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IEEE International Conference on Healthcare informatics (ICHI)

作者： Pritham Ram Na Hong Hua Xu Xiaoqian Jiang Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven CT USA McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Secondary use or reuse of biomedical research data has drawn significant attention and is of growing importance. Non-standardized representation and wide variability of clinical and observational data of biomedical research pose a major challenge to data collection, integration, analysis, and sharing. To address this requirement, we present experiences of using GPT for Sheets as a tool to map study-level biomedical research variables or data elements to Common data Elements (CDEs). In this study, the trial variables were extracted from the data dictionaries in the RADx-rad program, and the NIH Pediatric COVID-19 Working Group recommended CDEs that include children (0 – 21 years) were used as the reference CDEs. Mapping experimental variables (vague data elements) to CDEs resulted in an Fl-score of 0.82 when considering only the first recommendation and 0.85 when considering either the first or second recommendation. Our results indicate that GPT language models can differentiate freely defined study variables and map them to standard CDEs. This, in turn, potentially further enhances interoperability with a wider range of biomedical studies.

关键词： Pediatrics Dictionaries Biological system modeling Medical services data collection data models informatics

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High-dose-rate Brachytherapy Planning with Dendrite Cross-Attention UNet

High-dose-rate Brachytherapy Planning with Dendrite Cross-At...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Saini, Sourav Wei, Yawen Rong, Jingzhao Liu, Xiaofeng Dept. of Radiology and Biomedical Imaging Yale School of Medicine Yale University New Haven 06519 CT United States Dept. of Biostatistics (Health Informatics and Data Science) Yale School of Public Health Yale University New Haven 06519 CT United States Dept. of Department of Biomedical Informatics and Data Science Yale School of Medicine Yale University New Haven 06519 CT United States

ISBN: (纸本)9781510685949

Treatment of cervical cancer commonly involves high-dose-rate brachytherapy (HDR-BT), a procedure that requires precise and efficient planning to achieve the best patient outcomes. Historically, the HDR-BT planning process has been labor-intensive and largely dependent on the expertise of the clinician, resulting in potential inconsistencies in the quality of treatment. To overcome this issue, we propose an innovative method that employs advanced deep-learning models to improve HDR-BT planning. This paper presents the Dendrite CrossAttention UNet (DCA-UNet), which features a sophisticated dendritic structure comprising a primary branch for stacked inputs and three auxiliary branches dedicated to the segmentation of the clinical target volume (CTV), bladder, and rectum. This architecture enhances the model's understanding of organ-at-risk (OAR) areas, thereby improving dose prediction accuracy. Extensive evaluations reveal that DCA-UNet significantly enhances the precision of HDR-BT dose predictions across different applicator types. Our findings indicate that DCA-UNet consistently outperforms both traditional UNet and the more recent SwimUNetr models. By advancing the use of cross-attention mechanisms in deep learning frameworks, this research aids in the standardization of HDR-BT planning and opens up promising possibilities for future advancements in cervical cancer care. © 2025 SPIE

关键词： Brachytherapy Cervical Cancer Deep Learning Dose Prediction Treatment Planning

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Assessing the Mental Health Impact of COVID-19 on the US Population: A Large-Scale Survey Analysis Using SMOTE 24

Assessing the Mental Health Impact of COVID-19 on the US Pop...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Rayguru, Chinmayee Su, Emily Chia-Yu Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University 250 Wu-Xing Street Taipei11031 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei11031 Taiwan

ISBN: (纸本)9798400716874

The coronavirus (COVID-19) outbreak, recognized as one of the deadliest health crises in recent history, swiftly affected over 200 countries. The pandemic has posed complex challenges, specifically affected not only the rising number of cases but also deeply influenced individual mental health. This research explores the significant mental health implications of the COVID-19 pandemic on the US population, analyzed through a large-scale survey of 25,136 participants across various ages, economic backgrounds, and chronic disease status. We categorized mental health status into three risk levels: low, moderate, and high, based on the key features influencing stress levels. Notably, our findings reveal that the age group 25-54 years exhibited higher anxiety levels compared to other age groups. The implementation of Extreme Gradient Boosting (XGBoost) with Synthetic Minority Over-sampling (SMOTE) Technique for balancing data yielded impressive accuracy rates: 94.55% for high risk, 90.73% for moderate risk, and 77.77% for low risk, respectively. These results significantly outperformed the Random Forest (RF) model in both imbalanced and SMOTE balanced datasets. Furthermore, the study identified high obesity and chronic diseases, such as bronchitis, as factors exacerbating stress levels. This research contributes valuable insights to the mental health condition prediction during COVID-19 pandemic by underlining the importance of targeted interventions for high-risk groups. © 2024 Copyright ACM.

