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González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Campbell et al.", "parsedDate": "2012-10", "numChildren": 1 }, "data": { "key": "KWUPGP7X", "version": 9371, "itemType": "journalArticle", "title": "Automated before-procedure electronic health record screening to assess appropriateness for GI endoscopy and sedation", "creators": [ { "creatorType": "author", "firstName": "Emily J.", "lastName": "Campbell" }, { "creatorType": "author", "firstName": "Arun", "lastName": "Krishnaraj" }, { "creatorType": "author", "firstName": "Mitchell", "lastName": "Harris" }, { "creatorType": "author", "firstName": "Sanjay", "lastName": "Saini" }, { "creatorType": "author", "firstName": "James M.", "lastName": "Richter" } ], "abstractNote": "BACKGROUND: Endoscopists are performing greater numbers of procedures, often on patients with complex conditions, in ambulatory settings because of changing patient demographics and referral patterns. To assist with the pre-procedure assessment of such patients, we deployed an advanced electronic health record tool, the Queriable Patient Inference Dossier (QPID), to review clinical histories and generate e-mail alerts to providers, based on clinical guidelines.\nOBJECTIVE: Study the feasibility of an automated pre-procedure alert system for outpatient endoscopy.\nDESIGN: We retrospectively reviewed 5 physicians' use of the application and their responses to the alerts.\nSETTING: A hospital-based endoscopy unit and its two satellite outpatient clinics, Boston area, Massachusetts.\nPATIENTS: Adult outpatients referred for endoscopy with moderate sedation.\nINTERVENTION: Pre-procedure alerts automatically sent 7 days before the procedure, highlighting any conditions/clinical history that may affect management of the patient.\nMAIN OUTCOME MEASUREMENTS: Physician use of the pre-procedure alert system and its effect on patient management.\nRESULTS: We studied 1682 procedures that met inclusion criteria for review by QPID and 364 alerts (1.6% of the eligible procedures). 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Nearly 80% of the alerts were reviewed and responded to by the physicians, and 70 total alerts resulted in a change in patient management (4.2% of eligible procedures).\nLIMITATIONS: The small size of the study group and the low rate of adverse events during the study period limit our findings. We thus plan to conduct a larger follow-up study to demonstrate changes in safety and efficiency.\nCONCLUSION: Use of advanced electronic health record technologies, such as QPID, may improve provider efficiency and patient outcomes in endoscopy units.", "publicationTitle": "Gastrointestinal Endoscopy", "publisher": "", "place": "", "date": "Oct 2012", "volume": "76", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "786-792", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Gastrointest. 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González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } } }, "data": { "key": "DBKMKQVA", "version": 9358, "parentItem": "FNXD6KR5", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2016-06-10T16:36:13Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/22901989", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-06-10T16:36:13Z", "dateModified": "2016-06-10T16:36:13Z" } }, { "key": "JRIHMZQK", "version": 9372, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/JRIHMZQK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/JRIHMZQK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Hanauer et al.", "parsedDate": "2015-06", "numChildren": 1 }, "data": { "key": "JRIHMZQK", "version": 9372, "itemType": "journalArticle", "title": "Supporting information retrieval from electronic health records: A report of University of Michigan's nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE)", "creators": [ { "creatorType": "author", "firstName": "David A.", "lastName": "Hanauer" }, { "creatorType": "author", "firstName": "Qiaozhu", "lastName": "Mei" }, { "creatorType": "author", "firstName": "James", "lastName": "Law" }, { "creatorType": "author", "firstName": "Ritu", "lastName": "Khanna" }, { "creatorType": "author", "firstName": "Kai", "lastName": "Zheng" } ], "abstractNote": "OBJECTIVE: This paper describes the University of Michigan's nine-year experience in developing and using a full-text search engine designed to facilitate information retrieval (IR) from narrative documents stored in electronic health records (EHRs). The system, called the Electronic Medical Record Search Engine (EMERSE), functions similar to Google but is equipped with special functionalities for handling challenges unique to retrieving information from medical text.\nMATERIALS AND METHODS: Key features that distinguish EMERSE from general-purpose search engines are discussed, with an emphasis on functions crucial to (1) improving medical IR performance and (2) assuring search quality and results consistency regardless of users' medical background, stage of training, or level of technical expertise.