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

英文中文

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

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.

结肠镜检查是一种用于检测结直肠癌的筛查和诊断程序，具有监测和提高腺瘤检出率的特定质量指标。这些质量指标存储在不同的文档中，如结肠镜检查、病理和放射学报告。缺乏整合的标准化文献阻碍了结直肠癌的研究。使用自然语言处理(NLP)和机器学习(ML)技术提取临床概念是人工数据抽象的替代方案。上下文词嵌入模型，如BERT(来自变形金刚的双向编码器表示)和FLAIR，提高了NLP任务的性能。结合多个临床训练的嵌入可以改善单词表示并提高临床NLP系统的性能。本研究的目的是使用连接的临床嵌入从合并结肠镜检查文件中提取全面的临床概念。我们为三种报告类型构建了高质量的注释语料库。BERT和FLAIR嵌入在未标记的结肠镜相关文件上进行训练。我们建立了一个混合人工神经网络(h-ANN)来连接和微调BERT和FLAIR嵌入。为了从三种报告类型中提取感兴趣的概念，从h-ANN中初始化3个模型，并使用带注释的语料库进行微调。模型结肠镜、病理和放射学报告的f1得分分别为91.76%、92.25%和88.55%。

{"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

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.

临床命名实体识别(NER)是许多下游自然语言处理(NLP)应用的重要组成部分，如信息提取和去识别。最近，利用词嵌入的深度学习(DL)方法在临床NLP任务中很受欢迎。然而，在评估和组合来自不同领域的词嵌入方面的工作很少。本研究的目标是通过开发一个结合不同嵌入的深度学习模型，并研究来自普通和临床领域的标准嵌入和上下文嵌入的组合，来提高临床出院摘要中的NER的性能。我们开发了:1)一个人工注释的高质量内部语料库和2)一个具有输入嵌入层的NER模型，该模型结合了不同的嵌入:标准词嵌入，基于上下文的词嵌入，使用卷积神经网络(CNN)的字符级词嵌入，以及一个外部知识来源以及单词特征作为单热向量。然后嵌入双向长短期记忆(Bi-LSTM)层和条件随机场(CRF)层。所提出的模型在两个公开可用的数据集上达到或克服了最先进的性能，在内部语料库上的F1得分为94.31%。在加入混合域临床预训练的上下文嵌入后，内部语料库的F1得分进一步提高到95.36%。本研究展示了一种结合不同嵌入的有效方法，可以提高识别性能，帮助临床记录的下游去识别。

{"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

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.

结肠镜检查在结直肠癌(CC)筛查中起着至关重要的作用。不幸的是，与此过程相关的数据分别存储在不同的文档中，结肠镜检查、病理和放射学报告。缺乏综合的标准化文件妨碍了质量指标和临床及转化研究的准确报告。自然语言处理(NLP)已被用作人工数据抽象的替代方法。基于机器学习(ML)的NLP解决方案的性能在很大程度上依赖于标注语料库的准确性。由于数据隐私法以及所需的成本和工作量，大容量带注释的语料库的可用性受到限制。此外，手工标注过程容易出错，这使得缺乏高质量的标注语料库成为部署机器学习解决方案的最大瓶颈。本研究的目的是确定对结肠镜检查质量至关重要的临床实体，并根据标准化的注释指南，使用特定领域的分类法构建高质量的注释语料库。带注释的语料库可用于训练ML模型，用于各种下游任务。

{"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

Psychiatric and medical disorders, social and family environment, and legal distress are important determinants of distress that impact the effectiveness of the treatment in opioid treatment program (OTP). This information is not routinely captured in electronic health record, but may be found in clinical notes. This study aims to explore the feasibility and effectiveness of natural language processing (NLP) strategy for identifying legal, social, mental and medical determinates of distress along with emotional pain rooted in family environment from clinical narratives of patients with opioid addiction, and then using this information to find its impact on OTP outcomes. Analysis in this study showed that mental and legal distress significantly impact the result of the treatment in OTP.

精神和医学疾病、社会和家庭环境以及法律困扰是影响阿片类药物治疗计划(OTP)治疗有效性的困扰的重要决定因素。这些信息通常不会在电子健康记录中记录，但可以在临床记录中找到。本研究旨在探讨自然语言处理(NLP)策略从阿片类药物成瘾患者的临床叙述中识别法律、社会、精神和医学决定因素以及根植于家庭环境的情绪痛苦的可行性和有效性，然后利用这些信息发现其对OTP结果的影响。本研究的分析表明，精神和法律上的痛苦显著影响OTP治疗的结果。

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

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.

嗜酸性粒细胞食管炎（EoE）是一种炎症性食管疾病，发病率越来越高。诊断的黄金标准是由临床病理学家对患者的活检组织样本进行人工检查，以确定在单个高倍视野（400 倍放大率）内是否存在 15 个或更多的嗜酸性粒细胞。嗜酸性粒细胞增多症的诊断是一个繁琐的过程，给评估疾病的严重程度和进展增加了难度。我们提出了一种利用深度图像分割量化嗜酸性粒细胞的自动化方法。应用 U-Net 模型和后处理系统可生成基于嗜酸性粒细胞的统计数据，从而诊断咽喉炎并描述疾病的严重程度和进展情况。这些统计数据是在最初诊断咽喉炎时从活检中获取的，然后与患者元数据（临床和治疗表型）进行比较。这样做的目的是找到联系，以便在新患者初次确诊疾病时为其治疗计划提供潜在指导。该研究还进一步应用了深度图像分类模型，以发现除嗜酸性粒细胞以外可用于诊断咽喉炎的其他特征。这是第一项将深度学习计算机视觉方法用于咽喉炎诊断的研究，也是第一项为跟踪疾病严重程度和进展提供自动化流程的研究。

{"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

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.

人们生活、学习、工作和娱乐场所的条件可以解释健康方面的差异和不平等。事实上，个人的健康部分与获得保健的机会和质量有关，主要与他的行为、社会经济条件和其他与社区有关的因素有关，这些因素往往是保健组织难以解决的。为了满足对当地社会服务组织信息的需求和提供资源转介的能力，已经提出了一些平台，提供电子社会资源目录，并促进转介到社会服务机构。然而，这些平台在对医疗保健组织的依赖性、应用程序可移植性、服务可用性以及用户参与的交互(如跟踪、监视和通知)方面存在局限性。此外，现有的社会资源转诊平台存在服务碎片化和需要帮助的个人与服务提供者之间脱节的问题。在本文中，我们介绍了智能社区健康(SCH)，这是一个新颖的独立平台，优先将有需要的人与当地社区资源联系起来。SCH是一个提供全方位服务的端到端社区服务提供商推荐平台，旨在帮助解决我们社区内紧迫的社会、环境和健康需求。该平台由个人寻找服务的移动应用程序和管理社区服务提供者和卫生保健组织的web应用程序仪表板组成。

{"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

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

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

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