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

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

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Foundation Models in Electrocardiogram: A Review

arXiv

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arXiv 2024年

作者： Han, Yu Liu, Xiaofeng Zhang, Xiang Ding, Cheng University of Chinese Academy of Sciences Beijing China Department of Biomedical Informatics and Data Science Yale University Connecticut United States Department of Computer Science University of North Carolina at Charlotte Charlotte United States Department of Biomedical Engineering Georgia Institute of Technology Atlanta United States

The electrocardiogram (ECG) is ubiquitous across various healthcare domains, such as cardiac arrhythmia detection and sleep monitoring, making ECG analysis critically essential. Traditional deep learning models for ECG are task-specific, with a narrow scope of functionality and limited generalization capabilities. Recently, foundation models (FMs), also known as large pre-training models, have fundamentally reshaped the scheme of model design and representation learning, enhancing the performance across a variety of downstream tasks. This success has drawn interest in the exploration of FMs to address ECG-based medical challenges concurrently. This survey provides a timely, comprehensive and up-to-date overview of FMs for large-scale ECG-FMs. First, we offer a brief background introduction to FMs. Then, we discuss the model architectures, pre-training methods, and adaptation approaches of ECG-FMs from a methodology perspective. Despite the promising opportunities of ECG-FMs, we also outline the challenges and potential future directions. Overall, this survey aims to provide researchers and practitioners with insights into the research of ECG-FMs on theoretical underpinnings, domain-specific applications, and avenues for future exploration. © 2024, CC BY.

关键词： Electrocardiograms

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Constructing Coronary Artery Vessel Segmentation Based on Deep Learning Models and Image Processing 24

Constructing Coronary Artery Vessel Segmentation Based on De...

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

作者： Peng, Kang-Syuan Chang, Hsiao-Huang Hsu, Ying-Kai Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Cardiovascular Surgery Department Taipei Veterans General Hospital Taipei City TaiwanTaipei Medical University Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Narrowing of the coronary arteries is an important indicator of the severity of coronary artery disease (CAD) in patients. Previous research using deep learning to identify narrowed vessels has primarily been based on object detection, but this approach has several problems, such as the inability to detect multivessel narrowing, calculate the actual rate of narrowing, and determine the exact location of occurrence. Therefore, we believe that the determination of narrowing should return to the original method, namely, calculating the maximum and minimum diameters around the vessel. However, to automate this task, a preprocessing step for separating each vessel is needed, especially for the complex structure of the left coronary artery. We designed a deep learning model capable of analyzing an entire coronary angiography (CAG) image for the separation and tracking of vessels. This not only serves as a preprocessing step for calculating narrowing but also allows for the analysis of different vessel flow directions, providing doctors with comprehensive assessment indicators. © 2024 Copyright ACM.

关键词： Image segmentation

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The Time-Varying Impact of COVID-19 on the Acute Kidney Disorders:A Historical Matched Cohort Study and Mendelian Randomization Analysis

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Health data science 2024年第1期4卷 215-225页

作者： Chunyang Li Chao Zhang Jie Chen Yilong Chen Zhiye Ying Yao Hu Huan Song Ping Fu Xiaoxi Zeng Division of Nephrology West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China Med-X Center for Informatics Sichuan UniversityChengdu 610065China Department of Core Laboratory Sichuan Provincial Peopleʼs HospitalUniversity of Electronic Science and Technology of ChinaChengdu 610054China Centre of Public Health Sciences Faculty of MedicineUniversity of IcelandReykjavíkIceland

Background:This study aimed to explore the time-varying impact of COVID-19 on acute kidney disorders,including acute kidney injury and other acute kidney ***:From the UK Biobank,10,121 participants with COVID-19 were matched with up to 3 historically unexposed controls by age,sex,Townsend deprivation index,and the status of hospitalization or receiving critical *** investigated the association between COVID-19 and incidence of acute kidney disorders,within the ffrst 4 weeks after infection,using conditional and time-varying Cox proportional hazard *** addition,one-sample Mendelian randomization,utilizing the polygenic risk score for COVID-19 as an instrumental variable,was conducted to explore the potential causality of the ***:In the matched cohort study,we observed a signiffcant association between COVID-19 and acute kidney disorders predominantly within the ffrst 3 *** impact of COVID-19 was time dependent,peaking in the second week(hazard ratio,12.77;95%conffdence interval,5.93 to 27.70)and decreasing by the fourth week(hazard ratio,2.28;95%conffdence interval,0.75 to 6.93).In subgroup analyses,only moderate to severe COVID-19 cases were associated with acute worsening of renal function in a time-dependent ***-sample Mendelian randomization analyses further showed that COVID-19 might exert a“short-term”causal effect on the risk of acute kidney disorders,primarily conffned to the ffrst week after ***:The risk of acute kidney disorders following COVID-19 demonstrates a time-varying *** effects were observed only in patients with moderate or severe but not mild COVID-19.