\nRESULTS: Since its initial deployment, EMERSE has been enthusiastically embraced by clinicians, administrators, and clinical and translational researchers. To date, the system has been used in supporting more than 750 research projects yielding 80 peer-reviewed publications. In several evaluation studies, EMERSE demonstrated very high levels of sensitivity and specificity in addition to greatly improved chart review efficiency.\nDISCUSSION: Increased availability of electronic data in healthcare does not automatically warrant increased availability of information. The success of EMERSE at our institution illustrates that free-text EHR search engines can be a valuable tool to help practitioners and researchers retrieve information from EHRs more effectively and efficiently, enabling critical tasks such as patient case synthesis and research data abstraction.\nCONCLUSION: EMERSE, available free of charge for academic use, represents a state-of-the-art medical IR tool with proven effectiveness and user acceptance.", "publicationTitle": "Journal of Biomedical Informatics", "publisher": "", "place": "", "date": "Jun 2015", "volume": "55", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "290-300", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "J Biomed Inform", "DOI": "10.1016/j.jbi.2015.05.003", "citationKey": "", "url": "", "accessDate": "", "PMID": "25979153", "PMCID": "PMC4527540", "ISSN": "1532-0480", "archive": "", "archiveLocation": "", "shortTitle": "Supporting information retrieval from electronic health records", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Electronic Health Records", "type": 1 }, { "tag": "Electronic Health Records (E05.318.308.940.968.625.500)", "type": 1 }, { "tag": "Information storage and retrieval (L01.470)", "type": 1 }, { "tag": "Meaningful Use", "type": 1 }, { "tag": "Michigan", "type": 1 }, { "tag": "Natural Language Processing", "type": 1 }, { "tag": "Search Engine", "type": 1 }, { "tag": "Search engine (L01.470.875)", "type": 1 }, { "tag": "Software", "type": 1 }, { "tag": "Software Validation", "type": 1 }, { "tag": "data mining", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-06-10T16:34:39Z", "dateModified": "2016-06-10T16:34:39Z" } }, { "key": "KP64N2TI", "version": 9357, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/KP64N2TI", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/KP64N2TI", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/43368/items/JRIHMZQK", "type": "application/json" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. 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Cited By :7

", "tags": [], "relations": {}, "dateAdded": "2016-06-10T16:07:38Z", "dateModified": "2016-06-10T16:07:38Z" } }, { "key": "SHCANDPR", "version": 9356, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/SHCANDPR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/SHCANDPR", "type": "text/html" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Hirsch et al.", "parsedDate": "2015", "numChildren": 2 }, "data": { "key": "SHCANDPR", "version": 9356, "itemType": "journalArticle", "title": "HARVEST, a longitudinal patient record summarizer", "creators": [ { "creatorType": "author", "firstName": "J.S.", "lastName": "Hirsch" }, { "creatorType": "author", "firstName": "J.S.", "lastName": "Tanenbaum" }, { "creatorType": "author", "firstName": "S.L.", "lastName": "Gorman" }, { "creatorType": "author", "firstName": "C.", "lastName": "Liu" }, { "creatorType": "author", "firstName": "E.", "lastName": "Schmitz" }, { "creatorType": "author", "firstName": "D.", "lastName": "Hashorva" }, { "creatorType": "author", "firstName": "A.", "lastName": "Ervits" }, { "creatorType": "author", "firstName": "D.", "lastName": "Vawdrey" }, { "creatorType": "author", "firstName": "M.", "lastName": "Sturm" }, { "creatorType": "author", "firstName": "N.", "lastName": "Elhadad" } ], "abstractNote": "Objective: To describe HARVEST, a novel point-of-care patient summarization and visualization tool, and to conduct a formative evaluation study to assess its effectiveness and gather feedback for iterative improvements. Materials and methods: HARVEST is a problem-based, interactive, temporal visualization of longitudinal patient records. Using scalable, distributed natural language processing and problem salience computation, the system extracts content from the patient notes and aggregates and presents information from multiple care settings. Clinical usability was assessed with physician participants using a timed, task-based chart review and questionnaire, with performance differences recorded between conditions (standard data review system and HARVEST). Results: HARVEST displays patient information longitudinally using a timeline, a problem cloud as extracted from notes, and focused access to clinical documentation. Despite lack of familiarity with HARVEST, when using a task-based evaluation, performance and time-to-task completion was maintained in patient review scenarios using HARVEST alone or the standard clinical information system at our institution. Subjects reported very high satisfaction with HARVEST and interest in using the system in their daily practice. Discussion: HARVEST is available for wide deployment at our institution. Evaluation provided informative feedback and