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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Li, Yiming Peng, Xueqing Li, Jianfu Peng, Suyuan Pei, Donghong Tao, Cui Xu, Hua Hong, Na The University of Texas Health Science Center at Houston School of Biomedical Informatics HoustonTX United States Yale University Section of Biomedical Informatics and Data Science School of Medicine New HavenCT United States Peking University National Institute of Health Data Science Beijing China The University of Texas Md Anderson Cancer Center HoustonTX United States

ISBN: (纸本)9798350302639

Acupuncture point location information is significant to the effect of acupuncture therapy. A wealth of knowledge related to acupuncture point locations is typically dispersed across books or reports in free-text format, which can pose challenges for machines to understand and facilitate. In this study, our purpose is to develop a natural language processing (NLP) tool to extract acupuncture point location information. We adopted the World Health Organization (WHO) Standard Acupuncture Point Locations in the Western Pacific Region (WHO Standard) as our reference standard and corpus. Our proposed approach to develop and validate the NLP tool involves three key steps: 1) We leveraged pre-existing terminologies from the anatomy domain, specifically the International Anatomical Terminology (IAT) and Uber-anatomy Ontology (UBERON), to construct a baseline dictionary of acupoint locations. This dictionary was then used in conjunction with a dictionary lookup method to match terms in the acupuncture location descriptions outlined by the WHO Standard;2) We employed NLP models based on machine learning and deep learning techniques, specifically utilizing Conditional Random Fields (CRFs) and Recurrent Neural Network (RNN) for training. The CRFs and RNN models achieved overall F1 scores of 0.955 and 0.945, respectively;3) We encoded the extracted entities into standardized concepts and enriched the baseline dictionary using these entities. In this study, a customized NLP tool was developed and validated for extracting acupuncture point location terms. This tool can be used in various informatics applications to support acupuncture research, education, and practices. © 2023 IEEE.

关键词： Acupuncture

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes 1

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

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

ISBN: (纸本)9798350342796

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization. © 2023 IEEE.

关键词： DNA

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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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Mapping Study Variables to Common data Elements Using GPT for Sheets: Towards Standardized data Collection and Sharing

Mapping Study Variables to Common Data Elements Using GPT fo...

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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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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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Effects of Tirzepatide in Type 2 Diabetes: Individual Variation and Relationship to Cardiometabolic Outcomes

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Journal of the American College of Cardiology 2025年第19期85卷 1858-1872页

作者： Aminorroaya, Arya Oikonomou, Evangelos K. Biswas, Dhruva Jastreboff, Ania M. Khera, Rohan Section of Cardiovascular Medicine Department of Internal Medicine Yale School of Medicine New Haven CT United States Cardiovascular Data Science (CarDS) Lab Yale School of Medicine New Haven CT United States Section of Endocrinology and Metabolism Department of Medicine Yale School of Medicine New Haven CT United States Section of Pediatric Endocrinology Department of Pediatrics Yale School of Medicine New Haven CT United States Yale Obesity Research Center (Y-Weight) Yale School of Medicine New Haven CT United States Center for Outcomes Research and Evaluation (CORE) Yale New Haven Hospital New Haven CT United States Section of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT United States Section of Health Informatics Department of Biostatistics Yale School of Public Health New Haven CT United States

Background: Tirzepatide—a dual GIP/GLP-1 receptor agonist—exerts pleiotropic effects on cardiometabolic health. Objectives: The authors sought to investigate the efficacy of tirzepatide in improving different cardiometabolic risk factors across individuals and subpopulations. Methods: Using an independent, global data-sharing and analytics platform, we performed an individual participant data meta-analysis by pooling data from 7 Phase 3 randomized clinical trials that compared tirzepatide with placebo or standard antihyperglycemic agents in individuals with type 2 diabetes. The study outcomes were the presence of a range of cardiometabolic abnormalities, representing components of metabolic syndrome (MetS) (elevated waist circumference, triglycerides, blood pressure, and fasting blood glucose, and decreased high-density lipoprotein cholesterol), as well as elevated body mass index and MetS (≥3 cardiometabolic abnormalities). Outcomes were modeled using mixed-effects models, with inverse probability weighting to account for study design differences. Results: We included 7,805 participants with a weighted median age of 59 years (Q1-Q3: 51-66 years) and 43.2% women. Over a weighted median treatment duration of 41.0 weeks, tirzepatide reduced the odds of all cardiometabolic abnormalities, ranging from 34% reduction for the odds of decreased high-density lipoprotein cholesterol (OR: 0.66 [95% CI: 0.52-0.84]) to 96% reduction in the odds of elevated body mass index (OR: 0.04 [95% CI:0.02-0.08]), and 72% reduction for the odds of MetS (OR: 0.28 [95% CI: 0.24-0.33]). Tirzepatide's superior efficacy in resolving MetS was consistent across demographic and clinical subpopulations, with higher efficacy in age <65 years vs ≥65 years, and in individuals without vs with baseline use of sodium-glucose cotransporter 2 inhibitors (P for interaction = 0.008 and 0.009, respectively). Conclusions: This pooled analysis suggests that tirzepatide may improve cardiometabolic abnormalities a

关键词： diabetes mellitus type 2 heart disease risk factors meta-analysis randomized controlled trial tirzepatide

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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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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification

Elevating Ocular Diagnosis: Harnessing the Power of Efficien...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Vankadari Mohith Kalpana Raja I. R. Oviya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven USA

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

In response to the evolving landscape of healthcare technology this study addresses the need for automated diagnostic tools in the field of ophthalmology. By utilizing deep learning methods specifically leveraging the EfficientNetV2S framework we achieve an overall accuracy rate of 94.6% in classifying images depicting common eye conditions like cataract, diabetic retinopathy, glaucoma, and normal cases. Our model exhibits capabilities across a diverse spectrum of medical conditions due to meticulous preprocessing and fine-tuning procedures. The results highlight the potential for automated technology integration in healthcare settings, which might improve patient care and treatment plans. This paper highlights the use of advanced neural network designs in the diagnosis of ocular illnesses, offering insights at the nexus of learning and medical image processing.

关键词： Glaucoma Deep learning Diabetic retinopathy Accuracy Computational modeling Neural networks Medical treatment

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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation

EMPATHIC: Emulating Human-Like Multimodal Personality Archit...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Varshini Devi I I. R. Oviya Kalpana Raja Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios.

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： I. R. Oviya Shanmukha Sravya N Kalpana Raja Department of Computer Science Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models.

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