Patient-based Literature Retrieval and Integration: A Use Case for Diabetes and arterial Hypertension

Specialized search engines such as PubMed, MedScape or Cochrane have increased dramatically the visibility of biomedical scientific results. These web-based tools allow physicians to access scientific papers instantly. However, this decisive improvement had not a proportional impact in clinical practice due to the lack of advanced search methods. Even queries highly specified for a concrete pathology frequently retrieve too many information, with publications related to patients treated by the physician beyond the scope of the results examined. In this work we present a new method to improve scientific article search using patient information. Two pathologies have been used within the project to retrieve relevant literature to patient data and to be integrated with other sources. Promising results suggest the suitability of the approach, highlighting publications dealing with patient features and facilitating literature search to physicians

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results: We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions: CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems

A Bibliometric Analysis of the Most Cited Articles Published in the Cumhuriyet Dental Journal

Objectives: The aim of this study was to evaluate the bibliometric profile of the most cited articles published in Cumhuriyet Dental Journal (CDJ). Materials and methods: TR Dizin database was used to search the most frequently cited articles. After the screening process, a researcher organized the articles according to the citation counts. The citation counts, publication year, authorship, contributing institutions and countries, manuscript language, field of dental research, study type and design, data analysis method and keywords were evaluated. Results: 123 citations were made to 76 articles. The citation counts ranged from 1 to 6. While the highest citation counts (n=17) were in 2012, the highest citation prevalence (1.93) was in 2011. There was a predominance of research area of Dental Materials (23.7%), original articles (69.7%), experimental studies (38.2%) and analytical data analysis method (90.2%). Original research articles in Dental Materials (88.9%) and Behavioral, Epidemiological and Health Services Research (100%), review articles in Prosthodontics (37.5%), and case reports in Diagnostic Research (57.1%) was more common. The highest citation prevalence was found in Periodontology (2.75). Most of the articles (28.9%) have 3 authors. Most of the articles(90.8%) originated from Turkey with the greatest contributions from Cumhuriyet University Faculty of Dentistry (22.4%). The manuscript language was Turkish at a rate of 57.9%. Among a total of 282 keywords, the most frequently used keywords were ‘‘bond strength’’ (n=6) and ‘‘composite resin’’ (n=5). Conclusions: The profile of citations in CDJ shows that original research in the research areas of Dental Materials and Behavioral, Epidemiological and Health Services Research is predominant, with growing participation of local authors

Citation classics on dental caries: a systematic review

Objective A systematic search was performed for the identification and analysis of the 100 most often cited articles on dental caries and to highlight the changing trends in the field of dentistry over time. Materials and Methods The search was performed without any restriction on the study design, publication year, or language using the Web of Science (WoS) group of Clarivate Analytics enabling the search through “All Databases.” Based on the citation count as available in WoS, the articles were sorted in a descending manner. Information regarding each article was then extracted, which included its authorship, counts of citation (in other databases), citation density, current citation index (2019), publication year, country of publication, journal of article, evidence level based on study design, and keywords description. Results The count of citation for each article varied in each database, that is, 175 to 2,003 in WoS, 89 to 1,981 in Scopus, and 126 to 3,492 when searched in Google Scholar. The highest number of articles (n = 10) related to dental caries were published in 2004. A total of 301 authors made valuable contributions to this field, out of which J.D. Featherstone had coauthored 6 articles. A significant negative correlation (p < 0.01) was found between the age of the article and the citation density (r =–0.545). However, a nonsignificant correlation (p = 0.952) occurred between the age of publication and the citation count (r = 0.006). Conclusion The results of this systematic review provide a critical appraisal of the context underpinning scientific developments in the field of dental caries and also highlighted trends in clinical management and research

Global mapping of research trends on antibacterial activity of green silver nanoparticles

Over the years, the quest for antibacterial agents from green nanoparticles has attracted great attention due to the global rise in the prevalence of multi-drug resistant bacteria. Although several studies on the antibacterial activity of plant-mediated silver nanoparticles have been documented, no bibliometric studies on the subject have been reported to date. As a result, the present study aimed to assess the global research trends on the antibacterial activity of green silver nanoparticles from 2000 to 2020. In the present study, we explored Science Citation Index Expanded (SCIE) to extract research articles written in English on the subject within the specified period. Two hundred and sixty-nine (269) eligible research articles were included in the bibliometric analysis and R-package “bibliometrix” was used to analyse the documents for annual scientific publications, authors’ impact, most relevant institutions, countries productivity, frequent words, co-occurrence network, co-citation network and authors/institutions/countries collaboration networks. Based on the analysis, the top three (3) authors, journals, institutions and countries were Kumar V (n = 5), Zangeneh MM (n = 5) and Oh BT (n = 4); King Saud University, Banaras Hindu University and Islamic Azad University; Journal of Cluster Science (n = 10), Applied Organometallic Chemistry (n = 8) and Microbial Pathogenesis (n = 8); India, Iran, and Korea. The study findings highlighted the gaps in a research collaboration that negate productivity. Therefore, we are optimitic that this study would enlighten researchers in the field about the research lapses and the need for research collaboration in future studies

A bibliometric study of the top 100 most-cited randomized controlled trials, systematic reviews and meta-analyses published in endodontic journals

