Biomedical informatics insights最新文献_第4页

Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP. 迈向学习型医疗保健系统——由大数据和NLP授权的护理点知识交付。

Biomedical informatics insights Pub Date : 2016-06-23 eCollection Date: 2016-01-01 DOI: 10.4137/BII.S37977

Vinod C Kaggal, Ravikumar Komandur Elayavilli, Saeed Mehrabi, Joshua J Pankratz, Sunghwan Sohn, Yanshan Wang, Dingcheng Li, Majid Mojarad Rastegar, Sean P Murphy, Jason L Ross, Rajeev Chaudhry, James D Buntrock, Hongfang Liu

{"title":"Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.","authors":"Vinod C Kaggal, Ravikumar Komandur Elayavilli, Saeed Mehrabi, Joshua J Pankratz, Sunghwan Sohn, Yanshan Wang, Dingcheng Li, Majid Mojarad Rastegar, Sean P Murphy, Jason L Ross, Rajeev Chaudhry, James D Buntrock, Hongfang Liu","doi":"10.4137/BII.S37977","DOIUrl":"https://doi.org/10.4137/BII.S37977","url":null,"abstract":"The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future. ","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"8 Suppl 1","pages":"13-22"},"PeriodicalIF":0.0,"publicationDate":"2016-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S37977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34644173","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: 61

Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics. 使用可扩展文本分析挖掘消费者产品评论中与健康相关的问题。

Biomedical informatics insights Pub Date : 2016-06-20 eCollection Date: 2016-01-01 DOI: 10.4137/BII.S37791

Manabu Torii, Sameer S Tilak, Son Doan, Daniel S Zisook, Jung-Wei Fan

引用次数: 18

Mobile Health (mHealth) Services and Online Health Educators. 移动健康(mHealth)服务和在线健康教育者。

Biomedical informatics insights Pub Date : 2016-05-25 eCollection Date: 2016-01-01 DOI: 10.4137/BII.S35388

Muhammad Anshari, Mohammad Nabil Almunawar

引用次数: 20

Tennessee’s 3-Star Report: Using Available Data Systems to Reduce Missed Opportunities to Vaccinate Preteens 田纳西州的三星报告:利用现有的数据系统减少错过的机会，为青少年前接种疫苗

Biomedical informatics insights Pub Date : 2016-01-01 DOI: 10.4137/BII.S40207

Kelly L. Moore, Melissa K. Fankhauser, P. Hull

{"title":"Tennessee’s 3-Star Report: Using Available Data Systems to Reduce Missed Opportunities to Vaccinate Preteens","authors":"Kelly L. Moore, Melissa K. Fankhauser, P. Hull","doi":"10.4137/BII.S40207","DOIUrl":"https://doi.org/10.4137/BII.S40207","url":null,"abstract":"All preteens should receive tetanus–diphtheria–pertussis vaccine (Tdap), quadrivalent meningococcal vaccine (Men-ACWY), and the human papillomavirus (HPV) cancer vaccine series. In Tennessee, HPV vaccination rates have stagnated at low levels for a decade. Three fundamental strategies to reduce missed opportunities for immunization include administering all recommended vaccines at the same visit, making strong recommendations for vaccines, and auditing and feedback. In Tennessee, during each summer, a surge of preteens visit local health departments (LHDs) to receive a required Tdap vaccine before entering seventh grade, presenting an opportunity to administer Men-ACWY and HPV. The Tennessee Immunization Program (TIP) coined the term “3-Star visit” for such encounters and developed a monthly report to track them using data from the Patient Tracking Billing Management Information System (PTBMIS) used by LHDs across Tennessee. Implementation of this quality improvement report has correlated with a substantial increase in 3-Star visits from 2013 to 2016, particularly during the summer months.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"8 1","pages":"15 - 21"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S40207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70687420","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: 4

Some correlates of electronic health information management system success in nigerian teaching hospitals. 尼日利亚教学医院电子健康信息管理系统成功的相关因素。

Biomedical informatics insights Pub Date : 2015-04-01 eCollection Date: 2015-01-01 DOI: 10.4137/BII.S20229

Adebowale I Ojo, Sunday O Popoola

引用次数: 24

Combining an expert-based medical entity recognizer to a machine-learning system: methods and a case study. 将基于专家的医疗实体识别器与机器学习系统相结合:方法和案例研究。

