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Background: Sexual and Gender Diverse Youth (SGDY) are at increased risk for suicide due to unique experiences including discrimination, family or friend rejection, and low positive affect. Peer mentors (PMs) may offer a unique opportunity for intervention but are underutilized for suicide prevention among SGDY., Objective: Little is known about the training needed for PMs when working with SGDY at risk for suicide. We developed an intervention, Supporting Transitions to Adulthood and Reducing Suicide (STARS), to improve suicide prevention among SGDY and increase social support, coping, and positive effects. PMs were trained by a licensed clinical therapist and provided a manual. PMs meet virtually for 6 weeks, providing social support, strategies to diminish the impact of discrimination, connection to safe spaces, and reinforcement of intentions to use Safety Plans with mentees., Methods: To understand PMs' experiences in their role, including distress, fidelity to the manual, and perceptions of feasibility and acceptability of STARS and mentees' Safety Plan, we collected survey data from mentees and PMs as well as in-depth interviews with PMs after the completion of the intervention., Results: As of September 2024, all peer mentees (N=64) have completed the study and all PMs have finished providing sessions for peer mentees. PMs (n=5) reported overall high comfort (8.52) and low distress (1.93) during sessions. All 5 PMs had high fidelity (>90%) to the PM intervention training. All 5 PMs reported high feasibility (17.50), acceptability (20), and appropriateness (20) of the STARS intervention. Mentees (n=27) reported high confidence ratings (3.54) in speaking with their PMs., Conclusions: Peer mentorship for SGDY who are at risk for suicide was feasible and acceptable by PMs and mentees alike. PMs reported that they felt comfortable and confident during the sessions. Mentees also reported confidence in working with their PMs. Future research should explore the optimal strategies to support PMs and mentees as they engage in suicide prevention work as well as incorporate feedback from the PMs in this study to ensure optimal outcomes., Trial Registration: ClinicalTrials.gov NCT05018143; https://clinicaltrials.gov/study/NCT05018143., International Registered Report Identifier (irrid): RR2-10.2196/48177., (©Jennifer T Tran, Jessica Webster, James R Wolfe, Jennifer Ben Nathan, Lindiwe Mayinja, Marin Kautz, Maria A Oquendo, Gregory K Brown, David Mandell, Danielle Mowery, José A Bauermeister, Lily A Brown. Originally published in JMIR Formative Research (https://formative.jmir.org), 29.01.2025.)

Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging., Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media., Methods: We proposed a two-layer RAG framework for query-focused answer generation and evaluated a proof of concept for the framework in the context of query-focused summary generation from social media forums, focusing on emerging drug-related information. Our modular framework generates individual summaries followed by an aggregated summary to answer medical queries from large amounts of user-generated social media data in an efficient manner. We compared the performance of a quantized large language model (Nous-Hermes-2-7B-DPO), deployable in low-resource settings, with GPT-4. For this proof-of-concept study, we used user-generated data from Reddit to answer clinicians' questions on the use of xylazine and ketamine., Results: Our framework achieves comparable median scores in terms of relevance, length, hallucination, coverage, and coherence when evaluated using GPT-4 and Nous-Hermes-2-7B-DPO, evaluated for 20 queries with 76 samples. There was no statistically significant difference between GPT-4 and Nous-Hermes-2-7B-DPO for coverage (Mann-Whitney U=733.0; n 1 =37; n 2 =39; P=.89 two-tailed), coherence (U=670.0; n 1 =37; n 2 =39; P=.49 two-tailed), relevance (U=662.0; n 1 =37; n 2 =39; P=.15 two-tailed), length (U=672.0; n 1 =37; n 2 =39; P=.55 two-tailed), and hallucination (U=859.0; n 1 =37; n 2 =39; P=.01 two-tailed). A statistically significant difference was noted for the Coleman-Liau Index (U=307.5; n 1 =20; n 2 =16; P<.001 two-tailed)., Conclusions: Our RAG framework can effectively answer medical questions about targeted topics and can be deployed in resource-constrained settings., (©Sudeshna Das, Yao Ge, Yuting Guo, Swati Rajwal, JaMor Hairston, Jeanne Powell, Drew Walker, Snigdha Peddireddy, Sahithi Lakamana, Selen Bozkurt, Matthew Reyna, Reza Sameni, Yunyu Xiao, Sangmi Kim, Rasheeta Chandler, Natalie Hernandez, Danielle Mowery, Rachel Wightman, Jennifer Love, Anthony Spadaro, Jeanmarie Perrone, Abeed Sarker. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.01.2025.)