关键词： COVID-19

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High-Density Lipoproteins Associate with Age-Related Macular Degeneration in the All of Us Research Program

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Ophthalmology 2025年第6期132卷 684-691页

作者： Chen, Jimmy S. Esko, Jeffrey D. Walker, Evan Gordts, Philip L.S.M. Baxter, Sally L. Toomey, Christopher B. Division of Ophthalmology Informatics and Data Science Viterbi Family Department of Ophthalmology and Shiley Eye Institute University of California San Diego La Jolla California United States Glycobiology Research and Training Center University of California San Diego La Jolla California United States Division of Biomedical Informatics Department of Medicine University of California San Diego La Jolla California United States

Purpose: Extracellular lipoprotein aggregation is a critical event in age-related macular degeneration (AMD) pathogenesis. In this study, we sought to analyze associations between clinical and genetic-based factors related to lipoprotein metabolism and risk for AMD in the All of Us research program. Design: Cross-sectional retrospective data analysis. Participants: A total of 5028 healthy participants and 2328 patients with AMD from All of Us. Methods: Participants with and without AMD were age, race, and sex matched in a 1:2 ratio, respectively. Smoking status, history of hyperlipidemia, and statin use were extracted in a binary manner. Statin use was further subcategorized into hepatically versus nonhepatically metabolized statins. Laboratory values for low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TGs) were also extracted, and outliers were excluded from analysis. The PLINK toolkit was used to extract single nucleotide polymorphisms (SNPs) associated with LDL and HDL dysregulation, as published in prior work. Odds ratio curves were computed to assess the risk between LDL, TG, and HDL versus AMD. All clinical and genetic variables were input into a multivariable logistic regression model, and odds ratios and P values were generated. Main Outcome Measures: Statistical significance of risk factors for AMD, thresholded at P ≤ 0.05. Results: On multivariable regression analysis, statin use and low and high HDL were significantly associated with increased AMD risk (P < 0.001 for all variables). Additionally, the multivariable regression implicated HDL-associated SNP's increased risk for AMD. Last, LPA was identified (P = 0.007) as a novel SNP associated with increased AMD risk. Conclusions: There exists a U-shaped relationship between HDL and AMD risk, such that high and low HDL are significantly associated with increased AMD risk. Additionally, SNPs associated with HDL metabolism are associated with AMD risk. This analysis further e

关键词： Drusen HDL LDL Single nucleotide polymorphism TG

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Tomato Disease Classification Using CNN 4th

Tomato Disease Classification Using CNN

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Archanaa, N. Daniel, V. Vifert Jenuben Divya, S. Raja, Kalpana Oviya, I.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

ISBN: (纸本)9789819736898

Plant disease detection using various machine learning techniques is a very popular research area. Many promising results have been obtained, but only a few specific applications can still make farmers’ lives easier. This research focuses on the important task of identifying tomato diseases at an early stage to prevent their spread and protect crop yields more broadly, emphasizing the idea that disease prevention will more important than treatment. Leveraging the capabilities of convolutional neural networks (CNN), we developed a model that diagnoses eight distinct tomato diseases using only their images. Diseases listed include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, and tomato yellow leaf curl virus. An integral part of our approach involves sophisticated image prepossessing and enhancement techniques, ensuring data quality and model robustness. Feature extraction plays a key role in transforming raw image data into recognizable patterns. The architecture of the CNN has been meticulously designed, consisting of multiple layers with a notable focus on the ReLU activation function and other relevant hyper-parameters. Our main dataset comes from PlantVillage, known for synthesizing rich and diverse plant images. After rigorous training and validation, the CNN model showed exceptional accuracy of 96.88% in tomato disease identification. However, challenges such as overfitting, image quality variability, and class imbalances were encountered but were resolved through strategic data augmentation, careful CNN overlay. In summary, our research presents a practical solution for farmers and agricultural entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Desai, Pooja M. Raj, Ayush Albers, David Kashyap, Aparajita Mamykina, Lena Department of Biomedical Informatics Columbia University Irving Medical Center New York NY United States Columbia University Data Science Institute New York NY United States Department of Medicine University of Colorado Aurora CO United States Department of Biomedical Informatics Columbia University New York NY United States

ISBN: (纸本)9798400713958

Recommendation systems for nutrition management have become increasingly popular. Current solutions focus on optimizing for users taste preferences, often neglecting practical factors including personal priorities, existing plans, and situational constraints. We propose similarity-based meal recommendation – a novel approach that considers users existing meal plans and recommends similar meals that also align with their nutrition goals. This approach is designed to support meal decisions in on-the-spot, particularly when individuals are in constrained settings with limited options for adjustment (e.g. a cafeteria). We conducted a feasibility study with 15 participants to evaluate the utility of similarity-based recommendations in constrained settings and refine the system design. Findings show (1) a novel approach for similarity-based meal recommendations, (2) insights into the dimensions of similarity influencing actionability across diverse, and (3) opportunities to enhance user control over the system’s design. The study contributes to the advancement of human-centered AI technologies in nutrition. © 2025 Copyright held by the owner/author(s).

