. This consolidation eliminates the need for separate bioinformatic pipelines and manual intervention, directly resolving the contradiction by achieving high automation without proportionally increasing system complexity.
Solution Approach 2:
The multimodal factory system is designed as a universal platform capable of handling multiple data modalities (genomic, radiological, clinical) and performing various functions (data reconciliation, preprocessing, model training) through a single unified system. This multi-functionality allows the system to automate diverse tasks without requiring separate specialized tools for each function.
2Measurement precision
If data from multiple modalities is integrated, then prediction accuracy improves, but data reconciliation becomes more difficult
Solution Approach 1:
The patent introduces a data reconciliation module as an intermediary component within the multimodal factory system that specifically handles the challenge of integrating multiple data modalities. This module standardizes and harmonizes data from genomic, radiological, and clinical sources before they are fed into the prediction models, thereby improving prediction accuracy while managing the complexity of data reconciliation through a dedicated intermediary mechanism.
3Measurement precision
If complete datasets are required for all patients, then model accuracy improves, but data collection time and completeness decrease due to missing values
Solution Approach 1:
The patent implements a data imputation mechanism that performs preliminary action by filling in missing values before the model training process. The system automatically imputes missing data across different modalities, allowing models to be trained on complete datasets without requiring extensive additional data collection time, thus maintaining model accuracy while minimizing time loss.
4Reliability
If personalized predictions for each patient are generated, then treatment efficacy improves, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-processing and standardizing patient data through the multimodal factory pipeline before individualized prediction. This preliminary processing organizes and reconciles data in advance, enabling efficient generation of personalized predictions without excessive computational resource consumption during the actual prediction phase.
Data Source
AI summary
Systems and methods for training predictive models for generating clinical predictions from multimodal medical data include receiving multimodal medical data of one or more medical subjects, and preprocessing and aggregating one or more features of the multimodal medical data. Further, for each cohort of medical subjects from the medical subjects, the method includes training one or more predictive models to generate a clinical prediction for each of the diseases based on the features of the multimodal medical data and deploying the predictive models to a model bank. The predictive models are used for making clinical predictions based on multimodal medical data of individual medical subjects. Use of multimodal medical data improves accuracy of the clinical predictions. Further, deploying predictive models on the model bank improves accessibility and useability of the predictive models.