Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)最新文献

Biomedical Engineering Systems and Technologies: 15th International Joint Conference, BIOSTEC 2022, Virtual Event, February 9–11, 2022, Revised Selected Papers 生物医学工程系统与技术:第15届国际联合会议，BIOSTEC 2022，虚拟事件，2022年2月9-11日，修订论文选集

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-38854-5

引用次数: 1

The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings h-ANN模型:使用组合上下文嵌入的综合结肠镜概念编译

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2022-02-01 DOI: 10.5220/0010903300003123

Shorabuddin Syed, Adam Angel, H. Syeda, Carole Jennings, Joseph VanScoy, Mahanazuddin Syed, M. Greer, S. Bhattacharyya, M. Zozus, B. Tharian, F. Prior

{"title":"The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings","authors":"Shorabuddin Syed, Adam Angel, H. Syeda, Carole Jennings, Joseph VanScoy, Mahanazuddin Syed, M. Greer, S. Bhattacharyya, M. Zozus, B. Tharian, F. Prior","doi":"10.5220/0010903300003123","DOIUrl":"https://doi.org/10.5220/0010903300003123","url":null,"abstract":"Colonoscopy is a screening and diagnostic procedure for detection of colorectal carcinomas with specific quality metrics that monitor and improve adenoma detection rates. These quality metrics are stored in disparate documents i.e., colonoscopy, pathology, and radiology reports. The lack of integrated standardized documentation is impeding colorectal cancer research. Clinical concept extraction using Natural Language Processing (NLP) and Machine Learning (ML) techniques is an alternative to manual data abstraction. Contextual word embedding models such as BERT (Bidirectional Encoder Representations from Transformers) and FLAIR have enhanced performance of NLP tasks. Combining multiple clinically-trained embeddings can improve word representations and boost the performance of the clinical NLP systems. The objective of this study is to extract comprehensive clinical concepts from the consolidated colonoscopy documents using concatenated clinical embeddings. We built high-quality annotated corpora for three report types. BERT and FLAIR embeddings were trained on unlabeled colonoscopy related documents. We built a hybrid Artificial Neural Network (h-ANN) to concatenate and fine-tune BERT and FLAIR embeddings. To extract concepts of interest from three report types, 3 models were initialized from the h-ANN and fine-tuned using the annotated corpora. The models achieved best F1-scores of 91.76%, 92.25%, and 88.55% for colonoscopy, pathology, and radiology reports respectively.","PeriodicalId":72386,"journal":{"name":"Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)","volume":"35 1","pages":"189-200"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78367503","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}

引用次数: 2

DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes DeIDNER模型:用于临床记录去识别的神经网络命名实体识别模型

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2022-02-01 DOI: 10.5220/0010884500003123

Mahanazuddin Syed, K. Sexton, M. Greer, Shorabuddin Syed, Joseph VanScoy, Farhan Kawsar, Erica Olson, Karan B. Patel, Jake Erwin, S. Bhattacharyya, M. Zozus, F. Prior