directions for future improvements. Conclusions: HARVEST was designed to address the unmet need for clinicians at the point of care, facilitating review of essential patient information. The deployment of HARVEST in our institution allows us to study patient record summarization as an informatics intervention in a real-world setting. It also provides an opportunity to learn how clinicians use the summarizer, enabling informed interface and content iteration and optimization to improve patient care. © The Author 2014.", "publicationTitle": "Journal of the American Medical Informatics Association", "publisher": "", "place": "", "date": "2015", "volume": "22", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "263-274", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1136/amiajnl-2014-002945", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1067-5027", "archive": "Scopus", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Scopus", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Electronic health record", "type": 1 }, { "tag": "Natural Language Processing", "type": 1 }, { "tag": "Summarization", "type": 1 }, { "tag": "Visualization", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-06-10T16:07:38Z", "dateModified": "2016-06-10T16:07:38Z" } }, { "key": "HZJGH3R7", "version": 9355, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/HZJGH3R7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/HZJGH3R7", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/43368/items/CKFQEP9W", "type": "application/json" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "HZJGH3R7", "version": 9355, "parentItem": "CKFQEP9W", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2016-06-10T16:04:53Z", "url": "http://journals.plos.org/plosone/article/asset?id=10.1371/journal.pone.0087555.PDF", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Cohen et al. - 2014 - Redundancy-Aware Topic Modeling for Patient Record.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2016-06-10T16:04:53Z", "dateModified": "2016-06-10T16:05:00Z" } }, { "key": "KK6EMR87", "version": 9355, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/KK6EMR87", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/KK6EMR87", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/43368/items/CKFQEP9W", "type": "application/json" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } } }, "data": { "key": "KK6EMR87", "version": 9355, "parentItem": "CKFQEP9W", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2016-06-10T16:04:57Z", "url": "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087555", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "article.html", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2016-06-10T16:04:57Z", "dateModified": "2016-06-10T16:04:57Z" } }, { "key": "CKFQEP9W", "version": 9355, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/CKFQEP9W", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/CKFQEP9W", "type": "text/html" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Cohen et al.", "parsedDate": "2014-02-13", "numChildren": 2 }, "data": { "key": "CKFQEP9W", "version": 9355, "itemType": "journalArticle", "title": "Redundancy-Aware Topic Modeling for Patient Record Notes", "creators": [ { "creatorType": "author", "firstName": "Raphael", "lastName": "Cohen" }, { "creatorType": "author", "firstName": "Iddo", "lastName": "Aviram" }, { "creatorType": "author", "firstName": "Michael", "lastName": "Elhadad" }, { "creatorType": "author", "firstName": "Noémie", "lastName": "Elhadad" } ], "abstractNote": "The clinical notes in a given patient record contain much redundancy, in large part due to clinicians’ documentation habit of copying from previous notes in the record and pasting into a new note. Previous work has shown that this redundancy has a negative impact on the quality of text mining and topic modeling in particular. In this paper we describe a novel variant of Latent Dirichlet Allocation (LDA) topic modeling, Red-LDA, which takes into account the inherent redundancy of patient records when modeling content of clinical notes. To assess the value of Red-LDA, we experiment with three baselines and our novel redundancy-aware topic modeling method: given a large collection of patient records, (i) apply vanilla LDA to all documents in all input records; (ii) identify and remove all redundancy by chosing a single representative document for each record as input to LDA; (iii) identify and remove all redundant paragraphs in each record, leaving partial, non-redundant documents as input to LDA; and (iv) apply Red-LDA to all documents in all input records. Both quantitative evaluation carried out through log-likelihood on held-out data and topic coherence of produced topics and qualitative assessement of topics carried out by physicians show that Red-LDA produces superior models to all three baseline strategies. This research contributes to the emerging field of understanding the characteristics of the electronic health record and how to account for them in the framework of data mining. The code for the two redundancy-elimination baselines and Red-LDA is made publicly available to the community.", "publicationTitle": "PLOS