关键词： Human-Centered AI Mixed-Methods Nutrition Management RecSys User Studies

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CDEMapper: Enhancing NIH Common data Element Normalization using Large Language Models

arXiv

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arXiv 2024年

作者： Wang, Yan Huang, Jimin He, Huan Zhang, Vincent Zhou, Yujia Hao, Xubing Ram, Pritham Qian, Lingfei Xie, Qianqian Weng, Ruey-Ling Lin, Fongci Hu, Yan Cui, Licong Jiang, Xiaoqian Xu, Hua Hong, Na Department of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States Mcwilliam School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX United States

Objective Common data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CDEs presents challenges due to the broad range and variety of data elements. This study aims to develop an effective and efficient mapping tool to bridge the gap between local data elements and National Institutes of Health (NIH) CDEs. Methods We propose CDEMapper, a large language model (LLM)-powered mapping tool designed to assist in mapping local data elements to NIH CDEs. CDEMapper has three core modules: (1) CDE indexing and embeddings. NIH CDEs were indexed and embedded to support semantic search;(2) CDE recommendations. The tool combines Elasticsearch (BM25 similarity methods) with state-of-the-art GPT services to recommend candidate CDEs and their permissible values;and (3) Human review. Users review and select the NIH CDEs/values that best match their data elements and value sets. We evaluate the tool's recommendation accuracy against manually annotated mapping results. Results CDEMapper offers a publicly available, LLM-powered, and intuitive user interface that consolidates essential and advanced mapping services into a streamlined pipeline. It provides a step-by-step, quality-assured mapping workflow designed with a user-centered approach. The evaluation results demonstrated that augmenting BM25 with GPT embeddings and a ranker consistently enhances CDEMapper's mapping accuracy in three different mapping settings across four evaluation datasets. Discussions and Conclusions This work opens up the potential of using LLMs to assist with CDE recommendation and human curation when aligning local data elements with NIH CDEs. Additionally, this effort enhances clinical research data interoperability and helps researchers better understand the gaps between local data elements and NIH CDEs. Copyright © 2024, The Authors. All rights reserved.

关键词： Clinical research

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Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?

arXiv

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arXiv 2024年

作者： Hu, Yan Zuo, Xu Zhou, Yujia Peng, Xueqing Huang, Jimin Keloth, Vipina K. Zhang, Vincent J. Weng, Ruey-Ling Chen, Qingyu Jiang, Xiaoqian Roberts, Kirk E. Xu, Hua McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX United States Department of Biomedical Informatics and Data Science Yale School of Medicine Yale University New Haven United States

Backgrounds: Information extraction (IE) is critical in clinical natural language processing (NLP). While large language models (LLMs) excel on generative tasks, their performance on extractive tasks remains debated. Methods: We investigated two fundamental clinical IE tasks: Named Entity Recognition (NER) and Relation Extraction (RE). Using 1,588 clinical notes from four data sources including UT Physicians (UTP), MTSamples, MIMIC-III, and i2b2, we developed a comprehensive annotated corpus covering 4 main clinical entities (problems, tests, medications, and other treatments) and 16 modifiers (e.g., negation, certainty). We instruction-tuned LLaMA-2 and LLaMA-3 for both tasks, comparing their performance, generalizability, computational resources, and throughput to BERT. Results: LLaMA models consistently outperformed BERT across datasets and tasks. With sufficient training data (e.g., UTP), LLaMA showed marginal improvements (1% on NER, 1.5-3.7% on RE) over BERT;for limited training data (e.g., MTSamples, MIMIC-III), the improvements were greater. On the unseen i2b2 dataset, LLaMA-3-70B outperformed BERT by over 7% (F1) on NER and 4% (F1) on RE. However, LLaMA models required significantly more computing resources (i.e., memory and GPUs) and ran up to 28 times slower than BERT. To facilitate LLM use in clinical IE, we implemented"Kiwi," a clinical IE package featuring both BERT and LLaMA models and made it available at https://***/. Conclusion: This study is among the first to develop and evaluate a comprehensive clinical IE system using open-source LLMs. Results indicate that LLaMA models outperform BERT for clinical NER and RE but with higher computational costs and lower throughputs. These findings highlight that choosing between LLMs and traditional deep learning methods for clinical IE applications should remain task-specific, taking into account both performance metrics and practical considerations such as available computing resources and t

关键词： Natural language processing systems

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GPTON – Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data

arXiv

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arXiv 2024年

作者： Li, Rongbin Chen, Wenbo Li, Jinbo Xing, Hanwen Xu, Hua Li, Zhao Zheng, W. Jim McWilliams School of Biomedical Informatics University of Texas Health Science Center at Houston HoustonTX77030 United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT06510 United States

By leveraging GPT-4 for ontology narration, we developed GPTON to infuse structured knowledge into LLMs through verbalized ontology terms, achieving accurate text and ontology annotations for over 68% of gene sets in the top five predictions. Manual evaluations confirm GPTON's robustness, highlighting its potential to harness LLMs and structured knowledge to significantly advance biomedical research beyond gene set annotation. Copyright © 2024, The Authors. All rights reserved.

关键词： Gene Ontology

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Gastroenterology 2025年第5期168卷 1040-1041页

作者： Dennis L. Shung Section of Digestive Diseases Department of Medicine Yale School of Medicine New Haven Connecticut Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven Connecticut

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