Publications by authors named "Graciela Gonzalez Hernandez"
Background: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their effectiveness and is a key focus of public health initiatives worldwide. In cases of sinusitis, among the most common reasons for antibiotic prescriptions in children, healthcare providers must distinguish between bacterial and viral causes based on clinical signs and symptoms. However, due to the overlap between symptoms of acute sinusitis and viral upper respiratory infections, antibiotics are often over-prescribed.
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- Stigmatizing language in electronic health records (EHRs) can perpetuate negative stereotypes and undermine trust for patients from minoritized groups, especially related to their clinical histories.* -
- This study aimed to analyze the prevalence of doubt-casting language in admission notes, specifically looking at how it varies based on patient race and ethnicity.* -
- Utilizing natural language processing on over 54,000 admission notes from a diverse patient population, the researchers focused on the use of certain linguistic terms that reflect the degree of certainty about patients' reports of their health experiences.*
The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.
JMIR Public Health Surveill
September 2024
Article Synopsis
- Adverse drug events (AEs) are a major public health issue, contributing to hospitalizations and affecting patients' quality of life, with social media being explored as a potential source for tracking these events.
- This study investigates how effective social media analysis is for detecting AEs compared to traditional data sources like clinical literature and reporting systems.
- The research involved reviewing numerous studies that utilized social media for AE detection, assessing methods, data sources, and the relevance of findings in relation to existing data.
Article Synopsis
- The mpox outbreak in the U.S. led to over 32,000 cases and 58 deaths from May 2022 to March 2024, raising concerns about stigma and access to healthcare for sexual minority men and gender-diverse individuals.
- To address the lack of SMMGD perspectives in existing literature, this study aimed to gather their views on public health communication regarding mpox, focusing on inclusivity and equity.
- An analysis of 8,688 mpox-related tweets from SMMGD users identified 11 key discussion topics, with significant focus on health activism and vaccination discussions, as well as the impact of COVID-19 and public health responses.
Patients with chronic diseases have increasingly turned to social media to discuss symptoms and share the challenges they face with disease management. The primary aim of this study is to use naturally occurring data from X (formerly known as Twitter) to identify barriers to care faced by individuals affected by eosinophilic esophagitis (EoE). For this qualitative study, the X application programming interface with academic research access was used to search for posts that referenced EoE between 1 January 2019 and 10 August 2022.
View Article and Find Full Text PDFObjectives: To synthesize discussions among sexual minority men and gender diverse (SMMGD) individuals on mpox, given limited representation of SMMGD voices in existing mpox literature.
Methods: BERTopic (a topic modeling technique) was employed with human validations to analyze mpox-related tweets ( = 8,688; October 2020-September 2022) from 2,326 self-identified SMMGD individuals in the U.S.
Article Synopsis
- Researchers annotated over 9,700 tweets from users reporting their pregnancies to analyze health trends.
- They developed deep neural network classifiers that achieved an impressive F-score of 0.93 for identifying specific childhood health conditions associated with pregnancy exposures.
- The study highlights Twitter's potential as a valuable tool for assessing relationships between pregnancy factors and childhood health outcomes on a broad scale.
Article Synopsis
- The text discusses the significance of real-world data from social media, particularly Twitter, for health and social science research, emphasizing the need to identify user demographics like age and gender to evaluate research representativeness.
- It outlines the objective of a scoping review that summarizes existing literature on methods for predicting Twitter users' age and gender, noting the challenges involved in this process.
- The review analyzed 684 studies, finding 74 relevant ones that discussed age or gender prediction, revealing a predominance in gender prediction methods, with varying levels of performance in accuracy for both age and gender classifications.
Objective: Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence.
View Article and Find Full Text PDFFree-text information represents a valuable resource for epidemiological surveillance. Its unstructured nature, however, presents significant challenges in the extraction of meaningful information. This study presents a deep learning model for classifying otitis using pediatric medical records.
View Article and Find Full Text PDFObjective: The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results.
Methods: The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events).
Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented.