ONE", "publisher": "", "place": "", "date": "13-feb-2014", "volume": "9", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "e87555", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "PLOS ONE", "DOI": "10.1371/journal.pone.0087555", "citationKey": "", "url": "http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087555", "accessDate": "2016-06-10T16:04:53Z", "PMID": "", "PMCID": "", "ISSN": "1932-6203", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "PLoS Journals", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Centrality", "type": 1 }, { "tag": "Electronic medical records", "type": 1 }, { "tag": "Physicians", "type": 1 }, { "tag": "Preprocessing", "type": 1 }, { "tag": "Quantitative analysis", "type": 1 }, { "tag": "Sequence databases", "type": 1 }, { "tag": "Text mining", "type": 1 }, { "tag": "data mining", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-06-10T16:04:53Z", "dateModified": "2016-06-10T16:04:53Z" } }, { "key": "8EHFRQ8T", "version": 9372, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/8EHFRQ8T", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/8EHFRQ8T", "type": "text/html" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Zheng et al.", "parsedDate": "2011-12", "numChildren": 1 }, "data": { "key": "8EHFRQ8T", "version": 9372, "itemType": "journalArticle", "title": "Coreference resolution: a review of general methodologies and applications in the clinical domain", "creators": [ { "creatorType": "author", "firstName": "Jiaping", "lastName": "Zheng" }, { "creatorType": "author", "firstName": "Wendy W.", "lastName": "Chapman" }, { "creatorType": "author", "firstName": "Rebecca S.", "lastName": "Crowley" }, { "creatorType": "author", "firstName": "Guergana K.", "lastName": "Savova" } ], "abstractNote": "Coreference resolution is the task of determining linguistic expressions that refer to the same real-world entity in natural language. Research on coreference resolution in the general English domain dates back to 1960s and 1970s. However, research on coreference resolution in the clinical free text has not seen major development. The recent US government initiatives that promote the use of electronic health records (EHRs) provide opportunities to mine patient notes as more and more health care institutions adopt EHR. Our goal was to review recent advances in general purpose coreference resolution to lay the foundation for methodologies in the clinical domain, facilitated by the availability of a shared lexical resource of gold standard coreference annotations, the Ontology Development and Information Extraction (ODIE) corpus.", "publicationTitle": "Journal of Biomedical Informatics", "publisher": "", "place": "", "date": "Dec 2011", "volume": "44", "issue": "6", "section": "", "partNumber": "", "partTitle": "", "pages": "1113-1122", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "J Biomed Inform", "DOI": "10.1016/j.jbi.2011.08.006", "citationKey": "", "url": "", "accessDate": "", "PMID": "21856441", "PMCID": "PMC3226856", "ISSN": "1532-0480", "archive": "", "archiveLocation": "", "shortTitle": "Coreference resolution", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Electronic Health Records", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Information Storage and Retrieval", "type": 1 }, { "tag": "Linguistics", "type": 1 }, { "tag": "Medical informatics", "type": 1 }, { "tag": "Natural Language Processing", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-06-10T15:55:39Z", "dateModified": "2016-06-10T15:55:39Z" } }, { "key": "METVNCKH", "version": 9354, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/METVNCKH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/METVNCKH", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/43368/items/8EHFRQ8T", "type": "application/json" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } } }, "data": { "key": "METVNCKH", "version": 9354, "parentItem": "8EHFRQ8T", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2016-06-10T15:55:39Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/21856441", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-06-10T15:55:39Z", "dateModified": "2016-06-10T15:55:39Z" } }, { "key": "JZFWEN6U", "version": 9372, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/JZFWEN6U", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/JZFWEN6U", "type": "text/html" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Soualmia et al.", "parsedDate": "2012", "numChildren": 1 }, "data": { "key": "JZFWEN6U", "version": 9372, "itemType": "journalArticle", "title": "Matching health information seekers' queries to medical terms", "creators": [ { "creatorType": "author", "firstName": "Lina F.", "lastName": "Soualmia" }, { "creatorType": "author", "firstName": "Elise", "lastName": "Prieur-Gaston" }, { "creatorType": "author", "firstName": "Zied", "lastName": "Moalla" }, { "creatorType": "author", "firstName": "Thierry", "lastName": "Lecroq" }, { "creatorType": "author", "firstName": "Stéfan J.", "lastName": "Darmoni" } ], "abstractNote": "BACKGROUND: The Internet is a major source of health information but most seekers are not familiar with medical vocabularies. Hence, their searches fail due to bad query formulation. Several methods have been proposed to improve information retrieval: query expansion, syntactic and semantic techniques or knowledge-based methods. However, it would be useful to clean those queries which are misspelled. In this paper, we propose a simple yet efficient method in order to correct misspellings of queries submitted by health information seekers to a medical online search tool.