7 articles tagged with #missing-data. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce Pan-FM, a foundation model trained on multimodal medical imaging from seven organs that addresses the critical problem of missing data in real-world biomedical datasets. The model uses Saliency-Guided Masking to prevent bias toward dominant organs and demonstrates superior performance on disease prediction tasks across the UK Biobank.
Researchers propose Latent World Recovery (LWR), a machine learning framework that handles multimodal datasets with missing data by aligning different data types in a shared latent space rather than imputing missing values. The approach shows promise for bioscience applications like cancer classification and survival prediction where heterogeneous data sources are often incomplete.
Researchers introduce LongMoE, a machine learning framework designed to improve clinical AI systems by simultaneously handling missing patient data and tracking disease progression over time. The model combines mixture-of-experts routing with temporal pattern recognition, demonstrating improvements across major medical datasets (ADNI, OASIS-3, MIMIC-IV).
Researchers introduce FADTI, a diffusion-based framework for multivariate time series imputation that combines Fourier frequency analysis with attention mechanisms to handle missing data in healthcare, traffic, and biological systems. The model demonstrates superior performance over existing methods, particularly when dealing with high missing data rates and distribution shifts.
PAMF is a new machine learning framework that addresses incomplete multimodal time series data in healthcare by distinguishing between two types of missing data patterns and coupling imputation with downstream prediction tasks. The method uses flow matching with type-specific priors and weight sharing to achieve superior performance on healthcare benchmarks compared to existing approaches.
Researchers introduce the Sequential Triply Robust (STR) estimator to correct systematic biases in payment fraud detection models caused by authorization gates, unreported fraud, chargeback delays, and label corruption. The method achieves theoretical efficiency bounds while enabling models to train on fresher data, potentially reducing the need to wait months for complete chargeback information.
Researchers propose REMIND, a framework for medical multi-modal AI learning that addresses the challenge of missing data across multiple modalities. The solution uses a Mixture-of-Experts architecture to handle long-tail distributions of modality combinations and shows superior performance on real-world medical datasets.
