... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics最新文献_第4页

Using Closure Tables to Enable Cross-Querying of Ontologies in Database-Driven Applications. 在数据库驱动的应用程序中使用闭包表实现本体的交叉查询。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/BHI.2017.7897313

Daniel R Harris, Darren W Henderson, Jeffery C Talbert

引用次数: 3

Detection of Nuclei in H&E Stained Sections Using Convolutional Neural Networks. 卷积神经网络检测H&E染色切片细胞核。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/BHI.2017.7897216

Mina Khoshdeli, Richard Cong, Bahram Parvin

{"title":"Detection of Nuclei in H&E Stained Sections Using Convolutional Neural Networks.","authors":"Mina Khoshdeli, Richard Cong, Bahram Parvin","doi":"10.1109/BHI.2017.7897216","DOIUrl":"https://doi.org/10.1109/BHI.2017.7897216","url":null,"abstract":"Detection of nuclei is an important step in phenotypic profiling of histology sections that are usually imaged in bright field. However, nuclei can have multiple phenotypes, which are difficult to model. It is shown that convolutional neural networks (CNN)s can learn different phenotypic signatures for nuclear detection, and that the performance is improved with the feature-based representation of the original image. The feature-based representation utilizes Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Several combinations of input data representations are evaluated to show that by LoG representation, detection of nuclei is advanced. In addition, the efficacy of CNN for vesicular and hyperchromatic nuclei is evaluated. In particular, the frequency of detection of nuclei with the vesicular and apoptotic phenotypes is increased. The overall system has been evaluated against manually annotated nuclei and the F-Scores for alternative representations have been reported.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2017 ","pages":"105-108"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BHI.2017.7897216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35060341","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: 31

Mining standardized neurological signs and symptoms data for concussion identification. 挖掘标准化的神经体征和症状数据，以识别脑震荡。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/bhi.2017.7897261

Janani Venugopalan, Michelle C LaPlaca, May D Wang

{"title":"Mining standardized neurological signs and symptoms data for concussion identification.","authors":"Janani Venugopalan, Michelle C LaPlaca, May D Wang","doi":"10.1109/bhi.2017.7897261","DOIUrl":"10.1109/bhi.2017.7897261","url":null,"abstract":"The Centers for Disease Control estimate that 1.6 to 3.8 million concussions occur in sports and recreational activities annually. Studies have shown that concussions increase the risk of future injuries and mild cognitive disorders. Despite extensive research on sports related concussion risk factors, the factors which are most predictive of concussion outcome and recovery time course remain unknown. In order to overcome the issue of physician bias and to identify the factors which can best predict concussion diagnosis, we propose a multi-variate logistic regression based analysis. We demonstrate our results on a dataset with 126 subjects (ages 12-31). Our results indicate that among 322 features, our model selected 27-29 features which included a history of playing sports, history of a previous concussion, drowsiness, nausea, trouble focusing as measured by a common symptom list, and oculomotor function. The features picked using our model were found to be highly predictive of concussions and gave a prediction performance accuracy greater than 90%, Matthews correlation coefficient greater than 0.8 and the area under the curve greater than 0.95.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2017 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7375411/pdf/nihms-1595884.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38181782","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

Improving Multi-class Classification for Endomicroscopic Images by Semi-supervised Learning. 通过半监督学习改进内窥镜图像的多类分类

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/bhi.2017.7897191

Hang Wu, Li Tong, May D Wang

引用次数: 0

Intelligent Mortality Reporting with FHIR. FHIR智能死亡率报告。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-03-13 DOI: 10.1109/BHI.2017.7897235

Ryan A Hoffman, Hang Wu, Janani Venugopalan, Paula Braun, May D Wang

引用次数: 16

On Quantifying Diffusion of Health Information on Twitter. 论Twitter上健康信息的量化传播。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/BHI.2017.7897311