关键词： Kidney matched utilizing

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Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering

arXiv

引用

arXiv 2023年

作者： Hu, Yan Chen, Qingyu Du, Jingcheng Peng, Xueqing Keloth, Vipina Kuttichi Zuo, Xu Zhou, Yujia Li, Zehan Jiang, Xiaoqian Lu, Zhiyong Roberts, Kirk Xu, Hua School of Biomedical Informatics University of Texas Health Science at Houston Houston United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New Haven United States National Center for Biotechnology Information National Library of Medicine National Institutes of Health MD United States

Objective: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. Materials and Methods: We evaluated these models on two clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus, following the 2010 i2b2 concept extraction shared task, and (2) identifying nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). To improve the GPT models' performance, we developed a clinical task-specific prompt framework that includes (1) baseline prompts with task description and format specification, (2) annotation guideline-based prompts, (3) error analysis-based instructions, and (4) annotated samples for few-shot learning. We assessed each prompt's effectiveness and compared the models to BioClinicalBERT. Results: Using baseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804 for MTSamples, and 0.301, 0.593 for VAERS. Additional prompt components consistently improved model performance. When all four components were used, GPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and 0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt framework. Although these results trail BioClinicalBERT (F1 of 0.901 for the MTSamples dataset and 0.802 for the VAERS), it is very promising considering few training samples are needed. Conclusion: While direct application of GPT models to clinical NER tasks falls short of optimal performance, our task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical problems

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CANCERLLM: A LARGE LANGUAGE MODEL IN CANCER DOMAIN

arXiv

引用

arXiv 2024年

作者： Li, Mingchen Blaes, Anne Johnson, Steven Liu, Hongfang Xu, Hua Zhang, Rui Division of Computational Health Sciences University of Minnesota Twin Cities United States Division of Hematology Oncology and Transplantation University of Minnesota Twin Cities United States Institute for Health Informatics University of Minnesota Twin Cities United States McWilliams School of Biomedical Informatics UTHealth Houston United States Department of Biomedical Informatics and Data Science Yale School of Medicine United States

Medical Large Language Models (LLMs) such as ClinicalCamel 70B, Llama3- OpenBioLLM 70B have demonstrated impressive performance on a wide variety of medical NLP task. However, there still lacks a large language model (LLM) specifically designed for cancer domain. Moreover, these LLMs typically have billions of parameters, making them computationally expensive for healthcare systems. Thus, in this study, we propose CancerLLM, a model with 7 billion parameters and a Mistral-style architecture, pre-trained on 2,676,642 clinical notes and 515,524 pathology reports covering 17 cancer types, followed by fine-tuning on three cancer-relevant tasks, including cancer phenotypes extraction, cancer diagnosis generation, and cancer treatment plan generation. Our evaluation demonstrated that CancerLLM achieves state-of-the-art results compared to other existing LLMs, with an average F1 score improvement of 8.1%. Additionally, CancerLLM outperforms other models on two proposed robustness testbeds. This illustrates that CancerLLM can be effectively applied to clinical AI systems, enhancing clinical research and healthcare delivery in the field of cancer. © 2024, CC BY.

关键词： Clinical research

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rmlnomogram: An R package to construct an explainable nomogram for any machine learning algorithms

arXiv

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arXiv 2025年

作者： Sufriyana, Herdiantri Su, Emily Chia-Yu Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University 155 Section 2 Linong Street Beitou District Taipei112304 Taiwan

Background: Current nomogram can only be created for regression algorithm. Providing nomogram for any machine learning (ML) algorithms may accelerate model deployment in clinical settings or improve model availability. We developed an R package and web application to construct nomogram with model explainability of any ML algorithms. Methods: We formulated a function to transform an ML prediction model into a nomogram, requiring datasets with: (1) all possible combinations of predictor values;(2) the corresponding outputs of the model;and (3) the corresponding explainability values for each predictor (optional). Web application was also created. Results: Our R package could create 5 types of nomograms for categorical predictors and binary outcome without probability (1), categorical predictors and binary outcome with probability (2) or continuous outcome (3), and categorical with single numerical predictors and binary outcome with probability (4) or continuous outcome (5). Respectively, the first and remaining types optimally allowed maximum 15 and 5 predictors with maximum 3,200 combinations. Web application is provided with such limits. The explainability values were possible for types 2 to 5. Conclusions: Our R package and web application could construct nomogram with model explainability of any ML algorithms using a fair number of predictors. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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Stroke Prediction Using Machine Learning