Background: Atopic dermatitis (AD) is a chronic skin condition that millions of people around the world live with each day. Performing research into identifying the causes and treatment for this disease has great potential to provide benefits for these individuals. However, AD clinical trial recruitment is not a trivial task due to the variance in diagnostic precision and phenotypic definitions leveraged by different clinicians, as well as the time spent finding, recruiting, and enrolling patients by clinicians to become study participants. Thus, there is a need for automatic and effective patient phenotyping for cohort recruitment., Objective: This study aims to present an approach for identifying patients whose electronic health records suggest that they may have AD., Methods: We created a vectorized representation of each patient and trained various supervised machine learning methods to classify when a patient has AD. Each patient is represented by a vector of either probabilities or binary values, where each value indicates whether they meet a different criteria for AD diagnosis., Results: The most accurate AD classifier performed with a class-balanced accuracy of 0.8036, a precision of 0.8400, and a recall of 0.7500 when using XGBoost (Extreme Gradient Boosting)., Conclusions: Creating an automated approach for identifying patient cohorts has the potential to accelerate, standardize, and automate the process of patient recruitment for AD studies; therefore, reducing clinician burden and informing the discovery of better treatment options for AD., (©Andrew Wang, Rachel Fulton, Sy Hwang, David J Margolis, Danielle Mowery. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.01.2024.)

Background: Suicide attempts and suicide death disproportionately affect sexual and gender minority emerging adults (age 18-24 years). However, suicide prevention strategies tailored for emerging adult sexual and gender minority (EA-SGM) groups are not widely available. The Safety Planning Intervention (SPI) has strong evidence for reducing the risk for suicide in the general population, but it is unclear how best to support EA-SGM groups in their use of a safety plan. Our intervention (Supporting Transitions to Adulthood and Reducing Suicide [STARS]) builds on content from an existing life skills mobile app for adolescent men who have sex with men (iREACH) and seeks to target core risk factors for suicide among EA-SGM groups, namely, positive affect, discrimination, and social disconnection. The mobile app is delivered to participants randomized to STARS alongside 6 peer mentoring sessions to support the use of the safety plan and other life skills from the app to ultimately reduce suicide risk., Objective: We will pilot-test the combination of peer mentoring alongside an app-based intervention (STARS) designed to reduce suicidal ideation and behaviors. STARS will include suicide prevention content and will target positive affect, discrimination, and social support. After an in-person SPI with a clinician, STARS users can access content and activities to increase their intention to use SPI and overcome obstacles to its use. EA-SGM groups will be randomized to receive either SPI alone or STARS and will be assessed for 6 months., Methods: Guided by the RE-AIM (reach, efficacy, adoption, implementation, and maintenance) framework, we will recruit and enroll a racially and ethnically diverse sample of 60 EA-SGM individuals reporting past-month suicidal ideation. Using a type-1 effectiveness-implementation hybrid design, participants will be randomized to receive SPI (control arm) or to receive SPI alongside STARS (intervention arm). We will follow the participants for 6 months, with evaluations at 2, 4, and 6 months. Preliminary effectiveness outcomes (suicidal ideation and behavior) and hypothesized mechanisms of change (positive affect, coping with discrimination, and social support) will serve as our primary outcomes. Secondary outcomes include key implementation indicators, including participants' willingness and adoption of SPI and STARS and staff's experiences with delivering the program., Results: Study activities began in September 2021 and are ongoing. The study was approved by the institutional review board of the University of Pennsylvania (protocol number 849500). Study recruitment began on October 14, 2022., Conclusions: This project will be among the first tailored, mobile-based interventions for EA-SGM groups at risk for suicide. This project is responsive to the documented gaps for this population: approaches that address chosen family, focus on a life-course perspective, web approaches, and focus on health equity and provision of additional services relevant to sexual and gender minority youth., Trial Registration: ClinicalTrials.gov NCT05018143; https://classic.clinicaltrials.gov/ct2/show/NCT05018143., International Registered Report Identifier (irrid): DERR1-10.2196/48177., (©Lily A Brown, Jessica L Webster, Jennifer T Tran, James R Wolfe, Jesse Golinkoff, Esha Patel, Amanda C Arcomano, Jennifer Ben Nathan, Alexander Azat O'Connor, Yiqin Zhu, Maria Oquendo, Gregory K Brown, David Mandell, Danielle Mowery, José A Bauermeister. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 29.09.2023.)