Gokhan Bakal, Ramakanth Kavuluru

{"title":"On Quantifying Diffusion of Health Information on Twitter.","authors":"Gokhan Bakal, Ramakanth Kavuluru","doi":"10.1109/BHI.2017.7897311","DOIUrl":"https://doi.org/10.1109/BHI.2017.7897311","url":null,"abstract":"With the increasing use of digital technologies, online social networks are emerging as major means of communication. Recently, social networks such as Facebook and Twitter are also being used by consumers, care providers (physicians, hospitals), and government agencies to share health related information. The asymmetric user network and the short message size have made Twitter particularly popular for propagating health related content on the Web. Besides tweeting on their own, users can choose to retweet particular tweets from other users (even if they do not follow them on Twitter.) Thus, a tweet can diffuse through the Twitter network via the follower-friend connections. In this paper, we report results of a pilot study we conducted to quantitatively assess how health related tweets diffuse in the directed follower-friend Twitter graph through the retweeting activity. Our effort includes (1). development of a retweet collection and Twitter retweet graph formation framework and (2). a preliminary analysis of retweet graphs and associated diffusion metrics for health tweets. Given the ambiguous nature (due to polysemy and sarcasm) of health relatedness of tweets collected with keyword based matches, our initial study is limited to ≈ 200 health related tweets (which were manually verified to be on health topics) each with at least 25 retweets. To our knowledge, this is first attempt to study health information diffusion on Twitter through retweet graph analysis.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2017 ","pages":"485-488"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BHI.2017.7897311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35192142","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: 10

Phenotyping Hypotensive Patients in Critical Care Using Hospital Discharge Summaries. 利用出院摘要对重症监护中的低血压患者进行表型分析。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/BHI.2017.7897290

Yang Dai, Sharukh Lokhandwala, William Long, Roger Mark, Li-Wei H Lehman

{"title":"Phenotyping Hypotensive Patients in Critical Care Using Hospital Discharge Summaries.","authors":"Yang Dai, Sharukh Lokhandwala, William Long, Roger Mark, Li-Wei H Lehman","doi":"10.1109/BHI.2017.7897290","DOIUrl":"10.1109/BHI.2017.7897290","url":null,"abstract":"Among critically-ill patients, hypotension represents a failure in compensatory mechanisms and may lead to organ hypoperfusion and failure. In this work, we adopt a data-driven approach for phenotype discovery and visualization of patient similarity and cohort structure in the intensive care unit (ICU). We used Hierarchical Dirichlet Process (HDP) as a nonparametric topic modeling technique to automatically learn a d-dimensional feature representation of patients that captures the latent \"topic\" structure of diseases, symptoms, medications, and findings documented in hospital discharge summaries. We then used the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm to convert the d-dimensional latent structure learned from HDP into a matrix of pairwise similarities for visualizing patient similarity and cohort structure. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluated the clinical utility of the discovered topic structure in phenotyping critically-ill patients who experienced hypotensive episodes. Our results indicate that the approach is able to reveal clinically interpretable clustering structure within our cohort and may potentially provide valuable insights to better understand the association between disease phenotypes and outcomes.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2017 ","pages":"401-404"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473943/pdf/nihms867204.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35102763","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

Analyzing the Usage of Standards in Radiation Therapy Clinical Studies. 分析放射治疗临床研究中标准的使用情况。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/BHI.2017.7897277

Y Zhen, Y Jiang, L Yuan, J Kirkpartrick, J Wu, Y Ge

{"title":"Analyzing the Usage of Standards in Radiation Therapy Clinical Studies.","authors":"Y Zhen, Y Jiang, L Yuan, J Kirkpartrick, J Wu, Y Ge","doi":"10.1109/BHI.2017.7897277","DOIUrl":"10.1109/BHI.2017.7897277","url":null,"abstract":"Standards for scoring adverse effects after radiation therapy (RT) is crucial for integrated, consistent, and accurate analysis of toxicity results at large scale and across multiple studies. This project aims to investigate the usage of the three most commonly used standards in published RT clinical studies by developing a text-mining based analysis method. We develop and compare two text-mining methods, one based on regular expressions and one based on Naïve Bayes Classifier, to analyze published full articles in terms of their adoption of standards in RT. The full dataset includes published articles identified in MEDLINE between January 2010 and August 2015. A radiation oncology physician reviewed all the articles in the training/validation subset and produced the usage trending data manually as gold standard for validation. The regular-expression based method reported classifications and overall usage trends that are comparable to those of the domain expert. The CTCAE standard is becoming the overall most commonly used standards over time, but the pace of adoption seems very slow. Further examination of the results indicates that the usage vary by disease type. It suggests that further efforts are needed to improve and harmonize the standards for adverse effects scoring in RT research community.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2017 ","pages":"349-352"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5913419/pdf/nihms959691.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36056709","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

