Annotation of Sleep Depth Index with Scalable Deep Learning Yields Novel Digital Biomarkers for Sleep Health

Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. It provides limited information about the probability of arousal and may hinder the diagnosis of sleep disorders, such as insomnia. To address this issue, we propose a deep-learning method for automatic and scalable annotation of sleep depth index using existing sleep staging labels. Our approach is validated using polysomnography from over ten thousand recordings across four large-scale cohorts. The results show a strong correlation between the decrease in sleep depth index and the increase in arousal likelihood. Several case studies indicate that the sleep depth index captures more nuanced sleep structures than conventional sleep staging. Sleep biomarkers extracted from the whole-night sleep depth index exhibit statistically significant differences with medium-to-large effect sizes across groups of varied subjective sleep quality and insomnia symptoms. These sleep biomarkers also promise utility in predicting the severity of obstructive sleep apnea, particularly in severe cases. Our study underscores the utility of the proposed method for continuous sleep depth annotation, which could reveal more detailed structures and dynamics within whole-night sleep and yield novel digital biomarkers beneficial for sleep health.

High-throughput Biomedical Relation Extraction for Semi-Structured Web Articles Empowered by Large Language Models

Objective: To develop a high-throughput biomedical relation extraction system that takes advantage of the large language models' (LLMs) reading comprehension ability and biomedical world knowledge in a scalable and evidential manner. Methods: We formulate the relation extraction task as a simple binary classification problem for large language models such as ChatGPT. Specifically, LLMs make the decision based on the external corpus and its world knowledge, giving the reason for the judgment to factual verification. This method is tailored for semi-structured web articles, wherein we designate the main title as the tail entity and explicitly incorporate it into the context, and the potential head entities are matched based on a biomedical thesaurus. Moreover, lengthy contents are sliced into text chunks, embedded, and retrieved with additional embedding models, ensuring compatibility with the context window size constraints of available open-source LLMs. Results: Using an open-source LLM, we extracted 304315 relation triplets of three distinct relation types from four reputable biomedical websites. To assess the efficacy of the basic pipeline employed for biomedical relation extraction, we curated a benchmark dataset annotated by a medical expert. Evaluation results indicate that the pipeline exhibits performance comparable to that of GPT-4. Case studies further illuminate challenges faced by contemporary LLMs in the context of biomedical relation extraction for semi-structured web articles. Conclusion: The proposed method has demonstrated its effectiveness in leveraging the strengths of LLMs for high-throughput biomedical relation extraction. Its adaptability is evident, as it can be seamlessly extended to diverse semi-structured biomedical websites, facilitating the extraction of various types of biomedical relations with ease.

EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models

Electronic health records (EHR) contain vast biomedical knowledge and are rich resources for developing precise medicine systems. However, due to privacy concerns, there are limited high-quality EHR data accessible to researchers hence hindering the advancement of methodologies. Recent research has explored using generative modelling methods to synthesize realistic EHR data, and most proposed methods are based on the generative adversarial network (GAN) and its variants for EHR synthesis. Although GAN-style methods achieved state-of-the-art performance in generating high-quality EHR data, such methods are hard to train and prone to mode collapse. Diffusion models are recently proposed generative modelling methods and set cutting-edge performance in image generation. The performance of diffusion models in realistic EHR synthesis is rarely explored. In this work, we explore whether the superior performance of diffusion models can translate to the domain of EHR synthesis and propose a novel EHR synthesis method named EHRDiff. Through comprehensive experiments, EHRDiff achieves new state-of-the-art performance for the quality of synthetic EHR data and can better protect private information in real training EHRs in the meanwhile.