\nMETHODS: In addition to query normalizations and exact phonetic term matching, we tested two approximate string comparators: the similarity score function of Stoilos and the normalized Levenshtein edit distance. We propose here to combine them to increase the number of matched medical terms in French. We first took a sample of query logs to determine the thresholds and processing times. In the second run, at a greater scale we tested different combinations of query normalizations before or after misspelling correction with the retained thresholds in the first run.\nRESULTS: According to the total number of suggestions (around 163, the number of the first sample of queries), at a threshold comparator score of 0.3, the normalized Levenshtein edit distance gave the highest F-Measure (88.15%) and at a threshold comparator score of 0.7, the Stoilos function gave the highest F-Measure (84.31%). By combining Levenshtein and Stoilos, the highest F-Measure (80.28%) is obtained with 0.2 and 0.7 thresholds respectively. However, queries are composed by several terms that may be combination of medical terms. The process of query normalization and segmentation is thus required. The highest F-Measure (64.18%) is obtained when this process is realized before spelling-correction.\nCONCLUSIONS: Despite the widely known high performance of the normalized edit distance of Levenshtein, we show in this paper that its combination with the Stoilos algorithm improved the results for misspelling correction of user queries. Accuracy is improved by combining spelling, phoneme-based information and string normalizations and segmentations into medical terms. These encouraging results have enabled the integration of this method into two projects funded by the French National Research Agency-Technologies for Health Care. The first aims to facilitate the coding process of clinical free texts contained in Electronic Health Records and discharge summaries, whereas the second aims at improving information retrieval through Electronic Health Records.", "publicationTitle": "BMC bioinformatics", "publisher": "", "place": "", "date": "2012", "volume": "13 Suppl 14", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "S11", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "BMC Bioinformatics", "DOI": "10.1186/1471-2105-13-S14-S11", "citationKey": "", "url": "", "accessDate": "", "PMID": "23095521", "PMCID": "PMC3439674", "ISSN": "1471-2105", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Information Storage and Retrieval", "type": 1 }, { "tag": "Internet", "type": 1 }, { "tag": "Language", "type": 1 }, { "tag": "Medical informatics", "type": 1 }, { "tag": "Vocabulary, Controlled", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-06-10T15:54:03Z", "dateModified": "2016-06-10T15:54:03Z" } }, { "key": "RUBFPZA3", "version": 9353, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/RUBFPZA3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/RUBFPZA3", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/43368/items/JZFWEN6U", "type": "application/json" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } } }, "data": { "key": "RUBFPZA3", "version": 9353, "parentItem": "JZFWEN6U", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2016-06-10T15:54:03Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/23095521", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-06-10T15:54:03Z", "dateModified": "2016-06-10T15:54:03Z" } }, { "key": "WT39U59Z", "version": 9372, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/WT39U59Z", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/WT39U59Z", "type": "text/html" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Reeves et al.", "parsedDate": "2013-02", "numChildren": 1 }, "data": { "key": "WT39U59Z", "version": 9372, "itemType": "journalArticle", "title": "Detecting temporal expressions in medical narratives", "creators": [ { "creatorType": "author", "firstName": "Ruth M.", "lastName": "Reeves" }, { "creatorType": "author", "firstName": "Ferdo R.", "lastName": "Ong" }, { "creatorType": "author", "firstName": "Michael E.", "lastName": "Matheny" }, { "creatorType": "author", "firstName": "Joshua C.", "lastName": "Denny" }, { "creatorType": "author", "firstName": "Dominik", "lastName": "Aronsky" }, { "creatorType": "author", "firstName": "Glenn T.", "lastName": "Gobbel" }, { "creatorType": "author", "firstName": "Diane", "lastName": "Montella" }, { "creatorType": "author", "firstName": "Theodore", "lastName": "Speroff" }, { "creatorType": "author", "firstName": "Steven H.", "lastName": "Brown" } ], "abstractNote": "BACKGROUND: Clinical practice and epidemiological information aggregation require knowing when, how long, and in what sequence medically relevant events occur. The Temporal Awareness and Reasoning Systems for Question Interpretation (TARSQI) Toolkit (TTK) is a complete, open source software package for the temporal ordering of events within narrative text documents. TTK was developed on newspaper articles. We extended TTK to support medical notes using veterans' affairs (VA) clinical notes and compared it to TTK.