Estimating Patient's Health State Using Latent Structure Inferred from Clinical Time Series and Text. 利用临床时间序列和文本推断的潜在结构估计患者的健康状况。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/BHI.2017.7897302

Aaron Zalewski, William Long, Alistair E W Johnson, Roger G Mark, Li-Wei H Lehman

{"title":"Estimating Patient's Health State Using Latent Structure Inferred from Clinical Time Series and Text.","authors":"Aaron Zalewski, William Long, Alistair E W Johnson, Roger G Mark, Li-Wei H Lehman","doi":"10.1109/BHI.2017.7897302","DOIUrl":"https://doi.org/10.1109/BHI.2017.7897302","url":null,"abstract":"Modern intensive care units (ICUs) collect large volumes of data in monitoring critically ill patients. Clinicians in the ICUs face the challenge of interpreting large volumes of high-dimensional data to diagnose and treat patients. In this work, we explore the use of Hierarchical Dirichlet Processes (HDP) as a Bayesian nonparametric framework to infer patients' states of health by combining multiple sources of data. In particular, we employ HDP to combine clinical time series and text from the nursing progress notes in a probabilistic topic modeling framework for patient risk stratification. Given a patient cohort, we use HDP to infer latent \"topics\" shared across multimodal patient data from the entire cohort. Each topic is modeled as a multinomial distribution over a vocabulary of codewords, defined over heterogeneous data sources. We evaluate the clinical utility of the learned topic structure using the first 24-hour ICU data from over 17,000 adult patients in the MIMIC-II database to estimate patients' risks of in-hospital mortality. Our results demonstrate that our approach provides a viable framework for combining different data modalities to model patient's states of health, and can potentially be used to generate alerts to identify patients at high risk of hospital mortality.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2017 ","pages":"449-452"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BHI.2017.7897302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35103722","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

Predicting Heart Rejection Using Histopathological Whole-Slide Imaging and Deep Neural Network with Dropout. 利用组织病理学全切片成像和带Dropout的深度神经网络预测心脏排斥反应。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2017-02-01 Epub Date: 2017-04-13 DOI: 10.1109/bhi.2017.7897190

Li Tong, Ryan Hoffman, Shriprasad R Deshpande, May D Wang

{"title":"Predicting Heart Rejection Using Histopathological Whole-Slide Imaging and Deep Neural Network with Dropout.","authors":"Li Tong, Ryan Hoffman, Shriprasad R Deshpande, May D Wang","doi":"10.1109/bhi.2017.7897190","DOIUrl":"https://doi.org/10.1109/bhi.2017.7897190","url":null,"abstract":"Cardiac allograft rejection is one major limitation for long-term survival for patients with heart transplants. The endomyocardial biopsy is one gold standard to screen heart rejection for patients that have heart transplantation. However, manual identification of heart rejection is expensive and time-consuming. With the development of imaging processing techniques and machine learning tools, automatic prediction of heart rejection using whole-slide images is one promising approach to improve the care of patients with heart transplants. In this paper, we first develop a histopathological whole-slide image processing pipeline to extract features automatically. Then, we construct deep neural networks with and without regularization and dropout to classify the patients into nonrejection and rejection respectively. Our results show that neural networks with regularization and dropout can significantly reduce overfitting and achieve more stable accuracies.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2017 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bhi.2017.7897190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38104358","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: 15