... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics最新文献_第3页

Structure-based Method for Predicting Deleterious Missense SNPs. 基于结构的预测有害错义SNPs的方法。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/bhi.2019.8834504

Boshen Wang, Wei Tian, Xue Lei, Alan Perez-Rathke, Yan Yuan Tseng, Jie Liang

{"title":"Structure-based Method for Predicting Deleterious Missense SNPs.","authors":"Boshen Wang, Wei Tian, Xue Lei, Alan Perez-Rathke, Yan Yuan Tseng, Jie Liang","doi":"10.1109/bhi.2019.8834504","DOIUrl":"10.1109/bhi.2019.8834504","url":null,"abstract":"Missense SNPs are key factors contributing towards many Mendelian disorders and complex diseases. Identifying whether a single amino acid substitution will lead to pathological effects is important for interpreting personal genome and for precision medicine. In this study, we describe a novel method for predicting whether a missense SNP likely brings about pathological effects. Our approach integrates sequence information, biophysical properties, and topological properties of protein structures. In our test dataset consisting of 500 deleterious variants and 500 neutral, our method achieves an accuracy of 0.823. The ROC curve of model has an AUC of 0.910. Our methods outperforms two well known methods, and is comparable with the widely used Polyphen-2 method, while requiring a much smaller amount (approximately 25%) of training data. Our method can be used to aid in distinguishing driver and passenger mutations in cancer and in assessing missense mutations assocaited with rare diseases. It can also be used to identifying mutations in rare disease where only limited patient exome data exsit.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8204715/pdf/nihms-1707462.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39240733","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}

引用次数: 0

CCi-MOBILE: Design and Evaluation of a Cochlear Implant and Hearing Aid Research Platform for Speech Scientists and Engineers. CCi MOBILE：为言语科学家和工程师设计和评估耳蜗植入物和助听器研究平台。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/BHI.2019.8834652

John H L Hansen, Hussnain Ali, Juliana N Saba, Charan M C Ram, Nursadul Mamun, Ria Ghosh, Avamarie Brueggeman

{"title":"CCi-MOBILE: Design and Evaluation of a Cochlear Implant and Hearing Aid Research Platform for Speech Scientists and Engineers.","authors":"John H L Hansen, Hussnain Ali, Juliana N Saba, Charan M C Ram, Nursadul Mamun, Ria Ghosh, Avamarie Brueggeman","doi":"10.1109/BHI.2019.8834652","DOIUrl":"10.1109/BHI.2019.8834652","url":null,"abstract":"Hearing loss is an increasingly prevalent condition resulting from damage to the inner ear which causes a reduction in speech intelligibility. The societal need for assistive hearing devices has increased exponentially over the past two decades; however, actual human performance with such devices has only seen modest gains relative to advancements in digital signal processing (DSP) technology. A major challenge with clinical hearing technologies is the limited ability to run complex signal processing algorithms requiring high computation power. The CCi-MOBILE platform, developed at UT-Dallas, provides the research community with an open-source, flexible, easy-to-use, software-mediated, powerful computing research interface to conduct a wide variety of listening experiments. The platform supports cochlear implants (CIs) and hearing aids (HAs) independently, as well as bimodal hearing (i.e., a CI in one ear and HA in the contralateral ear). The platform is ideally suited to address hearing research for: both quiet and naturalistic noisy conditions, sound localization, and lateralization. The platform uses commercially available smartphone/tablet devices as portable sound processors and can provide bilateral electric and acoustic stimulation. The hardware components, firmware, and software suite are presented to demonstrate safety to the speech scientist and CI/HA user, highlight user-specificity, and outline various applications of the platform for research.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874365/pdf/nihms-1019990.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685542","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}

引用次数: 0

A 0.5 nW Analog ECG Processor for Real Time R-wave Detection Based on Pan-Tompkins Algorithm 基于Pan-Tompkins算法的0.5 nW模拟心电实时r波检测处理器

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 DOI: 10.1109/BHI.2019.8834508

Cihan Berk Gungor, H. Toreyin

引用次数: 4

Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports. 从病理报告中提取信息的癌症登记处的深度迁移学习。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/bhi.2019.8834586

Mohammed Alawad, Shang Gao, John Qiu, Noah Schaefferkoetter, Jacob D Hinkle, Hong-Jun Yoon, J Blair Christian, Xiao-Cheng Wu, Eric B Durbin, Jong Cheol Jeong, Isaac Hands, David Rust, Georgia Tourassi

{"title":"Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports.","authors":"Mohammed Alawad, Shang Gao, John Qiu, Noah Schaefferkoetter, Jacob D Hinkle, Hong-Jun Yoon, J Blair Christian, Xiao-Cheng Wu, Eric B Durbin, Jong Cheol Jeong, Isaac Hands, David Rust, Georgia Tourassi","doi":"10.1109/bhi.2019.8834586","DOIUrl":"https://doi.org/10.1109/bhi.2019.8834586","url":null,"abstract":"Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitate cross-registry knowledge sharing. Specifically, we performed a series of experiments to determine whether a CNN trained with single-registry data is capable of transferring knowledge to another registry or whether developing a cross-registry knowledge database produces a more effective and generalizable model. Using data from two cancer registries and primary tumor site and topography as the information extraction task of interest, our study showed that TL results in 6.90% and 17.22% improvement of classification macro F-score over the baseline single-registry models. Detailed analysis illustrated that the observed improvement is evident in the low prevalence classes.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bhi.2019.8834586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33458011","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}

