IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine最新文献

{"title":"Diurnal Pain Classification in Critically Ill Patients using Machine Learning on Accelerometry and Analgesic Data.","authors":"Jessica Sena, Sabyasachi Bandyopadhyay, Mohammad Tahsin Mostafiz, Andrea Davidson, Ziyuan Guan, Jesimon Barreto, Tezcan Ozrazgat-Baslanti, Patrick Tighe, Azra Bihorac, William Robson Schwartz, Parisa Rashidi","doi":"10.1109/bibm58861.2023.10385764","DOIUrl":"10.1109/bibm58861.2023.10385764","url":null,"abstract":"<p><p>Quantifying pain in patients admitted to intensive care units (ICUs) is challenging due to the increased prevalence of communication barriers in this patient population. Previous research has posited a positive correlation between pain and physical activity in critically ill patients. In this study, we advance this hypothesis by building machine learning classifiers to examine the ability of accelerometer data collected from daily wearables to predict self-reported pain levels experienced by patients in the ICU. We trained multiple Machine Learning (ML) models, including Logistic Regression, CatBoost, and XG-Boost, on statistical features extracted from the accelerometer data combined with previous pain measurements and patient demographics. Following previous studies that showed a change in pain sensitivity in ICU patients at night, we performed the task of pain classification separately for daytime and nighttime pain reports. In the pain versus no-pain classification setting, logistic regression gave the best classifier in daytime (AUC: 0.72, F1-score: 0.72), and CatBoost gave the best classifier at nighttime (AUC: 0.82, F1-score: 0.82). Performance of logistic regression dropped to 0.61 AUC, 0.62 F1-score (mild vs. moderate pain, nighttime), and CatBoost's performance was similarly affected with 0.61 AUC, 0.60 F1-score (moderate vs. severe pain, daytime). The inclusion of analgesic information benefited the classification between moderate and severe pain. SHAP analysis was conducted to find the most significant features in each setting. It assigned the highest importance to accelerometer-related features on all evaluated settings but also showed the contribution of the other features such as age and medications in specific contexts. In conclusion, accelerometer data combined with patient demographics and previous pain measurements can be used to screen painful from painless episodes in the ICU and can be combined with analgesic information to provide moderate classification between painful episodes of different severities.</p>","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2023 ","pages":"2207-2212"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10923604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140095293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transmission cluster characteristics of global, regional, and lineage-specific SARS-CoV-2 phylogenies.","authors":"Mattia Prosperi,&nbsp;Brittany Rife,&nbsp;Simone Marini,&nbsp;Marco Salemi","doi":"10.1109/bibm55620.2022.9995364","DOIUrl":"https://doi.org/10.1109/bibm55620.2022.9995364","url":null,"abstract":"<p><p>The SARS-CoV-2 pandemic has been presenting in periodic waves and multiple variants, of which some dominated over time with increased transmissibility. SARS-CoV-2 is still adapting in the human population, thus it is crucial to understand its evolutionary patterns and dynamics ahead of time. In this work, we analyzed transmission clusters and topology of SARS-CoV-2 phylogenies at the global, regional (North America) and clade-specific (Delta and Omicron) epidemic scales. We used the Nextstrain's nCov open global all-time phylogeny (September 2022, 2,698 strains, 2,243 for North America, 499 for Delta21A, and 543 for Omicron20M), with Nextstrain's clade annotation and Pango lineages. Transmission clusters were identified using Phylopart, DYNAMITE, and several tree imbalance measures were calculated, including staircase-ness, Sackin and Colless index. We found that the phylogenetic clustering profiles of the global epidemic have highest diversification at a distance threshold of 3% (divergence of 10, where the tree sampled median is 49). Phylopart and DYNAMITE clusters moderately-to-highly agree with the Pango nomenclature and the Nextstrain's clade. At the regional and clade-specific scale, transmission clustering profiles tend to flatten and similar clusters are found at distance thresholds between 0.05% and 25%. All the considered phylogenies exhibit high tree imbalance with respect to what expected in random phylogenies, suggesting short infection times and antigenic drift, perhaps due to progressive transition from innate to adaptive immunity in the population.</p>","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2022 ","pages":"2940-2944"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912475/pdf/nihms-1865883.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10721273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Document-level DDI relation extraction with document-entity embedding","authors":"Mingliang Dou, Jijun Tang, Fei Guo","doi":"10.1109/BIBM52615.2021.9669319","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669319","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"97 3","pages":"392-397"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72615105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Network Pharmacological Mechanism of Yizhiningshen Oral Liquid in the Treatment of Tic Disorders","authors":"Lulu Zhang, Zhe Huang, Lixin Yang, Shujuan Du, R. Duan, J. Yang, Bingwen Hu","doi":"10.1109/BIBM52615.2021.9669746","DOIUrl":"https://doi.org/10.1109/BIBM52615.2021.9669746","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"22 1","pages":"3856-3863"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87182422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of fuzzy sets, aggregation operators and multi agent systems to simulate COVID-19 transmission in a context of absence of barrier gestures and social distancing: application to an island region","authors":"Sébastien Régis, O. Manicom, A. Doncescu","doi":"10.1109/BIBM49941.2020.9313136","DOIUrl":"https://doi.org/10.1109/BIBM49941.2020.9313136","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"148 