Aim To identify and analyse the main features of the top 100 most‐cited randomized controlled trials, systematic reviews and meta‐analyses published in endodontic journals from 1961 to 2018. Methodology The Clarivate Analytics’ Web of Science ‘All Databases’ was used to search and analyse the 100 most frequently cited randomized controlled trials, systematic reviews and meta‐analyses having ‘randomized’, ‘randomised’, ‘randomized controlled’, ‘randomised controlled’, ‘randomized controlled trial’, ‘randomized controlled trials’, ‘clinical trial’, ‘systematic’, ‘systematic review’, ‘meta‐analysis’, and ‘meta‐analyses’ in the title section. The ‘International Endodontic Journal’, ‘Journal of Endodontics’, ‘Oral Surgery Oral Medicine Oral Pathology Oral Radiology and Endodontology’, ‘Australian Endodontic Journal’, ‘Endodontics & Dental Traumatology’, ‘Endo‐Endodontic Practice Today’ and ‘European Endodontic Journal’ were included in the publication name section. After ranking the articles in a descending order based on their citation counts, each article was cross‐matched with the citation counts in Elsevier's Scopus and Google Scholar. The articles were analysed, and information on citation counts, citation density, year of publication, contributing authors, institutions and countries, journal of publication, study design, topic of the article and keywords was extracted. Results The citation counts of the 100 most‐cited articles varied from 235 to 20 (Web of Science), 276 to 17 (Scopus) and 696 to 1 (Google Scholar). The year in which the top 100 articles were published was 2010 (n = 13). Among 373 authors, the greatest number of articles was associated with three individuals namely Reader A (n = 5), Beck M (n = 5) and Kvist T (n = 5). Most of the articles originated from the United States (n = 24) with the greatest contribution from Ohio State University (USA) (n = 5). Randomized controlled trials were the most frequent study design (n = 45) followed by systematic reviews (n = 30) with outcome studies of root canal treatment being the major topic (n = 35). The Journal of Endodontics published the largest number of included articles (n = 70) followed by the International Endodontic Journal (n = 27). Among 259 unique keywords, meta‐analysis (n = 23) and systematic review (n = 23) were the most frequently used. Conclusion This study has revealed that year of publication had no obvious impact on citation count. The bibliometric analysis highlighted the quantity and quality of research, and the evolution of scientific advancements made in the field of Endodontology over time. Articles before 1996, that is prior to the CONSORT statement that encouraged authors to include specific terms in the title and keywords, may not have been included in this electronic search

Care episode retrieval: distributional semantic models for information retrieval in the clinical domain

Patients' health related information is stored in electronic health records (EHRs) by health service providers. These records include sequential documentation of care episodes in the form of clinical notes. EHRs are used throughout the health care sector by professionals, administrators and patients, primarily for clinical purposes, but also for secondary purposes such as decision support and research. The vast amounts of information in EHR systems complicate information management and increase the risk of information overload. Therefore, clinicians and researchers need new tools to manage the information stored in the EHRs. A common use case is, given a - possibly unfinished - care episode, to retrieve the most similar care episodes among the records. This paper presents several methods for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Models include variants of random indexing and the semantic neural network model word2vec. Two novel methods are introduced that utilize the ICD-10 codes attached to care episodes to better induce domain-specificity in the semantic model. We report on experimental evaluation of care episode retrieval that circumvents the lack of human judgements regarding episode relevance. Results suggest that several of the methods proposed outperform a state-of-the art search engine (Lucene) on the retrieval task

Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone

Eine Modellierungssprache zur Entwicklung effizienter Vorlagen für die klinische Befunddokumentation: Im Fachbereich der Gastroenterologie

Die klinische Dokumentation ist ein zentraler Bestandteil der Patientenversorgung. Sie dient der räumlichen und zeitlichen Überbrückung des Kommunikationsbedarfs zwischen den an der Versorgung beteiligten Akteuren. Die Erstellung einer vollständigen und präzi-sen Dokumentation beansprucht einen erheblichen Teil der ärztlichen Arbeitszeit. Diese Zeit zu reduzieren und dabei die Qualität der erfassten Daten zu verbessern gehört zu den technologischen Aufgaben des klinischen Informationssystems. Ziel dieser Arbeit ist die Konzeption einer Modellierungssprache zur Beschreibung von Befundvorlagen für die strukturierte Dokumentation. Darauf aufbauend werden die Möglichkeiten der Integration in konventionelle Informationssysteme beschrieben. Eine Anforderung dieser Arbeit ist es einer breiteren Autorenbasis die Mitgestaltung der Vorlagen zu ermöglichen. Dieses Ziel wurde insbesondere durch eine visuelle Notation sowie ein Konstrukt für die kollaborative Entwicklung der Vorlagen erreicht. Die Beschreibungssprache wurde zyklisch den Anfor-derungen der Klinikärzte und Autoren angepasst. Eine mit der Beschreibungssprache ver-knüpfbare Ontologie ist die Basis für Automatismen und verbessert als semantisches Be-zugssystem die Qualität der erfassten Daten. Das Artefakt ermöglicht zum einen die zeitef-fiziente Erstellung der Befundberichte durch die strukturierte, leitfadengestützte Doku-mentation und zum anderen wird analog zu dem narrativen Befundbericht automatisch ein formales Modell erstellt, dass die Möglichkeiten der Eingabe, Repräsentation und Auswer-tung der Daten erweitert. Im Rahmen der abschließenden Evaluation wurde das Artefakt in ein klinisches Informationssystem mit relationaler Datenbasis integriert. Es konnte ge-zeigt werden, dass durch das entwickelte Artefakt und insbesondere durch die Möglichkei-ten des formalen Modells, beispielsweise die Automatismen, das initiale Ziel einer zeiteffi-zienten Dokumentation erreicht wurde. Darüber hinaus konnte, vor allem durch erweiterte Möglichkeiten der Datenauswertung, die Qualität der Daten und deren Nutzen verbessert werden. Das Artefakt wurde innerhalb der Gastroenterologie evaluiert und kann auf weite-re Fachbereiche, insbesondere der Inneren Medizin übertragen werden