Temporal information extraction is the task of identifying temporal entities in a text and relating them to each other. In medicine, electronic health records (EHRs) contain text that documents the sequence of events during an encounter with a patient, and sometimes the events prior to the encounter (e.g., psychosocial environment and history). Temporality is especially important for the specialty of psychiatry. In this work, we describe the updates to the guidelines that allowed us to create a corpus of temporally-annotated psychiatric discharge summaries and progress notes in English. These updated guidelines were used to create a corpus of over 18,000 events, 2,200 time expressions, and 13,000 temporal relations. Temporal information extraction performance with a baseline system trained on non-psychiatric data obtains an F1 score of 0.152 on relation extraction, indicating the importance of this new dataset for making progress on temporal information extraction in the psychiatric domain.

Recent progress in large language models (LLMs) has enabled the automated processing of lengthy documents even without supervised training on a task-specific dataset.Yet, their zero-shot performance in complex tasks as opposed to straightforward information extraction tasks remains suboptimal.One feasible approach for tasks with lengthy, complex input is to first summarize the document and then apply supervised fine-tuning to the summary.However, the summarization process inevitably results in some loss of information.In this study we present a method for processing the summaries of long documents aimed to capture different important aspects of the original document.We hypothesize that LLM summaries generated with different aspect-oriented prompts contain different information signals, and we propose methods to measure these differences.We introduce approaches to effectively integrate signals from these different summaries for supervised training of transformer models.We validate our hypotheses on a high-impact task -30-day readmission prediction from a psychiatric discharge -using real-world data from four hospitals, and show that our proposed method increases the prediction performance for the complex task of predicting patient outcome.

MOTIVATION: In the training of predictive models using high-dimensional genomic data, multiple studies' worth of data are often combined to increase sample size and improve generalizability. A drawback of this approach is that there may be different sets of features measured in each study due to variations in expression measurement platform or technology. It is often common practice to work only with the intersection of features measured in common across all studies, which results in the blind discarding of potentially useful feature information that is measured in individual or subsets of studies. RESULTS: We characterize the loss in predictive performance incurred by using only the intersection of feature information available across all studies when training predictors using gene expression data from microarray and sequencing datasets. We study the properties of linear and polynomial regression for imputing discarded features and demonstrate improvements in the external performance of prediction functions through simulation and in gene expression data collected on breast cancer patients. To improve this process, we propose a pairwise strategy that applies any imputation algorithm to two studies at a time and averages imputed features across pairs. We demonstrate that the pairwise strategy is preferable to first merging all datasets together and imputing any resulting missing features. Finally, we provide insights on which subsets of intersected and study-specific features should be used so that missing-feature imputation best promotes cross-study replicability. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/YujieWuu/Pairwise_imputation. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

The development of social media has made the dissemination of information more convenient, which means part of people can gather more information and then change their behaviors. In other words, information spreading would effect disease-control. To investigate the effect of information on vaccination game, an epidemic SEIR/VA (susceptible-exposed-infected-removed-vaccinated-avoided) model and a combined strategy-updating rule are proposed. Besides, temporary protective measures triggered by information are used to reflect the role of information dissemination. Based on the mean field approximation theory, differential equations are established to study the effect of information and vaccination coverage as well as vaccination efficacy under mandatory policy. Then, considering payoff and peer pressure, differential equations are developed to characterize the change of vaccination rate over time and the results of the final epidemic size as well as mean social payoff corresponding to different vaccine cost and efficiency. It can be observed that individuals with information obtained become more sensitive to the price and utility of vaccines. Scilicet, only when the vaccine is effective and cheap enough, information spreading can promote individual to take vaccination, which in turn eliminates the disease. Otherwise, as information spreads, people become more reluctant to get vaccinated. In other words, information dissemination is a double-edged sword.

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA's model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA's performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA's utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.