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- Preterm birth, defined as delivery before 37 weeks, is a critical global health issue, contributing to high neonatal and infant mortality rates, especially in the U.S., with recent studies suggesting a link between COVID-19 infection during pregnancy and increased preterm birth risk.
- This study employed machine learning and natural language processing to analyze Twitter data from pregnant women to determine the correlation between the timing of COVID-19 infection during pregnancy and the incidence of preterm births.
- The analysis identified 298 Twitter users who reported COVID-19 infections and their birth outcomes, with a distribution of cases across the first, second, and third trimesters, and found a notable percentage of preterm births among those infected.*
Article Synopsis
- Hypertension is a major global health issue, marked by high rates of medication nonadherence, which traditional surveys struggle to accurately assess due to various biases.
- The study leveraged patient reviews from WebMD to analyze reasons for changes in angiotensin receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs) using natural language processing.
- Out of 343,459 reviews, the analysis revealed that a significant majority of users reported adverse events—primarily musculoskeletal issues for ARBs and respiratory problems for ACEIs—as the main reasons for adjusting their medications.
Article Synopsis
- * The latest iteration included five tasks across platforms like Twitter and Reddit, covering topics such as COVID-19, therapies, and drug-related events in both English and Spanish, with 29 teams participating from 18 countries.
- * The top systems in competitions utilized advanced deep learning techniques, particularly pre-trained transformer models, and a dataset of over 61,000 social media posts will be available for future research.
Article Synopsis
- Accurate documentation of phenotypes in electronic health records (EHR) is crucial for genetic diagnosis, but current variations in reporting hinder computational analysis and existing NLP methods are not fully trained on EHR data.
- A new system called PhenoID was developed at the Children's Hospital of Philadelphia, which includes a manually annotated corpus of over 3,000 dysmorphology observations aligned with the Human Phenotype Ontology (HPO) to enhance phenotype extraction from clinical notes.
- PhenoID outperformed prior methods with a performance score of 0.717, highlighting the potential of transformer-based models for extracting genetic phenotypes, though it also revealed issues with the HPO terminology and understanding by the models.
Article Synopsis
- * Natural language processing (NLP) methods can significantly streamline the extraction process, particularly as the COVID-19 pandemic highlighted gaps in essential data, such as demographics and clinical outcomes in genomic records.
- * The development of automated pipelines using machine learning and NLP will allow for better identification of key patient characteristics from relevant articles, enhancing the richness of data available for epidemiological studies.
Background: Adverse drug events (ADEs) are a considerable public health burden resulting in disability, hospitalization, and death. Even those ADEs deemed nonserious can severely impact a patient's quality of life and adherence to intervention. Monitoring medication safety, however, is challenging.
View Article and Find Full Text PDFBackground: There has been an unprecedented effort to sequence the SARS-CoV-2 virus and examine its molecular evolution. This has been facilitated by the availability of publicly accessible databases, the Global Initiative on Sharing All Influenza Data (GISAID) and GenBank, which collectively hold millions of SARS-CoV-2 sequence records. Genomic epidemiology, however, seeks to go beyond phylogenetic analysis by linking genetic information to patient characteristics and disease outcomes, enabling a comprehensive understanding of transmission dynamics and disease impact.
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- SSRIs are widely prescribed for mental health issues, but many patients stop taking them, making it crucial to understand why.
- A study analyzed online drug reviews to find out the reasons for discontinuation, changes, or dose adjustments of SSRIs, using data from 667 reviews on WebMD.
- The research found that the main reason for stopping or switching SSRIs was adverse side effects, while dose changes were largely due to adjustments by either patients or healthcare professionals.
Article Synopsis
- The introduction of large language models (LLMs) represents a significant change in how we generate text, allowing for human-like chat interactions.
- LLM-based chatbots can enhance academic efficiency, but ethical issues like fair use and biases need to be addressed.
- The editorial emphasizes the importance of effective usage, distinguishes between LLM use and plagiarism, calls for addressing bias and accuracy concerns, and highlights a promising future for LLM applications in academia.