Biomedical informatics insights Pub Date : 2013-08-01 eCollection Date: 2013-01-01 DOI: 10.4137/BII.S11770

Pierre Zweigenbaum, Thomas Lavergne, Natalia Grabar, Thierry Hamon, Sophie Rosset, Cyril Grouin

{"title":"Combining an expert-based medical entity recognizer to a machine-learning system: methods and a case study.","authors":"Pierre Zweigenbaum, Thomas Lavergne, Natalia Grabar, Thierry Hamon, Sophie Rosset, Cyril Grouin","doi":"10.4137/BII.S11770","DOIUrl":"https://doi.org/10.4137/BII.S11770","url":null,"abstract":"Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. Our case study specifically highlights the risk of overfitting incurred by an expert-based system. We observe that it prevents the combination of the 2 systems from obtaining improvements in precision, recall, or F-measure, and analyze the underlying mechanisms through a post-hoc feature-level analysis. Wrapping the expert-based system alone as attributes input to a CRF classifier does boost its F-measure from 0.603 to 0.710, bringing it on par with the data-driven system. The generalization of this method remains to be further investigated. ","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"6 Suppl 1","pages":"51-62"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S11770","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31747827","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: 8

Text Categorization of Heart, Lung, and Blood Studies in the Database of Genotypes and Phenotypes (dbGaP) Utilizing n-grams and Metadata Features. 利用n-图和元数据特征在基因型和表型数据库(dbGaP)中对心脏、肺和血液研究的文本分类。

Biomedical informatics insights Pub Date : 2013-07-22 Print Date: 2013-01-01 DOI: 10.4137/BII.S11987

Mindy K Ross, Ko-Wei Lin, Karen Truong, Abhishek Kumar, Mike Conway

{"title":"Text Categorization of Heart, Lung, and Blood Studies in the Database of Genotypes and Phenotypes (dbGaP) Utilizing n-grams and Metadata Features.","authors":"Mindy K Ross, Ko-Wei Lin, Karen Truong, Abhishek Kumar, Mike Conway","doi":"10.4137/BII.S11987","DOIUrl":"https://doi.org/10.4137/BII.S11987","url":null,"abstract":"The database of Genotypes and Phenotypes (dbGaP) allows researchers to understand phenotypic contribution to genetic conditions, generate new hypotheses, confirm previous study results, and identify control populations. However, effective use of the database is hindered by suboptimal study retrieval. Our objective is to evaluate text classification techniques to improve study retrieval in the context of the dbGaP database. We utilized standard machine learning algorithms (naive Bayes, support vector machines, and the C4.5 decision tree) trained on dbGaP study text and incorporated n-gram features and study metadata to identify heart, lung, and blood studies. We used the χ(2) feature selection algorithm to identify features that contributed most to classification performance and experimented with dbGaP associated PubMed papers as a proxy for topicality. Classifier performance was favorable in comparison to keyword-based search results. It was determined that text categorization is a useful complement to document retrieval techniques in the dbGaP. ","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"6 ","pages":"35-45"},"PeriodicalIF":0.0,"publicationDate":"2013-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S11987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31641327","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: 5

Using conversation topics for predicting therapy outcomes in schizophrenia. 利用谈话话题预测精神分裂症的治疗结果。

Biomedical informatics insights Pub Date : 2013-07-15 eCollection Date: 2013-01-01 DOI: 10.4137/BII.S11661

Christine Howes, Matthew Purver, Rose McCabe

引用次数: 0

Analysis of cross-institutional medication description patterns in clinical narratives. 临床叙述中跨机构药物描述模式分析。

Biomedical informatics insights Pub Date : 2013-06-24 Print Date: 2013-01-01 DOI: 10.4137/BII.S11634

Sunghwan Sohn, Cheryl Clark, Scott R Halgrim, Sean P Murphy, Siddhartha R Jonnalagadda, Kavishwar B Wagholikar, Stephen T Wu, Christopher G Chute, Hongfang Liu

引用次数: 13

Towards Converting Clinical Phrases into SNOMED CT Expressions. 临床短语转换为SNOMED CT表达的研究。

Biomedical informatics insights Pub Date : 2013-06-24 Print Date: 2013-01-01 DOI: 10.4137/BII.S11645

Rohit J Kate

引用次数: 17