Background: Free-text communication between patients and providers plays an increasing role in chronic disease management, through platforms varying from traditional health care portals to novel mobile messaging apps. These text data are rich resources for clinical purposes, but their sheer volume render them difficult to manage. Even automated approaches, such as natural language processing, require labor-intensive manual classification for developing training data sets. Automated approaches to organizing free-text data are necessary to facilitate use of free-text communication for clinical care., Objective: The aim of this study was to apply unsupervised learning approaches to (1) understand the types of topics discussed and (2) learn medication-related intents from messages sent between patients and providers through a bidirectional text messaging system for managing participant blood pressure (BP)., Methods: This study was a secondary analysis of deidentified messages from a remote, mobile, text-based employee hypertension management program at an academic institution. We trained a latent Dirichlet allocation (LDA) model for each message type (ie, inbound patient messages and outbound provider messages) and identified the distribution of major topics and significant topics (probability >.20) across message types. Next, we annotated all medication-related messages with a single medication intent. Then, we trained a second medication-specific LDA (medLDA) model to assess how well the unsupervised method could identify more fine-grained medication intents. We encoded each medication message with n-grams (n=1-3 words) using spaCy, clinical named entities using Stanza, and medication categories using MedEx; we then applied chi-square feature selection to learn the most informative features associated with each medication intent., Results: In total, 253 participants and 5 providers engaged in the program, generating 12,131 total messages: 46.90% (n=5689) patient messages and 53.10% (n=6442) provider messages. Most patient messages corresponded to BP reporting, BP encouragement, and appointment scheduling; most provider messages corresponded to BP reporting, medication adherence, and confirmatory statements. Most patient and provider messages contained 1 topic and few contained more than 3 topics identified using LDA. In total, 534 medication messages were annotated with a single medication intent. Of these, 282 (52.8%) were patient medication messages: most referred to the medication request intent (n=134, 47.5%). Most of the 252 (47.2%) provider medication messages referred to the medication question intent (n=173, 68.7%). Although the medLDA model could identify a majority intent within each topic, it could not distinguish medication intents with low prevalence within patient or provider messages. Richer feature engineering identified informative lexical-semantic patterns associated with each medication intent class., Conclusions: LDA can be an effective method for generating subgroups of messages with similar term usage and facilitating the review of topics to inform annotations. However, few training cases and shared vocabulary between intents precludes the use of LDA for fully automated, deep, medication intent classification., International Registered Report Identifier (irrid): RR2-10.1101/2021.12.23.21268061., (©Anahita Davoudi, Natalie S Lee, ThaiBinh Luong, Timothy Delaney, Elizabeth Asch, Krisda Chaiyachati, Danielle Mowery. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.06.2022.)

Background: Natural language processing (NLP) offers significantly faster variable extraction compared to traditional human extraction but cannot interpret complicated notes as well as humans can. Thus, we hypothesized that an "NLP-assisted" extraction system, which uses humans for complicated notes and NLP for uncomplicated notes, could produce faster extraction without compromising accuracy., Objective: The aim of this study was to develop and pilot an NLP-assisted extraction system to leverage the strengths of both human and NLP extraction of prostate cancer Gleason scores., Methods: We collected all available clinical and pathology notes for prostate cancer patients in an unselected academic biobank cohort. We developed an NLP system to extract prostate cancer Gleason scores from both clinical and pathology notes. Next, we designed and implemented the NLP-assisted extraction system algorithm to categorize notes into "uncomplicated" and "complicated" notes. Uncomplicated notes were assigned to NLP extraction and complicated notes were assigned to human extraction. We randomly reviewed 200 patients to assess the accuracy and speed of our NLP-assisted extraction system and compared it to NLP extraction alone and human extraction alone., Results: Of the 2051 patients in our cohort, the NLP system extracted a prostate surgery Gleason score from 1147 (55.92%) patients and a prostate biopsy Gleason score from 1624 (79.18%) patients. Our NLP-assisted extraction system had an overall accuracy rate of 98.7%, which was similar to the accuracy of human extraction alone (97.5%; P=.17) and significantly higher than the accuracy of NLP extraction alone (95.3%; P<.001). Moreover, our NLP-assisted extraction system reduced the workload of human extractors by approximately 95%, resulting in an average extraction time of 12.7 seconds per patient (vs 256.1 seconds per patient for human extraction alone)., Conclusions: We demonstrated that an NLP-assisted extraction system was able to achieve much faster Gleason score extraction compared to traditional human extraction without sacrificing accuracy., (©Shun Yu, Anh Le, Emily Feld, Emily Schriver, Peter Gabriel, Abigail Doucette, Vivek Narayan, Michael Feldman, Lauren Schwartz, Kara Maxwell, Danielle Mowery. Originally published in JMIR Cancer (https://cancer.jmir.org), 02.07.2021.)

