Biomedical informatics insights最新文献_第4页

Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System. 直接和电子健康记录访问明尼苏达州免疫信息系统中免疫临床决策支持。

Biomedical informatics insights Pub Date : 2016-12-22 eCollection Date: 2016-01-01 DOI: 10.4137/BII.S40208

Sripriya Rajamani, Aaron Bieringer, Stephanie Wallerius, Daniel Jensen, Tamara Winden, Miriam Halstead Muscoplat

{"title":"Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System.","authors":"Sripriya Rajamani, Aaron Bieringer, Stephanie Wallerius, Daniel Jensen, Tamara Winden, Miriam Halstead Muscoplat","doi":"10.4137/BII.S40208","DOIUrl":"10.4137/BII.S40208","url":null,"abstract":"Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"8 1","pages":"23-29"},"PeriodicalIF":0.0,"publicationDate":"2016-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5181832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70687441","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

Clinical Decision Support for Immunizations (CDSi): A Comprehensive, Collaborative Strategy 临床决策支持免疫接种(CDSi):一个全面的协作战略

Biomedical informatics insights Pub Date : 2016-10-19 DOI: 10.4137/BII.S40204

Noam H. Arzt

引用次数: 5

Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records. 面向电子病历自然语言处理的对峙注释的高效查询。

Biomedical informatics insights Pub Date : 2016-07-19 eCollection Date: 2016-01-01 DOI: 10.4137/BII.S38916

Yuan Luo, Peter Szolovits

{"title":"Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records.","authors":"Yuan Luo, Peter Szolovits","doi":"10.4137/BII.S38916","DOIUrl":"https://doi.org/10.4137/BII.S38916","url":null,"abstract":"In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. We first formulate this problem into the interval query problem, for which optimal query/update time is in general logarithm. We next perform a tight time complexity analysis on the basic interval tree query algorithm and show its nonoptimality when being applied to a collection of 13 query types from Allen's interval algebra. We then study two closely related state-of-the-art interval query algorithms, proposed query reformulations, and augmentations to the second algorithm. Our proposed algorithm achieves logarithmic time stabbing-max query time complexity and solves the stabbing-interval query tasks on all of Allen's relations in logarithmic time, attaining the theoretic lower bound. Updating time is kept logarithmic and the space requirement is kept linear at the same time. We also discuss interval management in external memory models and higher dimensions. ","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"8 ","pages":"29-38"},"PeriodicalIF":0.0,"publicationDate":"2016-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4137/BII.S38916","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34331343","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

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

Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning. 通过自然语言处理和机器学习预测小儿癫痫手术候选者的方法问题。

Biomedical informatics insights Pub Date : 2016-05-22 eCollection Date: 2016-01-01 DOI: 10.4137/BII.S38308

Kevin Bretonnel Cohen, Benjamin Glass, Hansel M Greiner, Katherine Holland-Bouley, Shannon Standridge, Ravindra Arya, Robert Faist, Diego Morita, Francesco Mangano, Brian Connolly, Tracy Glauser, John Pestian

{"title":"Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.","authors":"Kevin Bretonnel Cohen, Benjamin Glass, Hansel M Greiner, Katherine Holland-Bouley, Shannon Standridge, Ravindra Arya, Robert Faist, Diego Morita, Francesco Mangano, Brian Connolly, Tracy Glauser, John Pestian","doi":"10.4137/BII.S38308","DOIUrl":"10.4137/BII.S38308","url":null,"abstract":"Objective: We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient's status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do. These hypotheses are tested by systematically evaluating the effects of the data source, amount of training data, class balance, classification algorithm, and feature set on classifier performance. The results support both hypotheses, with F-measures ranging from 0.71 to 0.82. The feature set, classification algorithm, amount of training data, class balance, and gold standard all significantly affected classification performance. It was further observed that classification performance was better than the highest agreement between two annotators, even at one year before documented surgery referral. The results demonstrate that such machine learning methods can contribute to predicting pediatric epilepsy surgery candidates and reducing lag time to surgery referral.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"8 ","pages":"11-8"},"PeriodicalIF":0.0,"publicationDate":"2016-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4876984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34446487","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

Big Data Application in Biomedical Research and Health Care: A Literature Review. 大数据在生物医学研究和医疗保健中的应用：文献综述。

Biomedical informatics insights Pub Date : 2016-01-19 eCollection Date: 2016-01-01 DOI: 10.4137/BII.S31559

Jake Luo, Min Wu, Deepika Gopukumar, Yiqing Zhao

{"title":"Big Data Application in Biomedical Research and Health Care: A Literature Review.","authors":"Jake Luo, Min Wu, Deepika Gopukumar, Yiqing Zhao","doi":"10.4137/BII.S31559","DOIUrl":"10.4137/BII.S31559","url":null,"abstract":"Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. The cost of acquiring and analyzing biomedical data is expected to decrease dramatically with the help of technology upgrades, such as the emergence of new sequencing machines, the development of novel hardware and software for parallel computing, and the extensive expansion of EHRs. Big data applications present new opportunities to discover new knowledge and create novel methods to improve the quality of health care. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies published in the last five years. In this paper, we review and discuss big data application in four major biomedical subdisciplines: (1) bioinformatics, (2) clinical informatics, (3) imaging informatics, and (4) public health informatics. Specifically, in bioinformatics, high-throughput experiments facilitate the research of new genome-wide association studies of diseases, and with clinical informatics, the clinical field benefits from the vast amount of collected patient data for making intelligent decisions. Imaging informatics is now more rapidly integrated with cloud platforms to share medical image data and workflows, and public health informatics leverages big data techniques for predicting and monitoring infectious disease outbreaks, such as Ebola. In this paper, we review the recent progress and breakthroughs of big data applications in these health-care domains and summarize the challenges, gaps, and opportunities to improve and advance big data applications in health care. ","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"8 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2016-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70687337","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

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