... 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

DeepDDK: A Deep Learning based Oral-Diadochokinesis Analysis Software. DeepDDK:一个基于深度学习的口头对话分析软件。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2019-01-01 Epub Date: 2019-09-12 DOI: 10.1109/bhi.2019.8834506

Yang Yang Wang, Ke Gao, Yunxin Zhao, Mili Kuruvilla-Dugdale, Teresa E Lever, Filiz Bunyak

{"title":"DeepDDK: A Deep Learning based Oral-Diadochokinesis Analysis Software.","authors":"Yang Yang Wang, Ke Gao, Yunxin Zhao, Mili Kuruvilla-Dugdale, Teresa E Lever, Filiz Bunyak","doi":"10.1109/bhi.2019.8834506","DOIUrl":"https://doi.org/10.1109/bhi.2019.8834506","url":null,"abstract":"Oromotor dysfunction caused by neurological disorders can result in significant speech and swallowing impairments. Current diagnostic methods to assess oromotor function are subjective and rely on perceptual judgments by clinicians. In particular, the widely used oral-diadochokinesis (oral-DDK) test, which requires rapid, alternate repetitions of speech-based syllables, is conducted and interpreted differently among clinicians. It is therefore prone to inaccuracy, which results in poor test reliability and poor clinical application. In this paper, we present a deep learning based software to extract quantitative data from the oral DDK signal, thereby transforming it into an objective diagnostic and treatment monitoring tool. The proposed software consists of two main modules: a fully automated syllable detection module and an interactive visualization and editing module that allows inspection and correction of automated syllable units. The DeepDDK software was evaluated on speech files corresponding to 9 different DDK syllables (e.g., \"Pa\", \"Ta\", \"Ka\"). The experimental results show robustness of both syllable detection and localization across different types of DDK speech tasks.","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":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bhi.2019.8834506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38326108","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: 4

A Social Cognitive Theory-based Framework for Monitoring Medication Adherence Applied to Endocrine Therapy in Breast Cancer Survivors. 基于社会认知理论的用药依从性监测框架应用于乳腺癌幸存者的内分泌治疗。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2018-03-01 Epub Date: 2018-04-09 DOI: 10.1109/BHI.2018.8333422

Mehdi Boukhechba, Sonia Baee, Alicia L Nobles, Jiaqi Gong, Kristen Wells, Laura E Barnes

{"title":"A Social Cognitive Theory-based Framework for Monitoring Medication Adherence Applied to Endocrine Therapy in Breast Cancer Survivors.","authors":"Mehdi Boukhechba, Sonia Baee, Alicia L Nobles, Jiaqi Gong, Kristen Wells, Laura E Barnes","doi":"10.1109/BHI.2018.8333422","DOIUrl":"10.1109/BHI.2018.8333422","url":null,"abstract":"Poor adherence to long-term therapies for chronic diseases, such as cancer, compromises effectiveness of treatment and increases the likelihood of disease progression, making medication adherence a critical issue in population health. While the field has documented many eers to adherence to medication, it has also come up with few efficacious solutions to medication adherence, indicating that new and innovative approaches are needed. In this paper, we evaluate medication-taking behaviors based on social cognitive theory (SCT), presenting patterns of adherence stratified across SCT constructs in 33 breast cancer survivors over an 8-month period. Findings indicate that medication adherence is a very personal experience influenced by many simultaneously interacting factors, and a deeper contextual understanding is needed to understand and develop interventions targeting non-adherence.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2018 ","pages":"275-278"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983047/pdf/nihms956367.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36189160","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

Quantification of Biological Responses as Predictors of Cognitive Outcome after Developmental TBI. 定量生物学反应作为发展性脑损伤后认知预后的预测因子。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2018-03-01 Epub Date: 2018-04-09 DOI: 10.1109/BHI.2018.8333448

Saman Sargolzaei, Yan Cai, Deborah Lee, Neil G Harris, Christopher C Giza

{"title":"Quantification of Biological Responses as Predictors of Cognitive Outcome after Developmental TBI.","authors":"Saman Sargolzaei, Yan Cai, Deborah Lee, Neil G Harris, Christopher C Giza","doi":"10.1109/BHI.2018.8333448","DOIUrl":"https://doi.org/10.1109/BHI.2018.8333448","url":null,"abstract":"Successful translational studies within the field of Traumatic Brain Injury (TBI) are concerned with determining reliable markers of injury outcome at chronic time points. Determination of injury severity following Fluid Percussion Injury (FPI) has long been limited to the measured atmospheric pressure associated with the delivered pulse. Duration of unresponsiveness to toe pinch (unconsciousness) was next introduced as an extra marker of injury severity. The current study is an effort to assess the utilization of acute injury-induced biological responses (duration of toe pinch unresponsiveness, percent body weight change, quantification of brain edema, and apnea duration) to predict cognitive performance at a subacute time point following developmental brain injury. Cognitive performance, when measured at a subacute phase, after developmental FPI was negatively correlated with the following variables, duration of toe pinch unresponsiveness, percent weight change, and quantified level of brain edema. These finding suggest the potential utilization of reliable severity assessment of injury-induced biological responses in determining outcome measures at subacute time points.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2018 ","pages":"381-384"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BHI.2018.8333448","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39266952","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: 1

Activity and Circadian Rhythm of Sepsis Patients in the Intensive Care Unit. 重症监护室脓毒症患者的活动和昼夜节律。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2018-03-01 Epub Date: 2018-04-09 DOI: 10.1109/BHI.2018.8333359

