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7th International Conference on Image Information Processing, ICIIP 2023

作者： Mohith, Vankadari Raja, Kalpana Oviya, I.R. Chennai India Yale University Section For Biomedical Informatics And Data Science School of Medicine New Haven United States

ISBN: (纸本)9798350371406

Diabetic retinopathy is a serious medical disorder that, if left untreated, can result in visual impairment or blindness. The precise and timely categorization of its severity is critical for appropriate medical management. This research describes an automated method for determining the severity of diabetic retinopathy using deep learning algorithms. The suggested methodology adopts a two-step process for identifying diabetic retinopathy and then categorizing its severity. In this technique, two pre-trained deep learning models, InceptionResNetV2 and DenseNet121, are used. For training and assessment purposes, the 'APTOS 2019 Blindness Detection' dataset which consists of five severity classes is used as the foundation. To overcome class imbalance, data augmentation approaches are used to improve the model's performance. The results of the experiment show that the strategy is effective, with an accuracy of 83.10 % and an F1 score of 82.77 %. This study contributes to the development of automated systems for assessing diabetic retinopathy, perhaps enhancing early detection and treatment. © 2023 IEEE.

关键词： Medical imaging

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Stroke Prediction Using Machine Learning 5

Stroke Prediction Using Machine Learning

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5th IEEE International Conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Abinandhini, D.M. Kumar, Aman Teja, Gudi Vishnu Sukesh, Mandava Divya, S. Chauhan, Naman Oviya, I.R. Raja, Kalpana Chennai India School of Medicine Yale University New Haven Section for Biomedical Informatics and Data Science CT United States

ISBN: (纸本)9798350305449

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%. © 2023 IEEE.

关键词： Logistic regression

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ML Based Phenotype Analysis Using Differential Gene Expression data in Schizophrenia 4

ML Based Phenotype Analysis Using Differential Gene Expressi...

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4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Aadhithyan, D. Shabarirajan, K.J. Maniappan, Varun Shivanesh, B. Divya, S. Praghaadeesh, R. Raja, Kalpana Oviya, I.R. Chennai India School Of Medicine Yale University Section For Biomedical Informatics And Data Science New HavenCT United States

ISBN: (纸本)9798350384628

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. © 2023 IEEE.

关键词： Machine learning

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JAMIA at 30: looking back and forward

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Journal of the American Medical informatics Association 2024年第1期31卷 1-9页

作者： Stead, William W. Miller, Randolph A. Ohno-Machado, Lucila Bakken, Suzanne Department of Biomedical Informatics Department of Medicine Vanderbilt University Medical Center Nashville United States Department of Biomedical Informatics Vanderbilt University School of Medicine Alexandria United States Section of Biomedical Informatics & Data Science Yale University School of Medicine New Haven CT 06520 United States School of Nursing Department of Biomedical Informatics Data Science Institute Columbia University NY 10032 NY United States

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Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Class Emotion Classification on Code-Mixed Text Messages 13

Findings of WASSA 2023 Shared Task: Multi-Label and Multi-Cl...

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13th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2023

作者： Bölücü, Necva Ameer, Iqra Al Bataineh, Ali Xu, Hua Data61 CSIRO Sydney Australia Division of Science and Engineering Penn State University AbingtonPA United States Norwich University United States Section of Biomedical Informatics and Data Science Yale School of Medicine United States

ISBN: (纸本)9781959429876

We present the results of the WASSA 2023 Shared-Task 2: Emotion Classification on codemixed text messages (Roman Urdu + English), which included two tracks for emotion classification: multi-label and multi-class. The participants were provided with a dataset of codemixed SMS messages in English and Roman Urdu labeled with 12 emotions for both tracks. A total of 5 teams (19 team members) participated in the shared task. We summarized the methods, resources, and tools used by the participating teams. We also made the data freely available for further improvements to the task. © 2023 Association for Computational Linguistics.

关键词： Classification (of information)

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Elevating Ocular Diagnosis: Harnessing the Power of EfficientNet for Eye Disease Classification 3

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

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

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

ISBN: (纸本)9798350372120

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. © 2024 IEEE.

关键词： Ophthalmology

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Development of a Natural Language Processing Tool to Extract Acupuncture Point Location Terms 11

Development of a Natural Language Processing Tool to Extract...

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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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EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation 14

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

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14th International Conference on Cloud Computing, data science and Engineering, Confluence 2024

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

ISBN: (纸本)9798350344837

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. © 2024 IEEE.

关键词： Emotion Recognition

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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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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

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

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

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. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

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