Objective: To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV(PLWHs) seeking care, using NLP methods applied to a large collection of EHR clinical notes from a large integrated health system in the southeast United States. Methods: We identified a cohort of PLWHs from the UF Health IDR and performed topic modeling analysis using Latent Dirichlet Allocation to uncover stigma-related dimensions and related social and behavioral contexts. Domain experts created a seed list of HIV-related stigma keywords, then applied a snowball strategy to review notes for additional terms until saturation was reached iteratively. To identify more target topics, we tested three keyword-based filtering strategies. The detected topics were evaluated using three widely used metrics and manually reviewed by specialists. In addition, we conducted word frequency analysis and topic variation analysis among subgroups to examine differences across age and sex-specific demographics. Results: We identified 9140 PLWHs at UF Health and collected 2.9 million clinical notes. Through the iterative keyword approach, we generated a list of 91 keywords associated with HIV-related stigma. Topic modeling on sentences containing at least one keyword uncovered a wide range of topic themes, such as "Mental Health Concern, Stigma", "Treatment Refusal, Isolation", and "Substance Abuse". Topic variation analysis across age subgroups revealed substantial differences. Conclusion: Extracting and understanding the HIV-related stigma and associated social and behavioral circumstances from EHR clinical notes enables scalable, time-efficient assessment and overcoming the limitations of traditional questionnaires. Findings from this research provide actionable insights to inform patient care and interventions to improve HIV-care outcomes.

In the rapidly changing world of smart technology, searching for documents has become more challenging due to the rise of advanced language models. These models sometimes face difficulties, like providing inaccurate information, commonly known as "hallucination." This research focuses on addressing this issue through Retrieval-Augmented Generation (RAG), a technique that guides models to give accurate responses based on real facts. To overcome scalability issues, the study explores connecting user queries with sophisticated language models such as BERT and Orca2, using an innovative query optimization process. The study unfolds in three scenarios: first, without RAG, second, without additional assistance, and finally, with extra help. Choosing the compact yet efficient Orca2 7B model demonstrates a smart use of computing resources. The empirical results indicate a significant improvement in the initial language model's performance under RAG, particularly when assisted with prompts augmenters. Consistency in document retrieval across different encodings highlights the effectiveness of using language model-generated queries. The introduction of UMAP for BERT further simplifies document retrieval while maintaining strong results.

There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it maximizes the mean of a scalar outcome in a population of interest, e.g., symptom reduction. However, clinical and intervention scientists often must balance multiple and possibly competing outcomes, e.g., symptom reduction and the risk of an adverse event. One approach to precision medicine in this setting is to elicit a composite outcome which balances all competing outcomes; unfortunately, eliciting a composite outcome directly from patients is difficult without a high-quality instrument, and an expert-derived composite outcome may not account for heterogeneity in patient preferences. We propose a new paradigm for the study of precision medicine using observational data that relies solely on the assumption that clinicians are approximately (i.e., imperfectly) making decisions to maximize individual patient utility. Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite outcomes. The estimated composite outcomes and estimated optimal individualized treatment rule provide new insights into patient preference heterogeneity, clinician behavior, and the value of precision medicine in a given domain. We derive inference procedures for the proposed estimators under mild conditions and demonstrate their finite sample performance through a suite of simulation experiments and an illustrative application to data from a study of bipolar depression.

There are about 45 million people in the US alone with a mental illness, and those illnesses and their courses of treatment can vary tremendously. But there is something most of those people have in common: a smartphone. A startup founded in Palo Alto, California, by a trio of doctors, including the former director of the US National Institute of Mental Health, is trying to prove that our obsession with the technology in our pockets can help treat some of today's most intractable medical problems: depression, schizophrenia, bipolar disorder, post-traumatic stress disorder, and substance abuse. Mindstrong Health is using a smartphone app to collect measures of people's cognition and emotional health as indicated by how they use their phones. Once a patient installs Mindstrong's app, it monitors things like the way the person types, taps, and scrolls while using other apps.

There's been a lot of concern lately, by parents especially, about video game addiction. The World Health Organization has added the behavioral condition "gaming disorder" to their International Statistical Classification of Diseases and Related Health Problems. In 2013, the American Psychological Association (APA) designated gaming disorder as "a condition for further study." But even that provoked pushback. Akin to an addiction to heroin or alcohol, the proposed diagnostic criteria roughly tracked those for substance abuse, such as withdrawal, tolerance, a desire to stop, and negative impact on life activities.

Imagine being trapped in a building overrun with alien humanoids. Your task is to shoot your way out. As you're fighting for your life in this fantastical world, in the background are seemingly out-of-place graphic health warnings. "Don't drink and drive", reads a poster riddled with gun shots. "I'm just buzzed", says another, depicting yellow caution tape draped across the scene of a car accident.