Proceedings. AMIA Symposium最新文献

{"title":"The effect of sample size and disease prevalence on supervised machine learning of narrative data.","authors":"Lawrence K McKnight,&nbsp;Adam Wilcox,&nbsp;George Hripcsak","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This paper examines the independent effects of outcome prevalence and training sample sizes on inductive learning performance. We trained 3 inductive learning algorithms (MC4, IB, and Naïve-Bayes) on 60 simulated datasets of parsed radiology text reports labeled with 6 disease states. Data sets were constructed to define positive outcome states at 4 prevalence rates (1, 5, 10, 25, and 50%) in training set sizes of 200 and 2,000 cases. We found that the effect of outcome prevalence is significant when outcome classes drop below 10% of cases. The effect appeared independent of sample size, induction algorithm used, or class label. Work is needed to identify methods of improving classifier performance when output classes are rare.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244149/pdf/procamiasymp00001-0560.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22138401","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}

{"title":"Robustness of empirical search strategies for clinical content in MEDLINE.","authors":"Nancy L Wilczynski,&nbsp;R Brian Haynes","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>It is important for clinical end users of MEDLINE to be able to retrieve articles that are both scientifically sound and directly relevant to clinical practice. The use of methodologic search filters (such as \"random allocation\" for sound studies of medical interventions) has been advocated to improve the accuracy of searching for such studies. Methodologic search filters have been tested in previous MEDLINE files but indexing continues to evolve and the operating characteristics of these search filters in current MEDLINE files are unknown.</p><p><strong>Objective: </strong>To determine the robustness of empirical search strategies developed in 1991 for detecting clinical content in MEDLINE in the year 2000.</p><p><strong>Design: </strong>A survey based on a hand search of 171 core health care journals using predetermined quality indicators for scientific merit and clinical relevance.</p><p><strong>Methods: </strong>6 trained, experienced research assistants read all issues of 171 journals for the publishing year 2000. Each article was rated using purpose and quality indicators and categorized into clinically relevant original studies, review articles, general papers, or case reports. The original and review articles were then categorized as 'pass' or 'fail' for methodologic rigor in the areas of therapy/quality improvement, diagnosis, prognosis, causation, economics, clinical prediction, and qualitative and review articles. Search strategies developed in 1991 were tested in the 2000 database.</p><p><strong>Main outcome measures: </strong>Sensitivity, specificity, precision, and accuracy of the search strategies.</p><p><strong>Results: </strong>Search strategies developed in 1991 generally performed at least as well in 2000 for both best single terms and combinations of terms for high-sensitivity MEDLINE searches for studies of treatment, prognosis, etiology and diagnosis. For example, the accuracy of \"clinical trial (pt)\" rose from 91.6% to 94.4% (P<0.05) for retrieving high-quality studies of treatments.</p><p><strong>Conclusion: </strong>Most MEDLINE search strategies developed in 1991 are robust when searching in the publishing year 2000.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244303/pdf/procamiasymp00001-0945.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22138412","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}

{"title":"Informatics tools to monitor progress and outcomes of patients with drug resistant tuberculosis in Peru.","authors":"Hamish S F Fraser,&nbsp;Darius Jazayeri,&nbsp;Carole D Mitnick,&nbsp;Joia S Mukherjee,&nbsp;Jaime Bayona","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Multi-drug resistant tuberculosis (MDR-TB) is an important and growing problem in many developing countries. New strategies have been developed to combat the disease but require complex treatment regimens and close monitoring of patients' bacteriology results. We describe a web-based medical record system deployed in Peru to support the management of MDR-TB. Web-based analyses have been developed to track drug sensitivity test results, patterns of sputum smear and culture results and time to conversion from positive to negative cultures. Individual and aggregate drug requirements can also be monitored in real time. Multiple analyses can be linked together and data can be graphed or downloaded to spreadsheets. Over 1200 patients are currently in the system. We argue that such a web-based clinical and epidemiological management system is an important component for successful implementation of complex health interventions in resource poor areas.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244209/pdf/procamiasymp00001-0311.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22138811","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}

{"title":"Filtering for medical news items using a machine learning approach.","authors":"Wanhong Zheng,&nbsp;Evangelos Milios,&nbsp;Carolyn Watters","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We address the problem of filtering medical news articles for targeted audiences. The approach is based on terms and one of the difficulties is extracting a feature set appropriate for the domain. This paper addresses the medical news-filtering problem using a machine learning approach. We describe the application of two supervised machine learning techniques, Decision Trees and Naïve Bayes, to automatically construct classifiers on the basis of a training set, in which news articles have been pre-classified by a medical expert and four other human readers. The goal is to classify the news articles into three groups: non-medical, medical intended for experts, and medical intended for other readers. While the general accuracy of the machine learning approach is around 78%, the accuracy of distinguishing non-medical articles from medical ones is shown to be 92%.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244368/pdf/procamiasymp00001-0990.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22138840","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}

