AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science最新文献

SNOMED CT is extensively employed to standardize data across diverse patient datasets and support cohort identification, with studies revealing its benefits and challenges. In this work, we developed a SNOMED CT-driven cohort query system over a heterogeneous Optum^® de-identified COVID-19 Electronic Health Record dataset leveraging concept mappings between ICD-9-CM/ICD-10-CM and SNOMED CT. We evaluated the benefits and challenges of using SNOMED CT to perform cohort queries based on both query code sets and actual patients retrieved from the database, leveraging the original ICD-9-CM and ICD-10-CM as baselines. Manual review of 80 random cases revealed 65 cases containing 148 true positive codes and 25 cases containing 63 false positive codes. The manual evaluation also revealed issues in code naming, mappings, and hierarchical relations. Overall, our study indicates that while the SNOMED CT-driven query system holds considerable promise for comprehensive cohort queries, careful attention must be given to the challenges offalsely included codes and patients.

Variations in laboratory test names across healthcare systems-stemming from inconsistent terminologies, abbreviations, misspellings, and assay vendors-pose significant challenges to the integration and analysis of clinical data. These discrepancies hinder interoperability and complicate efforts to extract meaningful insights for both clinical research and patient care. In this study, we propose a machine learning-driven solution, enhanced by natural language processing techniques, to standardize lab test names. By employing feature extraction methods that analyze both string similarity and the distributional properties of test results, we improve the harmonization of test names, resulting in a more robust dataset. Our model achieves a 99% accuracy rate in matching lab names, showcasing the potential of AI-driven approaches in resolving long-standing standardization challenges. Importantly, this method enhances the reliability and consistency of clinical data, which is crucial for ensuring accurate results in large-scale clinical studies and improving the overall efficiency of informatics-based research and diagnostics.

Amblyopia is a neurodevelopmental disorder affecting children's visual acuity, requiring early diagnosis for effective treatment. Traditional diagnostic methods rely on subjective evaluations of eye tracking recordings from high fidelity eye tracking instruments performed by specialized pediatric ophthalmologists, often unavailable in rural, low resource clinics. As such, there is an urgent need to develop a scalable, low cost, high accuracy approach to automatically analyze eye tracking recordings. Large Language Models (LLM) show promise in accurate detection of amblyopia; our prior work has shown that the Google Gemini model, guided by expert ophthalmologists, can detect control and amblyopic subjects from eye tracking recordings. However, there is a clear need to address the issues of transparency and trust in medical applications of LLMs. To bolster the reliability and interpretability of LLM analysis of eye tracking records, we developed a Feature Guided Interprative Prompting (FGIP) framework focused on critical clinical features. Using the Google Gemini model, we classify high-fidelity eye-tracking data to detect amblyopia in children and apply the Quantus framework to evaluate the classification results across key metrics (faithfulness, robustness, localization, and complexity). These metrics provide a quantitative basis for understanding the model's decision-making process. This work presents the first implementation of an Explainable Artificial Intelligence (XAI) framework to systematically characterize the results generated by the Gemini model using high-fidelity eye-tracking data to detect amblyopia in children. Results demonstrated that the model accurately classified control and amblyopic subjects, including those with nystagmus while maintaining transparency and clinical alignment. The results of this study support the development of a scalable and interpretable clinical decision support (CDS) tool using LLMs that has the potential to enhance the trustworthiness of AI applications.

Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool in medicine, widely used to detect and assess various health conditions. Different MRI sequences, such as T1-weighted, T2-weighted, and FLAIR, serve distinct roles by highlighting different tissue characteristics and contrasts. However, distinguishing them based solely on the description file is currently impossible due to confusing or incorrect annotations. Additionally, there is a notable lack of effective tools to differentiate these sequences. In response, we developed a deep learning-based toolkit tailored for small, unrefined MRI datasets. This toolkit enables precise sequence classification and delivers performance comparable to systems trained on large, meticulously curated datasets. Utilizing lightweight model architectures and incorporating a voting ensemble method, the toolkit enhances accuracy and stability. It achieves a 99% accuracy rate using only 10% of the data typically required in other research. The code is available at https://github.com/JinqianPan/MRISeqClassifier.

Assessing how accurately a cohort extracted from Electronic Health Records (EHR) represents the intended target population, or cohort fitness, is critical but often overlooked in secondary EHR data use. This scoping review aimed to (1) identify guidelines for assessing cohort fitness and (2) determine their thoroughness by examining whether they offer sufficient detail and computable methods for researchers. This scoping review follows the JBI guidance for scoping reviews and is refined based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklists. Searches were performed in Medline, Embase, and Scopus. From 1,904 results, 30 articles and 2 additional references were reviewed. Nine articles (28.13%) include a framework for evaluating cohort fitness but only 5 (15.63%) contain sufficient details and quantitative methodologies. Overall, a more comprehensive guideline that provides best practices for measuring the cohort fitness is still needed.

