COVID-19 vaccine equity and health equity conversations on Twitter
This study used social network analysis and trending hashtags on Twitter to identify trends related to health and vaccine equity during the Omicron wave. The analysis was conducted using consumer-friendly platforms/tools such as the Healthcare Hashtag Project and NodeXL. The study found that during the Omicron wave, there was a higher volume of tweets related to the more specific hashtag #VaccineEquity, as compared to the more general topic of #HealthEquity. The study also identified the top influencers for these hashtags and how they changed over time. The study proposes a combination of existing tools and approaches, inc...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Nishant R Jain Iris Zachary Suzanne A Boren Source Type: research
Using natural language processing to study homelessness longitudinally with electronic health record data subject to irregular observations
AMIA Annu Symp Proc. 2024 Jan 11;2023:894-903. eCollection 2023.ABSTRACTThe Electronic Health Record (EHR) contains information about social determinants of health (SDoH) such as homelessness. Much of this information is contained in clinical notes and can be extracted using natural language processing (NLP). This data can provide valuable information for researchers and policymakers studying long-term housing outcomes for individuals with a history of homelessness. However, studying homelessness longitudinally in the EHR is challenging due to irregular observation times. In this work, we applied an NLP system to extract h...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Alec B Chapman Daniel O Scharfstein Ann Elizabeth Montgomery Thomas Byrne Ying Suo Atim Effiong Tania Velasquez Warren Pettey Richard E Nelson Source Type: research
Web-based Prototype for Graphical Exploration of FHIR ® Questionnaire Responses
We describe the abstract feature design with the derived technical implementation to allow a universal, user-configurable data subselection mechanism to generate conditional one- and two-data-dimensional charts. The applicability of our developed prototype is demonstrated on synthetic FHIR data with the source code available at https://github.com/frankkramer-lab/FHIR-QR-Explorer.PMID:38222405 | PMC:PMC10785863 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Johann Frei Florian J Auer Steffen Netzband Yevgeniia Ignatenko Frank Kramer Source Type: research
Community Health Recommendations Driven by mHealth Population Surveillance Data Amongst Burmese Displaced People in Eastern India: A Pilot Usability Assessment of a Mobile Health Application for Data Collection
AMIA Annu Symp Proc. 2024 Jan 11;2023:933-941. eCollection 2023.ABSTRACTWith recent increases in armed conflict and forced migration, refugee health has become a growing priority amongst those who work in global health. Refugees and forced migrants, also known as displaced persons, face barriers to accessing health services and are often at an increased risk for adverse health outcomes, such as sexual violence, infectious diseases, poor maternal outcomes, and mental health concerns. Mobile health (mHealth) applications have been shown to increase access and improve health outcomes among refugee populations. Our study aims ...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Kylie Dougherty Ruth Masterson Creber Beichotha Zawtha Natalie C Benda Source Type: research
Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity
In this study, we sought to utilize the embeddings of first-person pronouns obtained from contextualized language representation models to capture ways these pronouns are used, to analyze mental status. De-identified text messages sent during online psychotherapy with weekly assessment of depression severity were used for evaluation. Results indicate the advantage of contextualized first-person pronoun embeddings over standard classification token embeddings and frequency-based pronoun analysis results in predicting depression symptom severity. This suggests contextual representations of first-person pronouns can enhance t...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Xinyang Ren Hannah A Burkhardt Patricia A Are án Thomas D Hull Trevor Cohen Source Type: research
Discovering predictive temporal patterns for Acute Kidney Injury from critical care data
AMIA Annu Symp Proc. 2024 Jan 11;2023:261-269. eCollection 2023.ABSTRACTAcute Kidney Injury is a severe clinical condition with a high risk of multi-organs complications and mortality. For this reason, early recognition is crucial. Our proposal based on a 3-window framework discovers all hidden regularities, called Approximate Predictive Functional Dependencies, with the aim to enable early recognition of high-risk patients during hospitalization in the Intensive Care Unit (ICU). We evaluated the different severity stages according to the Kidney Disease Improving Global Outcomes (KDIGO) guidelines, building different patho...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Beatrice Amico Carlo Combi Giovanni Gambaro Source Type: research
Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the impor...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yao-Shun Chuang Xiaoqian Jiang Chun-Teh Lee Ryan Brandon Duong Tran Oluwabunmi Tokede Muhammad F Walji Source Type: research
Promoting TEFCA with Blockchain Technology: A Decentralized Approach to Patient-centered Healthcare Data Management
This article delves into the potential of blockchain technology to promote TEFCA design. By providing an immutable and transparent ledger, blockchain ensures data integrity, openness, and patient privacy. Overall, the use of blockchain technology can help address the challenges of implementing TEFCA principles and promote patient empowerment and control over their health data, improve data interoperability, and enhance healthcare quality.PMID:38222410 | PMC:PMC10785864 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yan Zhuang Luxia Zhang Source Type: research
Framework for Research in Equitable Synthetic Control Arms
AMIA Annu Symp Proc. 2024 Jan 11;2023:530-539. eCollection 2023.ABSTRACTRandomized Clinical Trials (RCTs) measure an intervention's efficacy, but they may not be generalizable to a desired target population if the RCT is not equitable. Thus, representativeness of RCTs has become a national priority. Synthetic Controls (SCs) that incorporate observational data into RCTs have shown great potential to produce more efficient studies, but their equity is rarely considered. Here, we examine how to improve treatment effect estimation and equity of a trial by augmenting "on-trial" concurrent controls with SCs to form a Hybrid Cont...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Naffs Neehal Vibha Anand Kristin P Bennett Source Type: research
Real-World Analysis of Antipsychotic Drugs' Effect on Weight Gain: An EHR Application
AMIA Annu Symp Proc. 2024 Jan 11;2023:961-968. eCollection 2023.NO ABSTRACTPMID:38222412 | PMC:PMC10785896 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Shraddha Gupta Xing Song Source Type: research
An Automated Strategy to Calculate Medication Regimen Complexity
AMIA Annu Symp Proc. 2024 Jan 11;2023:1077-1086. eCollection 2023.ABSTRACTUnderstanding medication regimen complexity is important to understand what patients may benefit from pharmacist interventions. Medication Regimen Complexity Index (MRCI), a 65-item tool to quantify the complexity by incorporating the count, dosage form, frequency, and additional administration instructions of prescription medicines, provides a more nuanced way of assessing complexity. The goal of this study was to construct and validate a computational strategy to automate the calculation of MRCI. The performance of our strategy was evaluated by com...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yuzhi Lu Ariel R Green Rosalphie Quiles Casey Overby Taylor Source Type: research
A Computable Phenotype for the Identification of Sexual and Gender Minorities in Electronic Health Records
AMIA Annu Symp Proc. 2024 Jan 11;2023:1057-1066. eCollection 2023.ABSTRACTSexual gender minorities, including lesbian, gay, and bisexual (LGB) individuals face unique challenges due to discrimination, stigma, and marginalization, which negatively impact their well-being. Electronic health record (EHR) systems present an opportunity for LGB research, but accurately identifying LGB individuals in EHRs is challenging. Our study developed and validated a rule-based computable phenotype (CP) to identify LGB individuals and their subgroups using both structured data and unstructured clinical narratives from a large integrated he...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yongqiu Li Xing He Christopher Wheldon Yonghui Wu Mattia Prosperi Elizabeth A Shenkman Michael S Jaffee Jingchuan Guo Fei Wang Yi Guo Jiang Bian Source Type: research
Local Contrastive Learning for Medical Image Recognition
AMIA Annu Symp Proc. 2024 Jan 11;2023:1236-1245. eCollection 2023.ABSTRACTThe proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model pred...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Syed A Rizvi Ruixiang Tang Xiaoqian Jiang Xiaotian Ma Xia Hu Source Type: research
The SMART Text2FHIR Pipeline
Conclusion: Future work should include mapping more categories of NLP-extracted information into FHIR resources and mappings from additional open-source NLP tools.PMID:38222416 | PMC:PMC10785871 (Source: AMIA Annual Symposium Proceedings)
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Timothy A Miller Andrew J McMurry James Jones Daniel Gottlieb Kenneth D Mandl Source Type: research
Text Classification of Cancer Clinical Trial Eligibility Criteria
In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained clinical trial BERT model. Our results demonstrate the feasibility of automatically classifying common exclusion criteria. Additionally, we demonstrate the value of a pre-trained language model specifically for clinical trials, which ...
Source: AMIA Annual Symposium Proceedings - January 15, 2024 Category: Bioinformatics Authors: Yumeng Yang Soumya Jayaraj Ethan Ludmir Kirk Roberts Source Type: research