Background: Scientists are developing new computational methods and prediction models to better clinically understand COVID-19 prevalence, treatment efficacy, and patient outcomes. These efforts could be improved by leveraging documented COVID-19-related symptoms, findings, and disorders from clinical text sources in an electronic health record. Word embeddings can identify terms related to these clinical concepts from both the biomedical and nonbiomedical domains, and are being shared with the open-source community at large. However, it's unclear how useful openly available word embeddings are for developing lexicons for COVID-19-related concepts., Objective: Given an initial lexicon of COVID-19-related terms, this study aims to characterize the returned terms by similarity across various open-source word embeddings and determine common semantic and syntactic patterns between the COVID-19 queried terms and returned terms specific to the word embedding source., Methods: We compared seven openly available word embedding sources. Using a series of COVID-19-related terms for associated symptoms, findings, and disorders, we conducted an interannotator agreement study to determine how accurately the most similar returned terms could be classified according to semantic types by three annotators. We conducted a qualitative study of COVID-19 queried terms and their returned terms to detect informative patterns for constructing lexicons. We demonstrated the utility of applying such learned synonyms to discharge summaries by reporting the proportion of patients identified by concept among three patient cohorts: pneumonia (n=6410), acute respiratory distress syndrome (n=8647), and COVID-19 (n=2397)., Results: We observed high pairwise interannotator agreement (Cohen kappa) for symptoms (0.86-0.99), findings (0.93-0.99), and disorders (0.93-0.99). Word embedding sources generated based on characters tend to return more synonyms (mean count of 7.2 synonyms) compared to token-based embedding sources (mean counts range from 2.0 to 3.4). Word embedding sources queried using a qualifier term (eg, dry cough or muscle pain) more often returned qualifiers of the similar semantic type (eg, "dry" returns consistency qualifiers like "wet" and "runny") compared to a single term (eg, cough or pain) queries. A higher proportion of patients had documented fever (0.61-0.84), cough (0.41-0.55), shortness of breath (0.40-0.59), and hypoxia (0.51-0.56) retrieved than other clinical features. Terms for dry cough returned a higher proportion of patients with COVID-19 (0.07) than the pneumonia (0.05) and acute respiratory distress syndrome (0.03) populations., Conclusions: Word embeddings are valuable technology for learning related terms, including synonyms. When leveraging openly available word embedding sources, choices made for the construction of the word embeddings can significantly influence the words learned., (©Soham Parikh, Anahita Davoudi, Shun Yu, Carolina Giraldo, Emily Schriver, Danielle Mowery. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 22.02.2021.)

This paper describes a continuing initiative of the International Society for Disease Surveillance designed to bring together public health practitioners and analytics solution developers from both academia and industry. Funded by the Defense Threat Reduction Agency, a series of consultancies have been conducted on a range of topics of pressing concern to public health (e.g. developing methods to enhance prediction of asthma exacerbation, developing tools for asyndromic surveillance from chief complaints). The topic of this final consultancy, conducted at the University of Utah in January 2017, is focused on defining a roadmap for the development of algorithms, tools, and datasets for improving the capabilities of text processing algorithms to identify negated terms (i.e. negation detection ) in free-text chief complaints and triage reports.

Background: With a lifetime prevalence of 16.2%, major depressive disorder is the fifth biggest contributor to the disease burden in the United States., Objective: The aim of this study, building on previous work qualitatively analyzing depression-related Twitter data, was to describe the development of a comprehensive annotation scheme (ie, coding scheme) for manually annotating Twitter data with Diagnostic and Statistical Manual of Mental Disorders, Edition 5 (DSM 5) major depressive symptoms (eg, depressed mood, weight change, psychomotor agitation, or retardation) and Diagnostic and Statistical Manual of Mental Disorders, Edition IV (DSM-IV) psychosocial stressors (eg, educational problems, problems with primary support group, housing problems)., Methods: Using this annotation scheme, we developed an annotated corpus, Depressive Symptom and Psychosocial Stressors Acquired Depression, the SAD corpus, consisting of 9300 tweets randomly sampled from the Twitter application programming interface (API) using depression-related keywords (eg, depressed, gloomy, grief). An analysis of our annotated corpus yielded several key results., Results: First, 72.09% (6829/9473) of tweets containing relevant keywords were nonindicative of depressive symptoms (eg, "we're in for a new economic depression"). Second, the most prevalent symptoms in our dataset were depressed mood and fatigue or loss of energy. Third, less than 2% of tweets contained more than one depression related category (eg, diminished ability to think or concentrate, depressed mood). Finally, we found very high positive correlations between some depression-related symptoms in our annotated dataset (eg, fatigue or loss of energy and educational problems; educational problems and diminished ability to think)., Conclusions: We successfully developed an annotation scheme and an annotated corpus, the SAD corpus, consisting of 9300 tweets randomly-selected from the Twitter application programming interface using depression-related keywords. Our analyses suggest that keyword queries alone might not be suitable for public health monitoring because context can change the meaning of keyword in a statement. However, postprocessing approaches could be useful for reducing the noise and improving the signal needed to detect depression symptoms using social media., (©Danielle Mowery, Hilary Smith, Tyler Cheney, Greg Stoddard, Glen Coppersmith, Craig Bryan, Mike Conway. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 28.02.2017.)