\nMETHODS: We used a development set consisting of 200 VA clinical notes to modify and append rules to TTK's time tagger, creating Med-TTK. We then evaluated the performances of TTK and Med-TTK on an independent random selection of 100 clinical notes. Evaluation tasks were to identify and classify time-referring expressions as one of four temporal classes (DATE, TIME, DURATION, and SET). The reference standard for this test set was generated by dual human manual review with disagreements resolved by a third reviewer. Outcome measures included recall and precision for each class, and inter-rater agreement scores.\nRESULTS: There were 3146 temporal expressions in the reference standard. TTK identified 1595 temporal expressions. Recall was 0.15 (95% confidence interval [CI] 0.12-0.15) and precision was 0.27 (95% CI 0.25-0.29) for TTK. Med-TTK identified 3174 expressions. Recall was 0.86 (95% CI 0.84-0.87) and precision was 0.85 (95% CI 0.84-0.86) for Med-TTK.\nCONCLUSION: The algorithms for identifying and classifying temporal expressions in medical narratives developed within Med-TTK significantly improved performance compared to TTK. Natural language processing applications such as Med-TTK provide a foundation for meaningful longitudinal mapping of patient history events among electronic health records. The tool can be accessed at the following site: http://code.google.com/p/med-ttk/.", "publicationTitle": "International Journal of Medical Informatics", "publisher": "", "place": "", "date": "Feb 2013", "volume": "82", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "118-127", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Int J Med Inform", "DOI": "10.1016/j.ijmedinf.2012.04.006", "citationKey": "", "url": "", "accessDate": "", "PMID": "22595284", "PMCID": "", "ISSN": "1872-8243", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Electronic Health Records", "type": 1 }, { "tag": "Health Records, Personal", "type": 1 }, { "tag": "Narration", "type": 1 }, { "tag": "Natural Language Processing", "type": 1 }, { "tag": "Pattern Recognition, Automated", "type": 1 }, { "tag": "Software", "type": 1 }, { "tag": "Time Factors", "type": 1 }, { "tag": "United States", "type": 1 }, { "tag": "Vocabulary, Controlled", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-06-10T15:53:19Z", "dateModified": "2016-06-10T15:53:19Z" } }, { "key": "3ZRSXXW5", "version": 9352, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/3ZRSXXW5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/3ZRSXXW5", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/43368/items/WT39U59Z", "type": "application/json" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } } }, "data": { "key": "3ZRSXXW5", "version": 9352, "parentItem": "WT39U59Z", "itemType": "attachment", "linkMode": "linked_url", "title": "PubMed entry", "accessDate": "2016-06-10T15:53:19Z", "url": "http://www.ncbi.nlm.nih.gov/pubmed/22595284", "note": "", "contentType": "text/html", "charset": "", "tags": [], "relations": {}, "dateAdded": "2016-06-10T15:53:19Z", "dateModified": "2016-06-10T15:53:19Z" } }, { "key": "XFFAGFJA", "version": 9372, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/XFFAGFJA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/XFFAGFJA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Kovacevic et al.", "parsedDate": "2013-10", "numChildren": 1 }, "data": { "key": "XFFAGFJA", "version": 9372, "itemType": "journalArticle", "title": "Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives", "creators": [ { "creatorType": "author", "firstName": "Aleksandar", "lastName": "Kovacevic" }, { "creatorType": "author", "firstName": "Azad", "lastName": "Dehghan" }, { "creatorType": "author", "firstName": "Michele", "lastName": "Filannino" }, { "creatorType": "author", "firstName": "John A.", "lastName": "Keane" }, { "creatorType": "author", "firstName": "Goran", "lastName": "Nenadic" } ], "abstractNote": "OBJECTIVE: Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract temporal expressions and events from clinical narratives. The extracted temporal expressions were additionally normalized by assigning type, value, and modifier.\nMATERIALS AND METHODS: The system combines rule-based and machine learning approaches that rely on morphological, lexical, syntactic, semantic, and domain-specific features. Rule-based components were designed to handle the recognition and normalization of temporal expressions, while conditional random fields models were trained for event and temporal recognition.\nRESULTS: The system achieved micro F scores of 90% for the extraction of temporal expressions and 87% for clinical event extraction. The normalization component for temporal expressions achieved accuracies of 84.73% (expression's type), 70.44% (value), and 82.75% (modifier).