Stroke Prediction Using Machine Learning

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International Conference on Advances in Electronics, Computers and Communications (ICAECC)

作者： Abinandhini D M Aman Kumar Gudi Vishnu Teja Mandava Sukesh Divya S Naman Chauhan I R Oviya Kalpana Raja Department of Computer Science and Engineering (Artificial Intelligence) Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

A stroke is a serious medical emergency that happens when bleeding or blood clots cut off the blood flow to a part of the brain. With a mortality rate of 5.5 million per year, it ranks as the second leading cause of death globally. Over 15 million individuals experience a stroke each year, and one person dies from one every four minutes. According to the World Health Organization, stroke is the main cause of death and disability worldwide (WHO). Identifying the many stroke warning signs helps lessen the severity of the stroke. A stroke can be avoided in up to 80% of instances because it is typically the result of a poor lifestyle. As a result, stroke prediction becomes important and should be employed to stop it from causing long-term harm. The current study uses a variety of machine learning models, including Gaussian Naive Bayes, Logistic Regression, Support Vector Machine (SVM), KNN and Random Forest to predict stroke. The paper presents the comparison among all machine learning algorithms. Analysis of results revealed that KNN had the least accuracy of 76.32% and Random Forest had the highest accuracy of 94.81%.

关键词： Support vector machines Accuracy Organizations Stroke (medical condition) Predictive models Vectors Physiology

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ML Based Phenotype Analysis Using Differential Gene Expression data in Schizophrenia

ML Based Phenotype Analysis Using Differential Gene Expressi...

引用

Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Aadhithyan D Shabarirajan KJ Varun Maniappan Shivanesh B Divya S Praghaadeesh R Kalpana Raja I R Oviya Department of Computer Science and Engineering (Artificial Intelligence) Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

ISBN: (数字)9798350384628

ISBN: (纸本)9798350384635

In this paper, differential gene expression (DGE) data in schizophrenia is analysed using machine learning approaches with the objective of predicting cell phenotypes related to the condition. The dataset contains information on cis eQTNs, gene orientation, probe information, gene expression profiles, and SNP information. When first investigating well-known machine learning methods, a hybrid model combining Random Forests and XGBoost is suggested, however it only offers a little increase. Increases in predicted accuracy of more than 99.12% are made possible by the H2O AutoML package. The results demonstrate the capability of ML-based analysis to unravel the underlying biological mechanisms of schizophrenia and contribute to the design of individualised therapeutic approaches. The paper represents the capability of machine learning to comprehend the relationship between gene expression patterns and complicated illnesses like schizophrenia.

关键词： Water Training Accuracy Phenotypes Mental disorders Predictive models data models

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Development of Automated Detection Methods for Calculating Blood Vessel Width and Stenosis Rate in Cardiac Catheterization Imaging 24

Development of Automated Detection Methods for Calculating B...

引用

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

作者： Huang, Hsien-Cheng Chang, Hsiao-Huang Ke, Xiang-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Cardiovascular Surgery Department Taipei Veterans General Hospital Taipei City Taiwan Taipei Medical University Taipei City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Coronary artery disease is the most prevalent type of heart disease and has a considerable mortality rate. In diagnosing and assessing coronary artery disease, physicians must integrate a variety of clinical information and test results to evaluate the degree of coronary artery obstruction and continually learn and improve their skills in clinical practice to ensure the most accurate assessment of patients. Currently, the use of information technology to assist medical staff allows more effective and accurate analysis of symptoms. This approach not only serves as an auxiliary standard for physician diagnosis, thereby reducing the possibility of misdiagnosis, but also significantly improves patient treatment outcomes and medical quality while reducing the time burden on physicians for diagnosing patients. This study utilized image processing techniques and thinning algorithms to automate the detection of coronary artery width and, following the recommended methods of physicians in related fields, used specific vessel widths to calculate the degree of coronary artery narrowing. This provides a more accurate and efficient assessment method for evaluating the degree of vascular constriction in patients with coronary artery disease. © 2024 Copyright ACM.

关键词： Patient treatment

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