Postural Tachycardia Syndrome (POTS) is a chronic condition characterized by orthostatic intolerance and a significant rise in heart rate upon standing. Patients often experience debilitating symptoms, such as brain fog and chronic fatigue, which hinder daily functioning. Non-pharmacological management strategies, particularly pacing, are crucial for reducing symptom fluctuations and improving quality of life. Heart rate monitoring plays a key role in effective pacing, enabling patients to plan activities and prevent severe symptom onset. Recent technological advancements have increased interest in wearable devices for managing chronic conditions. This study examines the feasibility of using wearable technology to support symptom management in POTS patients. Through an Exploratory- Descriptive Qualitative approach, five key themes emerged, including personalized management strategies and the beneficial impact of real-time feedback. The findings suggest that wearable devices can enhance self-management, improve communication with healthcare providers, and empower patients to take a more proactive approach to their care.

Growth in the field of medical imaging research has revealed a need for larger volume and variety in available data. This need could be met using curated clinically acquired data, but the process for getting this data from the scanners to the scientists is complex and lengthy. We present a manifest-driven modular Extract, Transform, and Load (ETL) process named Locutus designed to appropriately handle difficulties present in the process of reusing clinically acquired medical imaging data. The design of Locutus was based on four foundational assumptions about medical data, research data, and communication. All parts of a workflow must communicate with each other and be adaptable to unique data delivery requests. In addition, the workflow must be robust to possible errors and uncertainties in clinically-acquired data, which may require human intervention to resolve. With these assumptions in mind,Locutus presents a five-phase workflow for downloading, deidentifying, and delivering unique requests for imaging data. The phases include initialization, data preparation, extraction of data from the research server to a pre-deidentification data warehouse, transformation into deidentified space, and loading into post-deidentification data warehouse. To date, this workflow has been used to process 32,962 imaging accessions for research use. This number is expected to grow as technical challenges are addressed and the role of humans is expected to shift from frequent intervention to regular monitoring.

Opioid overdose and opioid use disorder (OUD) remain a growing public health issue in the United States, affecting 6.1 million individuals in 2022, more than doubling the 2.5 million from 2021. Accurately identifying the opioid overdose and OUD related information is critical to study the outcomes and develop interventions. This study aims to identify opioid overdose and OUD mentions and their related information from clinical narratives. We compared encoder-based large language models (LLMs) and decoder-based generative LLMs in extracting nine crucial concepts related with opioid overdose and OUD including problematic opioid use. Through a cost-effective p-tuning algorithm, our decoder-based generative LLM, GatorTronGPT, achieved the best strict/lenient F1-score of 0.8637, and 0.9057, demonstrating the efficient of using generative LLMs for opioid overdose/OUD related information extraction. This study provided a tool to systematically extract opioid overdose, OUD, and their related information to facilitate opioid-related studies using clinical narratives.

Hearing loss is a prevalent and impactful condition that affects millions globally. In 2022, the U.S. Food and Drug Administration (FDA) approved over-the-counter (OTC) hearing aids for individuals with mild to moderate hearing loss, establishing a distinct category separate from prescription hearing aids. This regulatory change may leave some patients, particularly those unfamiliar with hearing aids, without medical guidance in their decision-making process. To address this, our team developed the CLEARdashboard (Consumer Led Evidence - Amplification Resources dashboard) as an educational platform to assist users in comparing the technical specifications of various OTC hearing aids. In this study, we proposed a new key feature on the CLEARdashboard that is to utilize Natural Language Processing (NLP) methods to analyze product reviews from two prominent hearing aid online retailers. Analyzing product reviews using NLP is particularly helpful because these reviews often contain detailed, real-world insights into the performance and usability of hearing aids that may not be captured in technical specifications alone. We used NLP techniques in the automatic summarization of large volumes of user feedback into concise "pros and cons" lists, providing patients with a clearer understanding of the strengths and limitations of each device. This approach saves patients from manually sifting through extensive reviews and helps them make informed choices based on aggregated consumer experiences. The generated summaries were validated by three human evaluators to ensure the most comprehensive and reliable method of presenting this information, enhancing the decision-making process for individuals selecting OTC hearing aids.

Rare diseases affect approximately 1 in 11 Americans, yet their diagnosis remains challenging due to limited clinical evidence, low awareness, and lack of definitive treatments. Our project aims to accelerate rare disease diagnosis by developing a comprehensive informatics framework leveraging data mining, semantic web technologies, deep learning, and graph-based embedding techniques. However, our on-premises computational infrastructure faces significant challenges in scalability, maintenance, and collaboration. This study focuses on developing and evaluating a cloud-based computing infrastructure to address these challenges. By migrating to a scalable, secure, and collaborative cloud environment, we aim to enhance data integration, support advanced predictive modeling for differential diagnoses, and facilitate widespread dissemination of research findings to stakeholders, the research community, and the public and also proposed a facilitated through a reliable, standardized workflow designed to ensure minimal disruption and maintain data integrity for existing research project.