{"title":"DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes","authors":"Mahanazuddin Syed, K. Sexton, M. Greer, Shorabuddin Syed, Joseph VanScoy, Farhan Kawsar, Erica Olson, Karan B. Patel, Jake Erwin, S. Bhattacharyya, M. Zozus, F. Prior","doi":"10.5220/0010884500003123","DOIUrl":"https://doi.org/10.5220/0010884500003123","url":null,"abstract":"Clinical named entity recognition (NER) is an essential building block for many downstream natural language processing (NLP) applications such as information extraction and de-identification. Recently, deep learning (DL) methods that utilize word embeddings have become popular in clinical NLP tasks. However, there has been little work on evaluating and combining the word embeddings trained from different domains. The goal of this study is to improve the performance of NER in clinical discharge summaries by developing a DL model that combines different embeddings and investigate the combination of standard and contextual embeddings from the general and clinical domains. We developed: 1) A human-annotated high-quality internal corpus with discharge summaries and 2) A NER model with an input embedding layer that combines different embeddings: standard word embeddings, context-based word embeddings, a character-level word embedding using a convolutional neural network (CNN), and an external knowledge sources along with word features as one-hot vectors. Embedding was followed by bidirectional long short-term memory (Bi-LSTM) and conditional random field (CRF) layers. The proposed model reaches or overcomes state-of-the-art performance on two publicly available data sets and an F1 score of 94.31% on an internal corpus. After incorporating mixed-domain clinically pre-trained contextual embeddings, the F1 score further improved to 95.36% on the internal corpus. This study demonstrated an efficient way of combining different embeddings that will improve the recognition performance aiding the downstream de-identification of clinical notes.","PeriodicalId":72386,"journal":{"name":"Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)","volume":"1 1","pages":"640-647"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91121539","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}

引用次数: 1

TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation TAX-Corpus:基于分类的结肠镜评估注释

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2022-02-01 DOI: 10.5220/0010876100003123

Shorabuddin Syed, Adam Angel, H. Syeda, Carole Jennings, Joseph VanScoy, Mahanazuddin Syed, M. Greer, S. Bhattacharyya, S. Al-Shukri, M. Zozus, F. Prior, B. Tharian

{"title":"TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation","authors":"Shorabuddin Syed, Adam Angel, H. Syeda, Carole Jennings, Joseph VanScoy, Mahanazuddin Syed, M. Greer, S. Bhattacharyya, S. Al-Shukri, M. Zozus, F. Prior, B. Tharian","doi":"10.5220/0010876100003123","DOIUrl":"https://doi.org/10.5220/0010876100003123","url":null,"abstract":"Colonoscopy plays a critical role in screening of colorectal carcinomas (CC). Unfortunately, the data related to this procedure are stored in disparate documents, colonoscopy, pathology, and radiology reports respectively. The lack of integrated standardized documentation is impeding accurate reporting of quality metrics and clinical and translational research. Natural language processing (NLP) has been used as an alternative to manual data abstraction. Performance of Machine Learning (ML) based NLP solutions is heavily dependent on the accuracy of annotated corpora. Availability of large volume annotated corpora is limited due to data privacy laws and the cost and effort required. In addition, the manual annotation process is error-prone, making the lack of quality annotated corpora the largest bottleneck in deploying ML solutions. The objective of this study is to identify clinical entities critical to colonoscopy quality, and build a high-quality annotated corpus using domain specific taxonomies following standardized annotation guidelines. The annotated corpus can be used to train ML models for a variety of downstream tasks.","PeriodicalId":72386,"journal":{"name":"Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)","volume":"43 1","pages":"162-169"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80760631","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}

引用次数: 0

Comparative Analysis of Patient Distress in Opioid Treatment Programs using Natural Language Processing 使用自然语言处理的阿片类药物治疗方案中患者痛苦的比较分析

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2022-02-01 DOI: 10.5220/0010976700003123

Fatemeh Shah-Mohammadi, Wanting Cui, K. Bachi, Yasmin L. Hurd, J. Finkelstein

引用次数: 1

Biomedical Engineering Systems and Technologies: 14th International Joint Conference, BIOSTEC 2021, Virtual Event, February 11–13, 2021, Revised Selected Papers 生物医学工程系统与技术:第14届国际联合会议，BIOSTEC 2021，虚拟事件，2021年2月11-13日，修订论文选集

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-20664-1

引用次数: 0

Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision. 利用深度学习计算机视觉推进嗜酸性粒细胞食管炎诊断和表型评估。

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2021-02-01 DOI: 10.5220/0010241900002865

William Adorno, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, Donald E Brown

