PaReGTA: An LLM-based EHR Data Encoding Approach to Capture Temporal Information

Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence models can be costly and data-hungry. We propose PaReGTA, an LLM-based encoding framework that (i) converts longitudinal EHR events into visit-level templated text with explicit temporal cues, (ii) learns domain-adapted visit embeddings via lightweight contrastive fine-tuning of a sentence-embedding model, and (iii) aggregates visit embeddings into a fixed-dimensional patient representation using hybrid temporal pooling that captures both recency and globally informative visits. Because PaReGTA does not require training from scratch but instead utilizes a pre-trained LLM, it can perform well even in data-limited cohorts. Furthermore, PaReGTA is model-agnostic and can benefit from future EHR-specialized sentence-embedding models. For interpretability, we introduce PaReGTA-RSS (Representation Shift Score), which quantifies clinically defined factor importance by recomputing representations after targeted factor removal and projecting representation shifts through a machine learning model. On 39,088 migraine patients from the All of Us Research Program, PaReGTA outperforms sparse baselines for migraine type classification while deep sequential models were unstable in our cohort.

Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images

Deep neural networks have revolutionized the field of supervised learning by enabling accurate predictions through learning from large annotated datasets. However, acquiring large annotated medical imaging datasets is a challenging task, especially for rare diseases, due to the high cost, time, and effort required for annotation. In these scenarios, unsupervised disease detection methods, such as anomaly detection, can save significant human effort. A typically used approach for anomaly detection is to learn the images from healthy subjects only, assuming the model will detect the images from diseased subjects as outliers. However, in many real-world scenarios, unannotated datasets with a mix of healthy and diseased individuals are available. Recent studies have shown improvement in unsupervised disease/anomaly detection using such datasets of unannotated images from healthy and diseased individuals compared to datasets that only include images from healthy individuals. A major issue remains unaddressed in these studies, which is selecting the best model for inference from a set of trained models without annotated samples. To address this issue, we propose Brainomaly, a GAN-based image-to-image translation method for neurologic disease detection using unannotated T1-weighted brain MRIs of individuals with neurologic diseases and healthy subjects. Brainomaly is trained to remove the diseased regions from the input brain MRIs and generate MRIs of corresponding healthy brains. Instead of generating the healthy images directly, Brainomaly generates an additive map where each voxel indicates the amount of changes required to make the input image look healthy. In addition, Brainomaly uses a pseudo-AUC metric for inference model selection, which further improves the detection performance. Our Brainomaly outperforms existing state-of-the-art methods by large margins.

HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease

Automated anomaly detection from medical images, such as MRIs and X-rays, can significantly reduce human effort in disease diagnosis. Owing to the complexity of modeling anomalies and the high cost of manual annotation by domain experts (e.g., radiologists), a typical technique in the current medical imaging literature has focused on deriving diagnostic models from healthy subjects only, assuming the model will detect the images from patients as outliers. However, in many real-world scenarios, unannotated datasets with a mix of both healthy and diseased individuals are abundant. Therefore, this paper poses the research question of how to improve unsupervised anomaly detection by utilizing (1) an unannotated set of mixed images, in addition to (2) the set of healthy images as being used in the literature. To answer the question, we propose HealthyGAN, a novel one-directional image-to-image translation method, which learns to translate the images from the mixed dataset to only healthy images. Being one-directional, HealthyGAN relaxes the requirement of cycle consistency of existing unpaired image-to-image translation methods, which is unattainable with mixed unannotated data. Once the translation is learned, we generate a difference map for any given image by subtracting its translated output. Regions of significant responses in the difference map correspond to potential anomalies (if any). Our HealthyGAN outperforms the conventional state-of-the-art methods by significant margins on two publicly available datasets: COVID-19 and NIH ChestX-ray14, and one institutional dataset collected from Mayo Clinic. The implementation is publicly available at https://github.com/mahfuzmohammad/HealthyGAN.