{"title":"Knowledge-based bioterrorism surveillance.","authors":"David L Buckeridge,&nbsp;Justin Graham,&nbsp;Martin J O'Connor,&nbsp;Michael K Choy,&nbsp;Samson W Tu,&nbsp;Mark A Musen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>An epidemic resulting from an act of bioterrorism could be catastrophic. However, if an epidemic can be detected and characterized early on, prompt public health intervention may mitigate its impact. Current surveillance approaches do not perform well in terms of rapid epidemic detection or epidemic monitoring. One reason for this shortcoming is their failure to bring existing knowledge and data to bear on the problem in a coherent manner. Knowledge-based methods can integrate surveillance data and knowledge, and allow for careful evaluation of problem-solving methods. This paper presents an argument for knowledge-based surveillance, describes a prototype of BioSTORM, a system for real-time epidemic surveillance, and shows an initial evaluation of this system applied to a simulated epidemic from a bioterrorism attack.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244298/pdf/procamiasymp00001-0117.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22139712","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}

{"title":"Open source handheld-based EMR for paramedics working in rural areas.","authors":"Vishwanath Anantraman,&nbsp;Tarjei Mikkelsen,&nbsp;Reshma Khilnani,&nbsp;Vikram S Kumar,&nbsp;Alex Pentland,&nbsp;Lucila Ohno-Machado","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We describe a handheld-based electronic medical record (EMR) for use in certain rural settings. The system is based on the Linux operating system and allows access to large mobile databases. The open source system is designed for paramedical health workers serving remote areas in rural India. A PDA loaded with the handheld-based EMR provides workers who have little access to medical doctors with different kinds of decision support and alerts. It addresses two important problems in developing countries: prenatal care and child health. This paper describes the technical challenges and innovation needed in the design, development, adaptation and implementation of the handheld EMR in a real setting in India</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244537/pdf/procamiasymp00001-0053.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22139763","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}

{"title":"A customizable similarity measure between histological cases.","authors":"Marie-Christine Jaulent,&nbsp;Adil Bennani,&nbsp;Christel Le Bozec,&nbsp;Eric Zapletal,&nbsp;Patrice Degoulet","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>IDEM, a computerized environment dedicated to pathologists, includes a Case Based Reasoning (CBR) procedure to retrieve similar histological cases in the database. The relevancy of a retrieved case strongly depends on the similarity measure comparing case descriptions. The present work deals with the definition of a similarity measure in the context of IDEM. In a first step, a theoretical measure (relational, numerical and informed), based on the domain constraints, was selected. In a second step, the theoretical measure is optimized according to the current case base. Results are presented for a database of 53 cases of breast tumors. The contribution of this work is to give to pathologists an interactive environment that optimizes the similarity measure between histological cases. This work is also a contribution to the CBR cycle life since the similarity measure can be adapted while new cases are added to the base.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244180/pdf/procamiasymp00001-0391.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22139775","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}

{"title":"Discovering protein similarity using natural language processing.","authors":"Indra N Sarkar,&nbsp;Thomas C Rindflesch","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Extracting protein interaction relationships from textual repositories, such as MEDLINE, may prove useful in generating novel biological hypotheses. Using abstracts relevant to two known functionally related proteins, we modified an existing natural language processing tool to extract protein interaction terms. We were able to obtain functional information about two proteins, Amyloid Precursor Protein and Prion Protein, that have been implicated in the etiology of Alzheimer's Disease and Creutzfeldt-Jakob Disease, respectively.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244512/pdf/procamiasymp00001-0718.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22139782","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}

{"title":"Asking questions: information needs in a surgical intensive care unit.","authors":"Madhu C Reddy,&nbsp;Wanda Pratt,&nbsp;Paul Dourish,&nbsp;Michael Shabot","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Even in the information-rich environment of hospitals, health-care providers face challenges in addressing their various information needs. Through a study of a patient-care team in a tertiary care Surgical Intensive Care Unit (SICU), we expanded our understanding of health-care providers' information needs in two important ways. First, the study focused on a patient-care team instead of individual health-care providers. Second, information needs were examined in a particular organizational setting, the SICU, which had not been previously studied. We found that organizational information was extremely important to SICU team members. Furthermore, the first resource that team members utilized was not electronic or paper but rather human: another team member.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244355/pdf/procamiasymp00001-0688.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22139856","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}

{"title":"Design and implementation of a real-time clinical alerting system for intensive care unit.","authors":"Hsiao-Ting Chen,&nbsp;Wun-Chun Ma,&nbsp;Der Ming Liou","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Nowadays many hospitals use clinical information systems (CIS) to improve patient health care and information management. CIS retrieves and manages patient information such as patient administration, laboratory data and medications. Specialized CIS, especially Intensive Care Unit (ICU) CIS, needs to handle more additional information. How to process and make use of this enormous amount of physiologic data generated by ICU is a significant topic in CIS design. We have designed and implemented a real-time clinical alerting system for ICU, which executes alert algorithms on the database records of 63 ICU physiologic parameters. The user can create various alert rules with our rule editor to supervise the values or trends of these parameters. When an alert condition on a specific patient is detected, the system notices the clinicians in charge with mobile phones or pagers. With this functionality, up-to-date patient information is provided for clinicians inverted exclamation mark | use in judging the patient inverted exclamation mark |s condition and making treatment decisions.</p>","PeriodicalId":79712,"journal":{"name":"Proceedings. AMIA Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2244389/pdf/procamiasymp00001-0172.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"22140174","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}