2048f54a98aa7aec577b7fcbf29513d8924d8cd9
Title
M2FP-UQ: Multi-Modal Few-Shot Prompting with Uncertainty Quantification for Robust Rare Disease Diagnosis
Problem Statement
Existing few-shot prompting methods for rare disease diagnosis often fail to effectively integrate multiple data modalities and struggle to provide reliable uncertainty estimates, which are crucial for clinical decision-making. This limitation hinders the practical application of AI in rare disease diagnosis, where data scarcity and the need for reliable predictions are paramount.
Motivation
Current approaches typically focus on single modality (e.g., text-based) few-shot learning or simple concatenation of different modalities, without properly accounting for the uncertainty in predictions. By developing a multi-modal few-shot prompting framework that incorporates uncertainty quantification, we can provide more robust and interpretable rare disease diagnoses, leveraging diverse data sources while acknowledging the limitations of the model's predictions. This approach is inspired by the success of multi-modal learning in other medical domains and the growing recognition of the importance of uncertainty estimation in high-stakes decision-making scenarios.
Proposed Method
We propose Multi-Modal Few-Shot Prompting with Uncertainty Quantification (M2FP-UQ), a novel approach that combines multi-modal learning with Bayesian uncertainty estimation for rare disease diagnosis. The method involves: 1) Designing a multi-modal encoder that processes textual descriptions, medical images, and structured data (e.g., lab results) simultaneously. 2) Implementing a few-shot prompting mechanism that dynamically selects the most relevant examples from each modality based on the query. 3) Employing a Bayesian neural network layer that outputs both the predicted diagnosis and associated uncertainty. 4) Developing an adaptive prompting strategy that adjusts the number and type of examples based on the estimated uncertainty. 5) Incorporating a causal inference module to identify potential confounding factors and improve the robustness of the diagnoses.
Step-by-Step Experiment Plan
Step 1: Data Preparation
Collect and preprocess multi-modal rare disease datasets. Combine existing public datasets (e.g., MIMIC-III for structured clinical data, ChestX-ray14 for medical images) with a curated dataset of rare disease cases. Ensure data includes textual descriptions, medical images, and structured data for each case.
Step 2: Implement Multi-Modal Encoder
Develop a multi-modal encoder using pre-trained models for each modality: BERT for text, ResNet for images, and a custom MLP for structured data. Implement a fusion layer to combine features from all modalities.
Step 3: Few-Shot Prompting Mechanism
Design a dynamic few-shot prompting system that selects relevant examples based on similarity to the query across all modalities. Use cosine similarity in the embedded space for each modality to select the most relevant examples.
Step 4: Bayesian Uncertainty Estimation
Implement a Bayesian neural network layer using Monte Carlo Dropout. This layer will output both the predicted diagnosis and associated uncertainty.
Step 5: Adaptive Prompting Strategy
Develop an algorithm that adjusts the number and type of few-shot examples based on the estimated uncertainty. Higher uncertainty should trigger the inclusion of more diverse examples.
Step 6: Causal Inference Module
Implement a causal inference module using a structural causal model to identify potential confounding factors in the diagnosis process.
Step 7: Experiment Setup
Compare M2FP-UQ against baselines: single-modality few-shot learning, simple multi-modal concatenation, and standard few-shot prompting without uncertainty quantification. Use GPT-4 as the base language model for all experiments.
Step 8: Evaluation
Assess performance using metrics such as diagnostic accuracy, calibration error, and area under the precision-recall curve. Conduct ablation studies to evaluate the contribution of each component.
Step 9: Clinical Utility Assessment
Conduct a small-scale user study with medical professionals to evaluate the interpretability and clinical utility of the uncertainty estimates and multi-modal explanations.
Step 10: Analysis and Reporting
Analyze results, focusing on improvements in rare disease diagnosis accuracy and the quality of uncertainty estimates. Prepare a comprehensive report and research paper detailing the findings.
Test Case Examples
Baseline Prompt Input (Single-Modality)
Patient presents with progressive muscle weakness, difficulty swallowing, and fasciculations. What is the most likely diagnosis?
Baseline Prompt Expected Output (Single-Modality)
Based on the symptoms described, the most likely diagnosis is Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease.
Proposed Prompt Input (M2FP-UQ)
Patient presents with progressive muscle weakness, difficulty swallowing, and fasciculations. [IMAGE: EMG showing widespread denervation] [STRUCTURED_DATA: Creatine kinase levels: 450 U/L, Forced Vital Capacity: 65% predicted] Provide a diagnosis with uncertainty estimate.
Proposed Prompt Expected Output (M2FP-UQ)
Diagnosis: Amyotrophic Lateral Sclerosis (ALS)
Confidence: 85%
Uncertainty: ±7%
Explanation: The combination of progressive muscle weakness, dysphagia, and fasciculations, along with the EMG findings of widespread denervation and the mildly elevated creatine kinase levels, strongly suggests ALS. However, the uncertainty stems from the need to rule out other motor neuron diseases and the possibility of atypical presentations. Further tests, such as genetic screening for SOD1 mutations, may be warranted to increase diagnostic certainty.
Explanation
The M2FP-UQ method provides a more comprehensive analysis by integrating multiple data modalities and offering an uncertainty estimate. This allows for a more nuanced diagnosis and highlights areas where additional information might be needed, which is crucial for rare disease diagnosis where atypical presentations are common.
Fallback Plan
If the proposed M2FP-UQ method does not significantly outperform baselines, we will conduct a thorough error analysis to identify the limitations of our approach. This may involve examining cases where the multi-modal integration fails to improve diagnosis accuracy or where uncertainty estimates are poorly calibrated. We could then focus on improving specific components, such as the multi-modal fusion mechanism or the uncertainty estimation technique. Additionally, we might explore alternative approaches like meta-learning for few-shot adaptation or more sophisticated causal inference techniques. If these modifications do not yield substantial improvements, we could pivot the project towards an in-depth analysis of the challenges in multi-modal few-shot learning for rare disease diagnosis, providing insights into the limitations of current AI approaches in this domain and suggesting future research directions.