引用次数: 13

A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation. 使用受控空间变换的心电信号偏差分析框架

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/bhi.2019.8834617

Jiaming Chen, Ali Valehi, Fatemeh Afghah, Abolfazl Razi

{"title":"A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation.","authors":"Jiaming Chen, Ali Valehi, Fatemeh Afghah, Abolfazl Razi","doi":"10.1109/bhi.2019.8834617","DOIUrl":"10.1109/bhi.2019.8834617","url":null,"abstract":"Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symme- try in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7552434/pdf/nihms-1634684.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38499539","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}

引用次数: 0

Feature Exploration and Causal Inference on Mortality of Epilepsy Patients Using Insurance Claims Data. 基于保险理赔数据的癫痫患者死亡率特征探索及因果推断。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/bhi.2019.8834638

Yuanda Zhu, Hang Wu, May D Wang

{"title":"Feature Exploration and Causal Inference on Mortality of Epilepsy Patients Using Insurance Claims Data.","authors":"Yuanda Zhu, Hang Wu, May D Wang","doi":"10.1109/bhi.2019.8834638","DOIUrl":"https://doi.org/10.1109/bhi.2019.8834638","url":null,"abstract":"Approximately 0.5-1% of the global population is afflicted with epilepsy, a neurological disorder characterized by repeated seizures. Sudden Unexpected Death in Epilepsy (SUDEP) is a poorly understood complication that claims the lives of nearly 1-in-1000 epilepsy patients every year. This paper aims to explore diagnosis codes, demographic and payment features on mortality of epilepsy patients. We design a mortality prediction model with diagnosis codes and non-diagnosis features extracted from US commercial insurance claims data. We present classification accuracy of 0.91 and 0.85 by using different feature vectors. After analyzing the aforementioned features in prediction model, we extend the work to causal inference between modified diagnosis codes and selected non-diagnosis features. The uplift test of causal inference using three algorithms indicates that a patient is more likely to survive if upgrading from a low-coverage healthcare plan into a high-coverage plan.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bhi.2019.8834638","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38078197","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}

引用次数: 6

Improved Prediction on Heart Transplant Rejection Using Convolutional Autoencoder and Multiple Instance Learning on Whole-Slide Imaging. 利用卷积自动编码器和全切片成像上的多实例学习改进心脏移植排斥反应预测

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/bhi.2019.8834632

Yuanda Zhu, May D Wang, Li Tong, Shriprasad R Deshpande

{"title":"Improved Prediction on Heart Transplant Rejection Using Convolutional Autoencoder and Multiple Instance Learning on Whole-Slide Imaging.","authors":"Yuanda Zhu, May D Wang, Li Tong, Shriprasad R Deshpande","doi":"10.1109/bhi.2019.8834632","DOIUrl":"10.1109/bhi.2019.8834632","url":null,"abstract":"Heart transplant rejection is one major threat for the survival of patients with a heart transplant. Endomyocardial biopsies are effective in showing signs of heart transplant rejection even before patients have any symptoms. Manually examining the tissue samples is costly, time-consuming and error-prone. With recent advances in deep learning (DL) based image processing methods, automatic training and prediction on heart transplant rejection using whole-slide images expect to be promising. This paper develops an advanced pipeline for quality control, feature extraction, clustering and classification. We first implement a stacked convolutional autoencoder to extract feature maps for each tile; we then incorporate multiple instance learning (MIL) with dimensionality reduction and unsupervised clustering prior to classification. Our results show that utilizing unsupervised clustering after feature extraction can achieve higher classification results while preserving the capability for multi-class classification.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7310716/pdf/nihms-1595294.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38078196","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}

引用次数: 0

Alterations in Chromatin Folding Patterns in Cancer Variant-Enriched Loci. 癌症变异富集位点中染色质折叠模式的改变。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/bhi.2019.8834565

Alan Perez-Rathke, Samira Mali, Lin Du, Jie Liang

引用次数: 4

ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention. ECGNET：通过深度视觉注意力学习检测心房颤动时的注意点。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/BHI.2019.8834637

Seyed Sajad Mousavi, Fatemah Afghah, Abolfazl Razi, U Rajendra Acharya

{"title":"ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention.","authors":"Seyed Sajad Mousavi, Fatemah Afghah, Abolfazl Razi, U Rajendra Acharya","doi":"10.1109/BHI.2019.8834637","DOIUrl":"10.1109/BHI.2019.8834637","url":null,"abstract":"The complexity of the patterns associated with atrial fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the application of current signal processing and shallow machine learning approaches to accurately detect this condition. Deep neural networks have shown to be very powerful to learn the non-linear patterns in various problems such as computer vision tasks. While deep learning approaches have been utilized to learn complex patterns related to the presence of AF in electrocardiogram (ECG) signals, they can considerably benefit from knowing which parts of the signal is more important to focus on during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect the presence of AF in the ECG signals. The first channel takes in an ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the same ECG signal to consider all features of the entire signal. Besides improving detection accuracy, this model can guide the physicians via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. The experimental results confirm that the proposed model significantly improves the performance of AF detection on well-known MIT-BIH AF database with 5-s ECG segments (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40%).","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570949/pdf/nihms-1634679.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38614878","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}

引用次数: 0

Prioritization of Cognitive Assessments in Alzheimer's Disease via Learning to Rank using Brain Morphometric Data. 通过使用脑形态测量数据学习排序来确定阿尔茨海默病认知评估的优先级。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-05-01 Epub Date: 2019-09-12 DOI: 10.1109/BHI.2019.8834618

Bo Peng, Xiaohui Yao, Shannon L Risacher, Andrew J Saykin, Li Shen, Xia Ning

引用次数: 2