Anis Davoudi, Duane B Corbett, Tezcan Ozrazgat-Baslanti, Azra Bihorac, Scott C Brakenridge, Todd M Manini, Parisa Rashidi

{"title":"Activity and Circadian Rhythm of Sepsis Patients in the Intensive Care Unit.","authors":"Anis Davoudi, Duane B Corbett, Tezcan Ozrazgat-Baslanti, Azra Bihorac, Scott C Brakenridge, Todd M Manini, Parisa Rashidi","doi":"10.1109/BHI.2018.8333359","DOIUrl":"https://doi.org/10.1109/BHI.2018.8333359","url":null,"abstract":"Early mobilization of critically ill patients in the Intensive Care Unit (ICU) can prevent adverse outcomes such as delirium and post-discharge physical impairment. To date, no studies have characterized activity of sepsis patients in the ICU using granular actigraphy data. This study characterizes the activity of sepsis patients in the ICU to aid in future mobility interventions. We have compared the actigraphy features of 24 patients in four groups: Chronic Critical Illness (CCI) sepsis patients in the ICU, Rapid Recovery (RR) sepsis patients in the ICU, non-sepsis ICU patients (control-ICU), and healthy subjects. We used a total of 15 statistical and circadian rhythm features extracted from the patients' actigraphy data collected over a five-day period. Our results show that the four groups are significantly different in terms of activity features. In addition, we observed that the CCI and control-ICU patients show less regularity in their circadian rhythm compared to the RR patients. These results show the potential of using actigraphy data for guiding mobilization practices, classifying sepsis recovery subtype, as well as for tracking patients' recovery.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2018 ","pages":"17-20"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BHI.2018.8333359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36662501","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: 7

On quantification of geometry and topology of protein pockets and channels for assessing mutation effects. 量化蛋白质口袋和通道的几何和拓扑结构以评估突变效应。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2018-03-01 Epub Date: 2018-04-09 DOI: 10.1109/BHI.2018.8333419

Wei Tian, Jie Liang

引用次数: 0

A General Method for Predicting Amino Acid Residues Experiencing Hydrogen Exchange. 预测发生氢交换的氨基酸残基的一般方法。

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2018-03-01 Epub Date: 2018-04-09 DOI: 10.1109/BHI.2018.8333438

Boshen Wang, Alan Perez-Rathke, Renhao Li, Jie Liang

{"title":"A General Method for Predicting Amino Acid Residues Experiencing Hydrogen Exchange.","authors":"Boshen Wang, Alan Perez-Rathke, Renhao Li, Jie Liang","doi":"10.1109/BHI.2018.8333438","DOIUrl":"10.1109/BHI.2018.8333438","url":null,"abstract":"Information on protein hydrogen exchange can help delineate key regions involved in protein-protein interactions and provides important insight towards determining functional roles of genetic variants and their possible mechanisms in disease processes. Previous studies have shown that the degree of hydrogen exchange is affected by hydrogen bond formations, solvent accessibility, proximity to other residues, and experimental conditions. However, a general predictive method for identifying residues capable of hydrogen exchange transferable to a broad set of proteins is lacking. We have developed a machine learning method based on random forest that can predict whether a residue experiences hydrogen exchange. Using data from the Start2Fold database, which contains information on 13,306 residues (3,790 of which experience hydrogen exchange and 9,516 which do not exchange), our method achieves good performance. Specifically, we achieve an overall out-of-bag (OOB) error, an unbiased estimate of the test set error, of 20.3 percent. Using a randomly selected test data set consisting of 500 residues experiencing hydrogen exchange and 500 which do not, our method achieves an accuracy of 0.79, a recall of 0.74, a precision of 0.82, and an F1 score of 0.78.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2018 ","pages":"341-344"},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5957487/pdf/nihms950122.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36115480","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

Prediction of Heart Transplant Rejection Using Histopathological Whole-Slide Imaging. 利用组织病理学全切片成像预测心脏移植排斥反应

... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics Pub Date : 2018-03-01 Epub Date: 2018-04-09 DOI: 10.1109/bhi.2018.8333416

Adrienne E Dooley, Li Tong, Shriprasad R Deshpande, May D Wang

{"title":"Prediction of Heart Transplant Rejection Using Histopathological Whole-Slide Imaging.","authors":"Adrienne E Dooley, Li Tong, Shriprasad R Deshpande, May D Wang","doi":"10.1109/bhi.2018.8333416","DOIUrl":"10.1109/bhi.2018.8333416","url":null,"abstract":"Endomyocardial biopsies are the current gold standard for monitoring heart transplant patients for signs of cardiac allograft rejection. Manually analyzing the acquired tissue samples can be costly, time-consuming, and subjective. Computer-aided diagnosis, using digitized whole-slide images, has been used to classify the presence and grading of diseases such as brain tumors and breast cancer, and we expect it can be used for prediction of cardiac allograft rejection. In this paper, we first create a pipeline to normalize and extract pixel-level and object-level features from histopathological whole-slide images of endomyocardial biopsies. Then, we develop a two-stage classification algorithm, where we first cluster individual tiles and then use the frequency of tiles in each cluster for classification of each whole-slide image. Our results show that the addition of an unsupervised clustering step leads to higher classification accuracy, as well as the importance of object-level features based on the pathophysiology of rejection. Future expansion of this study includes the development of a multiclass classification pipeline for subtypes and grades of cardiac allograft rejection.","PeriodicalId":72024,"journal":{"name":"... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics","volume":"2018 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302110/pdf/nihms-1595601.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38060188","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

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