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View article: Agentic AI in Healthcare: A Comprehensive Survey of Foundations, Taxonomy, and Applications
Agentic AI in Healthcare: A Comprehensive Survey of Foundations, Taxonomy, and Applications Open
View article: Amniotic Fluid Index and its Impact on Mode of Delivery and Fetomaternal Outcome in Term PROM
Amniotic Fluid Index and its Impact on Mode of Delivery and Fetomaternal Outcome in Term PROM Open
Background: The increasing prevalence of term premature rupture of membranes (PROM) poses significant challenges to obstetrical management, necessitating enhanced predictive methodologies for optimal clinical outcomes. Objectives: Primary …
View article: Elucidating Delays in Illness Recognition, Healthcare Seeking, and Healthcare Provision for Stillbirths and Neonatal Deaths in Seven Low- and Middle-Income Countries
Elucidating Delays in Illness Recognition, Healthcare Seeking, and Healthcare Provision for Stillbirths and Neonatal Deaths in Seven Low- and Middle-Income Countries Open
An estimated 1.9 million stillbirths and 2.3 million neonatal deaths occurred globally in 2023, disproportionately clustered in sub-Saharan Africa and South Asia. We aimed to determine the frequency of delays in illness recognition, health…
View article: Application of Natural Language Processing for Chronic Pain Research: A Scoping Review (Preprint)
Application of Natural Language Processing for Chronic Pain Research: A Scoping Review (Preprint) Open
BACKGROUND Chronic pain poses a significant global health burden, with pertinent contextual information relevant to it encapsulated in free text format across sources such as electronic health records (EHRs), published literature, and soc…
View article: Leveraging A Stacking Ensemble Model for Accurate Depression Prediction and Diagnosis Across All Ages
Leveraging A Stacking Ensemble Model for Accurate Depression Prediction and Diagnosis Across All Ages Open
Depression is a serious mental health issue affecting people of all ages, with early detection being crucial for timely treatment. In this study, we developed a highly accurate machine‐learning model using a stacking ensemble technique to …
View article: Characterization of Reddit Posts About Xylazine-Associated Wounds: Qualitative Study
Characterization of Reddit Posts About Xylazine-Associated Wounds: Qualitative Study Open
Background Xylazine has been associated with skin wounds. The rising prevalence of xylazine and its debated role in wound causation have sparked concerns among public health professionals, medical experts, and people who use drugs. Objecti…
View article: Automating inductive thematic analyses of health content using large language models: a proof-of-concept study using social media data
Automating inductive thematic analyses of health content using large language models: a proof-of-concept study using social media data Open
Objectives Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive, domain-specific expertise. We evaluated the feasibility of using LLMs to replicate expert-driven thematic analysis …
View article: Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models
Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models Open
View article: Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods
Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods Open
Social isolation and loneliness, which have been increasing in recent years strongly contribute toward suicide rates. Although social isolation and loneliness are not currently recorded within the US National Violent Death Reporting System…
View article: Automated Thematic Analyses Using LLMs: Xylazine Wound Management Social Media Chatter Use Case
Automated Thematic Analyses Using LLMs: Xylazine Wound Management Social Media Chatter Use Case Open
Background Large language models (LLMs) face challenges in inductive thematic analysis, a task requiring deep interpretive and domain-specific expertise. We evaluated the feasibility of using LLMs to replicate expert-driven thematic analys…
View article: Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods
Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods Open
Social isolation and loneliness, which have been increasing in recent years strongly contribute toward suicide rates. Although social isolation and loneliness are not currently recorded within the US National Violent Death Reporting System…
View article: Evaluation of Facebook as a Longitudinal Data Source for Parkinson’s Disease Insights
Evaluation of Facebook as a Longitudinal Data Source for Parkinson’s Disease Insights Open
Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder with a prolonged prodromal phase and progressive symptom burden. Traditional monitoring relies on clinical visits post-diagnosis, limiting the ability to captu…
View article: Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?
Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter? Open
This archive contains over 3 billion Tweet IDs associated with the paper:"Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?" The data provides:X (Twitter) IDs of posts…
View article: Leveraging large language models and traditional machine learning ensembles for ADHD detection from narrative transcripts
Leveraging large language models and traditional machine learning ensembles for ADHD detection from narrative transcripts Open
Despite rapid advances in large language models (LLMs), their integration with traditional supervised machine learning (ML) techniques that have proven applicability to medical data remains underexplored. This is particularly true for psyc…
View article: Evaluation of Facebook as a Longitudinal Data Source for Parkinson’s Disease Insights
Evaluation of Facebook as a Longitudinal Data Source for Parkinson’s Disease Insights Open
Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder with a prolonged prodromal phase and progressive symptom burden. Traditional monitoring relies on clinical visits post-diagnosis, limiting the ability to captu…
View article: The Digital Narrative: Chronic Pain Self-Management Insights from Reddit in Systemic Lupus Erythematosus
The Digital Narrative: Chronic Pain Self-Management Insights from Reddit in Systemic Lupus Erythematosus Open
Chronic pain is a pervasive and complex symptom experienced by patients with Systemic Lupus Erythematosus (SLE), often impacting their quality of life. Traditional data sources, such as clinical records and surveys, provide valuable insigh…
View article: Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks
Benchmarking Open-Source Large Language Models on Healthcare Text Classification Tasks Open
The application of large language models (LLMs) to healthcare information extraction has emerged as a promising approach. This study evaluates the classification performance of five open-source LLMs: GEMMA-3-27B-IT, LLAMA3-70B, LLAMA4-109B…
View article: Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records
Machine Learning and Natural Language Processing to Improve Classification of Atrial Septal Defects in Electronic Health Records Open
Background International Classification of Disease (ICD) codes can accurately identify patients with certain congenital heart defects (CHDs). In ICD‐defined CHD data sets, the code for secundum atrial septal defect (ASD) is the most common…
View article: “Strong Black Women”: Prescription Stimulant Use as a Site of Resistance in the Context of Black Women’s Labor and Negative Images of Black Womanhood
“Strong Black Women”: Prescription Stimulant Use as a Site of Resistance in the Context of Black Women’s Labor and Negative Images of Black Womanhood Open
Black women are experiencing rising rates of mortality from stimulant-involved overdose. Yet research on the reasons for use of stimulants among Black women are rare. The present study evaluates how the war on drugs has used negative stere…
View article: Building community through data: the value of a researcher driven open science ecosystem
Building community through data: the value of a researcher driven open science ecosystem Open
View article: Building Community Through Data: The value of a Researcher Driven Open Science Ecosystem
Building Community Through Data: The value of a Researcher Driven Open Science Ecosystem Open
Exponential scientific data growth presents challenges and opportunities for addressing complex public health issues like the opioid epidemic and chronic pain management. Despite the vast amount of research conducted globally, many dataset…
View article: “Strong Black Women”: Prescription Stimulant Use as a Site of Resistance in the Context of Black Women’s Labor and Negative Images of Black Womanhood
“Strong Black Women”: Prescription Stimulant Use as a Site of Resistance in the Context of Black Women’s Labor and Negative Images of Black Womanhood Open
Black women are experiencing rising rates of mortality from stimulant-involved overdose. Yet research on the reasons for use of stimulants among Black women are rare. The present study evaluates how the war on drugs has used negative stere…
View article: “Strong Black Women”: Prescription Stimulant Use as a Site of Resistance in the Context of Black Women’s Labor and Negative Images of Black Womanhood
“Strong Black Women”: Prescription Stimulant Use as a Site of Resistance in the Context of Black Women’s Labor and Negative Images of Black Womanhood Open
Black women are experiencing rising rates of mortality from stimulant-involved overdose. Yet research on the reasons for use of stimulants among Black women are rare. The present study evaluates how the war on drugs has used negative stere…
View article: Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Protocol for a Systematic Review
Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Protocol for a Systematic Review Open
Background In recent years, the intersection of natural language processing (NLP) and public health has opened innovative pathways for investigating social determinants of health (SDOH) in textual datasets. Despite the promise of NLP in th…
View article: CARE-SD: classifier-based analysis for recognizing provider stigmatizing and doubt marker labels in electronic health records: model development and validation
CARE-SD: classifier-based analysis for recognizing provider stigmatizing and doubt marker labels in electronic health records: model development and validation Open
Objective To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques. Materials and Methods We first created a lexicon and regular exp…
View article: “I Been Taking Adderall Mixing it With Lean, Hope I Don’t Wake Up Out My Sleep”: Harnessing Twitter to Understand Nonmedical Prescription Stimulant Use among Black Women and Men Subscribers
“I Been Taking Adderall Mixing it With Lean, Hope I Don’t Wake Up Out My Sleep”: Harnessing Twitter to Understand Nonmedical Prescription Stimulant Use among Black Women and Men Subscribers Open
Black women and men outpace other races for stimulant-involved overdose mortality despite lower lifetime use. Growth in mortality from prescription stimulant medications is increasing in tandem with prescribing patterns for these medicatio…
View article: Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study Open
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,…
View article: Evaluating large language models for health-related text classification tasks with public social media data
Evaluating large language models for health-related text classification tasks with public social media data Open
Objectives Large language models (LLMs) have demonstrated remarkable success in natural language processing (NLP) tasks. This study aimed to evaluate their performances on social media-based health-related text classification tasks. Materi…
View article: Natural Language Processing for Digital Health in the Era of Large Language Models
Natural Language Processing for Digital Health in the Era of Large Language Models Open
Summary Objectives: Large language models (LLMs) are revolutionizing the natural language pro-cessing (NLP) landscape within healthcare, prompting the need to synthesize the latest ad-vancements and their diverse medical applications. We a…
View article: MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction
MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction Open
Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, …