1","pages":"2298-2305"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77841778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the Medication Law of Traditional Chinese medicine treating Lumbago based on TCM electronic medical record","authors":"Jiao-Zhi Wang, Yong Xiao, Shaowu Shen, Y. Gui, Xuemei Lin, Xiao-Qiong Wang","doi":"10.1109/BIBM49941.2020.9313158","DOIUrl":"https://doi.org/10.1109/BIBM49941.2020.9313158","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"1 1","pages":"1598-1601"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75999938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Validation of a Yellow Fever Vaccine Model","authors":"C. R. B. Bonin, M. Lobosco, G. C. Fernandes, R. M. Martins, L. Camacho, L. Mota, S. Lima, A. C. Campi-Azevedo, O. Martins-Filho, Rodrigo Weber dos Santos","doi":"10.1109/bibm47256.2019.8983128","DOIUrl":"https://doi.org/10.1109/bibm47256.2019.8983128","url":null,"abstract":"An effective yellow fever vaccine has been available since 1937. Nevertheless, questions regarding its use remain poorly understood, such as the ideal dose to confer immunity against the disease, the need for booster dose, the optimal immunization schedule for immunocompetent, immunosuppressed, and children, among other issues. The objective of this work is to demonstrate that computational tools can be used to simulate different scenarios regarding yellow fever vaccination and the immune response of the individuals to this vaccine, thus assisting the response of some of these open questions. In this context, this work presents the results of a computational model of the human immune response to vaccination against yellow fever. The model takes into account essential cells and molecules of the human immune system, such as antigen-presenting cells, B and T lymphocytes, memory cells, and antibodies. The model was able to replicate the levels of antibodies obtained experimentally in different vaccination scenarios, allowing a quantitative validation with experimental data.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"31 1","pages":"2113-2120"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78525697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RNN-Based Alzheimer's Disease Prediction from Prodromal Stage using Diffusion Tensor Imaging","authors":"Matthew Velazquez, Rajaram Anantharaman, Salma Velazquez, Yugyung Lee","doi":"10.1109/bibm47256.2019.8983391","DOIUrl":"https://doi.org/10.1109/bibm47256.2019.8983391","url":null,"abstract":"Alzheimer's Disease is an irreversible, progressive brain disorder that slowly destroys cognitive abilities. In recent years, the relationship between the prodromal Mild Cognitive Impairment (MCI) stage and the Alzheimer's Disease (AD) stage has been extensively researched in hopes of finding a path towards early diagnosis. Early detection at the MCI stage can help determine appropriate treatment plans as well as assist in clinical trial enrollment as 32% of individuals with MCI will develop AD within 5 years. Computer vision studies leveraging Magnetic Resonance Imaging (sMRI, fMRI), Diffusion Tensor Imaging (DTI), and Positron Emission Tomography (PET) have led to encouraging results in classifying the different stages of AD. Studies around DTI specifically have shown that structural differences in white matter are prevalent between these stages. Rather than classification between stages, we propose a recurrent neural network model (RNN) based on the DTI modality for identifying the subset (32%) of individuals with Early Mild Cognitive Impairment (EMCI) that will develop AD. Our results are state-of-the-art and demonstrate high accuracy in determining which individuals will develop AD within the next 5-7 years. Additionally, we propose our augmentation methods for DTI data as well as our classification accuracy across the traditional AD stage categories.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"54 1","pages":"1665-1672"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81362005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Data Mining Approach for Biomarker Discovery Using Transcriptomics in Endometriosis","authors":"Sadia Akter, Dong Xu, S. Nagel, T. Joshi","doi":"10.1109/BIBM.2018.8621150","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621150","url":null,"abstract":"Endometriosis is a complex and common gynecological disorder affecting 5-10% of reproductive age women. Due to the lack of definitive diagnostic symptoms and expensive invasive procedures for diagnosing endometriosis, the average time for the diagnosis can be up to 10 years. This diagnostic latency has a very significant impact on endometriosis patients, and early diagnosis is desired in order to increase quality of life. In this study, we analyzed 38 RNA-seq transcriptomics samples (16 endometriosis and 22 controls) and identified genomic signatures as potential biomarkers. We applied innovative data mining approaches including a combination of a normalization techniques, generalized linear model (GLM) for identifying the differentially expressed genes and a decision tree algorithm for constructing models with higher predictive performance. A total of 5 candidate genes were identified as potential biomarkers of endometriosis, which outperformed the results from the Biosigner tool using a leave-one-out cross-validation technique. Our data mining approach can successfully distinguish the endometriosis patients from the non-endometriosis and can be potentially used as a prediction-based diagnostic tool for other diseases in future.","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"19 1","pages":"969-972"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81473540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter/Intra-Constraints Optimization for Fast Vessel Enhancement in X-ray Angiographic Image Sequence","authors":"Chenbing Du, Shuang Song, Danni Ai, Hong Song, Yong Huang, Yongtian Wang, Jian Yang","doi":"10.1109/BIBM.2018.8621540","DOIUrl":"https://doi.org/10.1109/BIBM.2018.8621540","url":null,"abstract":"","PeriodicalId":73283,"journal":{"name":"IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine","volume":"21 1","pages":"859-863"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74720733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}