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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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Using annotation for computerized support for fast skimming of cardiology electronic health record notes

Using annotation for computerized support for fast skimming ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Dehkordi, Mahshad Koohi H. Einstein, Andrew J. Zhou, Shuxin Elhanan, Gai Perl, Yehoshua Keloth, Vipina K. Geller, James Liu, Hao Njit Department of Computer Science NewarkNJ United States Irving Medical Center Cardiology Division Dept. of Medicine New YorkNY United States Desert Research Institute Renown Institute for Health Innovation RenoNV United States Yale School of Medicine Biomedical Informatics and Data Science New HavenCT United States Njit Department of Data Science NewarkNJ United States Montclair State University Department of Computer Science MontclairNJ United States

ISBN: (纸本)9798350337488

Under the circumstances prevalent in current healthcare, medical professionals such as physicians and nurses need to read large numbers of Electronic Health Record (EHR) notes. This demand fosters a situation in which providers typically do not read a whole note but quickly skim it, to capture its essential content. Consequently, by fast skimming, one may miss a critical medical fact. Annotation highlights important content in EHR notes, and enables healthcare professionals to perform fast skimming, thereby minimizing the risk of missing critical information, which is detrimental to patient care. We designed the Cardiology Interface Terminology (CIT) for the purpose of annotation of cardiology EHRs. We emphasize that by annotation we refer to highlighting important information in the EHR notes for enabling fast skimming rather than just recognizing names of diseases, drugs, etc. from Reference Terminologies as is usually done by existing Named Entity Recognition (NER) systems. The CIT design starts with the cardiology components of SNOMED CT. It is enhanced by mining phrases from cardiology EHRs, as potential CIT concepts, which are of higher granularity than SNOMED concepts. Machine learning (ML), the state of the art technique for mining concepts from EHRs, requires training data. However, there is no training data for designing CIT. In the first stage, we introduce an innovative semi-automatic method for mining concepts from EHRs, to replace costly manual mining. The only manual portion is the review of the automatically mined phrases, before their insertion as CIT concepts. The effectiveness of annotation of cardiology EHRs with CIT was evaluated utilizing proper metrics, and compared to annotation with SNOMED CT. In a future second stage, ML mining techniques will be used for enhancing CIT with extra concepts from EHRs, utilizing the concepts added in the first stage as training data. This work focuses on a novel semi-automated method to design the Cardiology I

关键词： annotation cardiology EHR fast skimming

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Single Cell Analysis of Adult Human Hematopoietic Stem and Progenitor Cells Identifies a Novel Lymphoid Primed Multipotent Progenitor That Expands in Relapsed Acute Myeloid Leukemia

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BLOOD 2021年第Sup1期138卷 3259-3259页

作者： Ediriwickrema, Asiri Luca, Bogdan A. Newman, Aaron M. Gentles, Andrew J. Majeti, Ravindra Department of Medicine Division of Hematology Stanford University Stanford CA Stanford Center for Biomedical Informatics Research Department of Medicine Stanford University Stanford CA Department of Biomedical Data Science Stanford University Stanford CA Departments of Medicine (Biomedical Informatics Research) and Biomedical Data Science Stanford University School of Medicine Stanford CA Department of Medicine Division of Hematology Cancer Institute and Institute for Stem Cell Biology and Regenerative Medicine Stanford University Stanford CA

Background: Hematopoietic stem and progenitor cells (HSPCs) are capable of generating all human blood cells. Although these cells have been extensively evaluated using both sorted (Corces et al. Nat. Genet. 2016) and single cell assays (Pellin et al. Nat. Commun. 2019), there remains significant uncertainty as to the degree of heterogeneity within HSPC subpopulations and their relevance to disease. The phenotypic diversity within HSPCs needs to be better characterized in order to understand the pathogenesis of numerous blood disorders including hematologic malignancies. To address this need, we extensively characterized healthy bone marrow mononuclear cells (BMMCs) with both whole transcriptome analysis (WTA) and surface marker evaluation. We hypothesized that by utilizing concurrent RNA and multiplexed (n=35) surface marker analysis, we could not only improve HSPC clustering but also characterize specific phenotypic states along unique hematopoietic differentiation trajectories. This analysis allowed us to identify new subpopulations within currently defined hematopoietic stem cells (HSCs), multipotent progenitors (MPPs) and lymphoid primed multipotent progenitors (LMPPs). Methods: Three healthy adult BMMC samples were stained with antibody conjugated oligonucleotides (BD Abseq) and analyzed using a nanowell cell capture system (BD Rhapsody). We filtered 8,070 high quality cells for 2,508 HSPCs, myeloid cells, and lymphocyte precursors. These cells were first clustered using matrix factorization in order to identify unique HSPC states. Novel clusters within the HSC, MPP, and LMPP subpopulations were purified using flow cytometry, and functionally evaluated with both in vitro and in vivo assays. Results: The antibody-derived tags (ADTs) obtained from BD Abseq comprised 33 of the most informative features (n=2000) and resulted in more stable clustering as determined by within sum of squares (WSS = 898 versus 934 for mRNA alone for 20 clusters). Additionally, we were