{"title":"Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision.","authors":"William Adorno, Alexis Catalano, Lubaina Ehsan, Hans Vitzhum von Eckstaedt, Barrett Barnes, Emily McGowan, Sana Syed, Donald E Brown","doi":"10.5220/0010241900002865","DOIUrl":"10.5220/0010241900002865","url":null,"abstract":"Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient's biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400× magnification). Diagnosing EoE can be a cumbersome process with added difficulty for assessing the severity and progression of disease. We propose an automated approach for quantifying eosinophils using deep image segmentation. A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression. These statistics are captured in biopsies at the initial EoE diagnosis and are then compared with patient metadata: clinical and treatment phenotypes. The goal is to find linkages that could potentially guide treatment plans for new patients at their initial disease diagnosis. A deep image classification model is further applied to discover features other than eosinophils that can be used to diagnose EoE. This is the first study to utilize a deep learning computer vision approach for EoE diagnosis and to provide an automated process for tracking disease severity and progression.","PeriodicalId":72386,"journal":{"name":"Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)","volume":"2021 ","pages":"44-55"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144887/pdf/nihms-1696150.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39027807","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

Biomedical Engineering Systems and Technologies: 13th International Joint Conference, BIOSTEC 2020, Valletta, Malta, February 24–26, 2020, Revised Selected Papers 生物医学工程系统与技术:第13届国际联合会议，BIOSTEC 2020，瓦莱塔，马耳他，2020年2月24-26日，修订论文选集

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2021-01-01 DOI: 10.1007/978-3-030-72379-8

引用次数: 2

Biomedical Engineering Systems and Technologies: 12th International Joint Conference, BIOSTEC 2019, Prague, Czech Republic, February 22–24, 2019, Revised Selected Papers 生物医学工程系统与技术:第12届国际联合会议，BIOSTEC 2019，布拉格，捷克共和国，2019年2月22-24日，修订论文选集

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2020-01-01 DOI: 10.1007/978-3-030-46970-2

Simone Diniz Junqueira Barbosa, Phoebe Chen, A. Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu, K. Sivalingam, D. Ślęzak, T. Washio, Xiaokang Yang, Junsong Yuan, R. Prates, Ana Roque, Arkadiusz Tomczyk, Elisabetta De Maria, F. Putze, R. Mouček, A. Fred, H. Gamboa

引用次数: 1

Smart Community Health: A Comprehensive Community Resource Recommendation Platform. 智慧社区健康:综合社区资源推荐平台。

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) Pub Date : 2020-01-01 DOI: 10.5220/0009118306140624

Mehdi Mekni, David Haynes

{"title":"Smart Community Health: A Comprehensive Community Resource Recommendation Platform.","authors":"Mehdi Mekni, David Haynes","doi":"10.5220/0009118306140624","DOIUrl":"https://doi.org/10.5220/0009118306140624","url":null,"abstract":"Health disparities and inequities are explained by the conditions of places where people live, learn, work and play. In fact, the health of an individual is partially related to access and quality of health care and mainly associated to his behaviours, socioeconomic conditions and other community related factors that are often challenging to address by health care organizations. To meet the need for information about local social services organizations and the ability to offer resource referrals, a number of platforms have been proposed that provide electronic social resource directories and facilitate referrals to social service agencies. However, these platforms show limitations with regards to their dependancy to health care organizations, application portability, service availability, and user engaging interactions such as tracking, monitoring and notification. Moreover, existing social resource referral platforms suffer from a fragmentation of services and a disconnection between individuals in need and service providers. In this paper, we introduce Smart Community Health (SCH), a novel independent platform that prioritizes connecting people in need with local community resources. SCH is a full-service, end-to-end community service provider recommendation platform designed to help address pressing social, environmental, and health needs within our communities. The platform is composed of a mobile application for individuals looking for services and a web application dashboard for the management of community service providers and health care organizations.","PeriodicalId":72386,"journal":{"name":"Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)","volume":"5 ","pages":"614-624"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358896/pdf/nihms-1726404.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39314840","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: 2