\nDISCUSSION: Compared to the initial agreement between human annotators (87-89%), the system provided comparable performance for both event and temporal expression mining. While (lenient) identification of such mentions is achievable, finding the exact boundaries proved challenging.\nCONCLUSIONS: The system provides a state-of-the-art method that can be used to support automated identification of mentions of clinical events and temporal expressions in narratives either to support the manual review process or as a part of a large-scale processing of electronic health databases.", "publicationTitle": "Journal of the American Medical Informatics Association: JAMIA", "publisher": "", "place": "", "date": "2013 Sep-Oct", "volume": "20", "issue": "5", "section": "", "partNumber": "", "partTitle": "", "pages": "859-866", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "J Am Med Inform Assoc", "DOI": "10.1136/amiajnl-2013-001625", "citationKey": "", "url": "", "accessDate": "", "PMID": "23605114", "PMCID": "PMC3756271", "ISSN": "1527-974X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Artificial Intelligence", "type": 1 }, { "tag": "Electronic Health Records", "type": 1 }, { "tag": "Humans", "type": 1 }, { "tag": "Information Storage and Retrieval", "type": 1 }, { "tag": "Natural Language Processing", "type": 1 }, { "tag": "Time", "type": 1 }, { "tag": "Translational Medical Research", "type": 1 }, { "tag": "clinical NLP", "type": 1 }, { "tag": "clinical text mining", "type": 1 }, { "tag": "event extraction", "type": 1 }, { "tag": "termporal expression extraction", "type": 1 }, { "tag": "termporal expression normalization", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-06-10T15:52:50Z", "dateModified": "2016-06-10T15:52:50Z" } }, { "key": "5RKQN4I6", "version": 9351, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/5RKQN4I6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/5RKQN4I6", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/43368/items/XFFAGFJA", "type": "application/json" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. 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González", "links": { "alternate": { "href": "https://www.zotero.org/ragonzalez", "type": "text/html" } } }, "creatorSummary": "Morrison et al.", "parsedDate": "2009", "numChildren": 2 }, "data": { "key": "MQ8SH2MJ", "version": 9372, "itemType": "journalArticle", "title": "Repurposing the Clinical Record: Can an Existing Natural Language Processing System De-identify Clinical Notes?", "creators": [ { "creatorType": "author", "firstName": "Frances P.", "lastName": "Morrison" }, { "creatorType": "author", "firstName": "Li", "lastName": "Li" }, { "creatorType": "author", "firstName": "Albert M.", "lastName": "Lai" }, { "creatorType": "author", "firstName": "George", "lastName": "Hripcsak" } ], "abstractNote": "Electronic clinical documentation can be useful for activities such as public health surveillance, quality improvement, and research, but existing methods of de-identification may not provide sufficient protection of patient data. The general-purpose natural language processor MedLEE retains medical concepts while excluding the remaining text so, in addition to processing text into structured data, it may be able provide a secondary benefit of de-identification. Without modifying the system, the authors tested the ability of MedLEE to remove protected health information (PHI) by comparing 100 outpatient clinical notes with the corresponding XML-tagged output. Of 809 instances of PHI, 26 (3.2%) were detected in output as a result of processing and identification errors. However, PHI in the output was highly transformed, much appearing as normalized terms for medical concepts, potentially making re-identification more difficult. The MedLEE processor may be a good enhancement to other de-identification systems, both removing PHI and providing coded data from clinical text.", "publicationTitle": "Journal of the American Medical Informatics Association : JAMIA", "publisher": "", "place": "", "date": "2009", "volume": "16", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "37-39", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "J Am Med Inform Assoc", "DOI": "10.1197/jamia.M2862", "citationKey": "", "url": "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2605586/", "accessDate": "2016-06-10T15:51:00Z", "PMID": "18952938", "PMCID": "PMC2605586", "ISSN": "1067-5027", "archive": "", "archiveLocation": "", "shortTitle": "Repurposing the Clinical Record", "language": "", "libraryCatalog": "PubMed Central", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-06-10T15:51:00Z", "dateModified": "2016-06-10T15:51:00Z" } }, { "key": "AI89XG77", "version": 9350, "library": { "type": "group", "id": 43368, "name": "Health Informatics PUJ", "links": { "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/43368/items/AI89XG77", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/health_informatics_puj/items/AI89XG77", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/43368/items/MQ8SH2MJ", "type": "application/json" } }, "meta": { "createdByUser": { "id": 329597, "username": "ragonzalez", "name": "Rafael A. 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