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A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications

arXiv

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arXiv 2024年

作者： Zhang, Gongbo Zhou, Yiliang Hu, Yan Xu, Hua Weng, Chunhua Peng, Yifan Department of Biomedical Informatics Columbia University New YorkNY United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Department of Biomedical Informatics & Data Science Yale School of Medicine New HavenCT United States

Objectives: Extracting PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method, PICOX, to extract overlapping PICO entities. Materials and Methods: PICOX first identifies entities by assessing whether a word marks the beginning or conclusion of an entity. Then, it uses a multi-label classifier to assign one or more PICO labels to a span candidate. PICOX was evaluated using one of the best-performing baselines, EBM-NLP, and three more datasets, i.e., PICO-Corpus and RCT publications on Alzheimer’s Disease or COVID-19, using entity-level precision, recall, and F1 scores. Results: PICOX achieved superior precision, recall, and F1 scores across the board, with the micro F1 score improving from 45.05 to 50.87 (p 0.01). On the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline and improved the micro recall score from 56.66 to 67.33. On the COVID-19 dataset, PICOX also outperformed the baseline and improved the micro F1 score from 77.10 to 80.32. On the AD dataset, PICOX demonstrated comparable F1 scores with higher precision when compared to the baseline. Conclusion: PICOX excels in identifying overlapping entities and consistently surpasses a leading baseline across multiple datasets. Ablation studies reveal that its data augmentation strategy effectively minimizes false positives and improves precision. © 2024, CC BY-NC-SA.

关键词： COVID-19

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Session Introduction: Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare 28

Session Introduction: Precision Medicine: Using Artificial I...

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28th Pacific Symposium on Biocomputing, PSB 2023

作者： Whirl-Carrillo, Michelle Brenner, Steven E. Chen, Jonathan H. Crawford, Dana C. Kidziński, Lukasz Ouyang, David Daneshjou, Roxana Department Of Biomedical Data Science Stanford School Of Medicine StanfordCA United States University Of California Berkeley BerkeleyCA United States Department Of Medicine Center For Biomedical Informatics Research Stanford School Of Medicine StanfordCA United States Department Of Population And Quantitative Health Sciences Case Western Reserve University ClevelandOH United States Bioclinica And Stanford University StanfordCA United States Smidt Heart Institute Cedars-Sinai Medical Center Los AngelesCA United States Departments Of Dermatology And Biomedical Data Science Stanford School Of Medicine StanfordCA United States

ISBN: (纸本)9789811270604

Precision medicine requires a deep understanding of complex biomedical and healthcare data, which is being generated at exponential rates and increasingly made available through public biobanks, electronic medical record systems and biomedical databases and knowledgebases. The complexity and sheer amount of data prohibit manual manipulation. Instead, the field depends on artificial intelligence approaches to parse, annotate, evaluate and interpret the data to enable applications to patient healthcare At the 2023 Pacific Symposium on Biocomputing (PSB) session entitled "Precision Medicine: Using Artificial Intelligence (AI) to improve diagnostics and healthcare", we spotlight research that develops and applies computational methodologies to solve biomedical problems. © 2022 The Authors.

关键词： Genome

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Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19:A Large-Scale Social Media Analysis with Natural Language Processing

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Health data science 2024年第1期4卷 152-161页

作者： Zhiyun Zhang Yining Hua Peilin Zhou Shixu Lin Minghui Li Yujie Zhang Li Zhou Yanhui Liao Jie Yang Department of Big Data in Health Science School of Public Health Zhejiang University School of MedicineHangzhouChina Department of Epidemiology Harvard T.H.Chan School of Public HealthBostonMAUSA Department of Biomedical Informatics Harvard Medical SchoolBostonMAUSA Division of General Internal Medicine and Primary Care Department of MedicineBrigham and Women’s HospitalBostonMAUSA Thrust of Data Science and Analytics The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina Department of Psychiatry Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouChina

Background:The COVID-19 pandemic has caused a disproportionate impact on the sexual and genderdiverse(SGD)*** with non-SGD populations,their social relations and health status are more vulnerable,whereas public health data regarding SGD are ***:To analyze the concerns and health status of SGD individuals,this cohort study leveraged 471,371,477 tweets from 251,455 SGD and 22,644,411 non-SGD users,spanning from 2020 February 1 to 2022 April *** outcome measures comprised the distribution and dynamics of COVID-related topics,attitudes toward vaccines,and the prevalence of ***:Topic analysis revealed that SGD users engaged more frequently in discussions related to“friends and family”(20.5%vs.13.1%,P<0.001)and“wear masks”(10.1%vs.8.3%,P<0.001)compared to non-SGD ***,SGD users exhibited a marked higher proportion of positive sentiment in tweets about vaccines,including Moderna,Pffzer,AstraZeneca,and Johnson&*** 102,464 users who self-reported COVID-19 diagnoses,SGD users disclosed signiffcantly higher frequencies of mentioning 61 out of 69 COVID-related symptoms than non-SGD users,encompassing both physical and mental health ***:The results provide insights into an understanding of the unique needs and experiences of the SGD community during the pandemic,emphasizing the value of social media data in epidemiological and public health research.

关键词： COV discussions users

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Evaluating 3D Printed VR Controller Prototypes to Increase VR Accessibility for Older Adults 3

Evaluating 3D Printed VR Controller Prototypes to Increase V...

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3rd IEEE International Conference on Human-Machine Systems, ICHMS 2022

作者： Gluck, Aaron Solini, Hannah Maiti, Kuntal Brinkley, Julian Clemson University School of Computing ClemsonSC United States Clemson University Human Factors Psychology ClemsonSC United States Clemson University Biomedical Data Science & Informatics ClemsonSC United States

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Virtual reality (VR) is transforming how technology benefits society, including the areas of training, socialization, and entertainment. While VR's growth has been substantial, VR hardware and applications are inaccessible in many ways, excluding a large population from experiencing its benefits. Current VR controllers potentially create an accessibility barrier for older adults who may experience confusion with the complexity of buttons, triggers, and joysticks. In the presented work, we recruited ten older adult participants to develop and evaluate 3D printed VR controller prototypes for usability and learnability. A series of participatory design focus groups discussed participants' needs, opinions, and design preferences, while usability scales evaluated the prototypes. Iterations were implemented between sessions based on this feedback to further refine the designs. Findings suggest that a simplified VR controller layout can decrease confusion and increase learnability, potentially improving older adults' ability to interact with virtual environments. © 2022 IEEE.

关键词： 3D printers

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PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023

Pacific Symposium on Biocomputing

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Pacific Symposium on Biocomputing 2023年第2023期 XI-XIII页

作者： Altman, Russ B. Hunter, Lawrence Ritchie, Marylyn D. Murray, Tiffany Klein, Teri E. Departments Of Bioengineering Genetics Medicine & Biomedical Data Science Stanford University United States Department Of Pharmacology University Of Colorado Health Sciences Center United States Department Of Genetics Institute For Biomedical Informatics University Of Pennsylvania United States Departments Of Biomedical Data Science & Medicine Stanford University United States

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Constructing an Automated Method for Interpreting Alzheimer’s Disease Based on Deep Learning Models and Image Processing 24

Constructing an Automated Method for Interpreting Alzheimer...

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

作者： Yi, Chia-Pin Ting, Hsien-Wei Yang, Tzu-Fang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 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

Alzheimer’s disease (AD) often presents only mild symptoms in its early stages, and as there is no direct diagnostic method currently available, many patients are diagnosed only after the condition has worsened. Considering that magnetic resonance imaging (MRI) can provide key information in the diagnosis of AD, but the condition may go undiagnosed under manual interpretation due to the subtleness of symptoms and the reliance on radiologists to interpret MRI images, this study aimed to develop an automated method for interpreting brain MR images, hoping to screen for potential AD patients. By combining image processing, the U-Net model, YOLOv8 model, various CNN model architectures, and ensemble data statistics, our method was able to identify features related to AD and predict whether a patient has the disease. Our method can process a complete set of MR images and automatically extract important slices for prediction, making it practical for medical applications. Furthermore, we tested the accuracy and reliability of the method with additional datasets. © 2024 Copyright ACM.

关键词： Magnetic resonance imaging

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University 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

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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