Methods of Information in Medicine最新文献

{"title":"An Intelligent Medical Isolation Observation Management System Based on the Internet of Things.","authors":"Wensheng Sun, Chunmei Wang, Jimin Sun, Ziping Miao, Feng Ling, Guangsong Wu","doi":"10.1055/s-0042-1757185","DOIUrl":"https://doi.org/10.1055/s-0042-1757185","url":null,"abstract":"<p><strong>Background: </strong>Since COVID-19 (coronavirus disease 2019) was discovered in December 2019, it has spread worldwide. Early isolation and medical observation management of cases and their close contacts are the key to controlling the spread of the epidemic. However, traditional medical observation requires medical staff to measure body temperature and other vital signs face to face and record them manually. There is a general shortage of human and personal protective equipment and a high risk of occupational exposure, which seriously threaten the safety of medical staff.</p><p><strong>Methods: </strong>We designed an intelligent crowd isolation medical observation management system framework based on the Internet of Things using wireless telemetry and big data cloud platform remote management technology. Through a smart wearable device with built-in sensors, vital sign data and geographical locations of medical observation subjects are collected and automatically uploaded to the big data monitoring platform on demand. According to the comprehensive analysis of the set threshold parameters, abnormal subjects are screened out, and activity tracking and health status monitoring for medical observation and management objectives are performed through monitoring and early warning management and post-event data traceability. In the trial of this system, the subjects wore the wristwatches designed in this study and real-time monitoring was conducted throughout the whole process. Additionally, for comparison, the traditional method was also used for these people. Medical staff came to measure their temperature twice a day. The subjects were 1,128 returned overseas Chinese from Europe.</p><p><strong>Results: </strong>Compared with the traditional vital sign detection method, the system designed in this study has the advantages of a fast response, low error, stability, and good endurance. It can monitor the temperature, pulse, blood pressure, and heart rate of the monitored subject in real time. The system designed in this study and the traditional vital sign detection method were both used to monitor 1,128 close contacts with COVID-19. There were six cases of abnormal body temperature that were missed by traditional manual temperature measurement in the morning and evening, and these six cases (0.53%) were sent to the hospital for further diagnosis. The abnormal body temperature of these six cases was not found in time when the medical staff came to check the temperature on a twice-a-day basis. The system designed in this study, however, can detect the abnormal body temperature of all these six people. The sensitivity and specificity of our system were both 100%.</p><p><strong>Conclusion: </strong>The system designed in this study can monitor the body temperature, blood oxygen, blood pressure, heart rate, and geographical location of the monitoring subject in real time. It can be extended to COVID-19 medical observation isolation points, shel","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 5-06","pages":"155-166"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9609025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing Automated Computer Algorithms to Phenotype Periodontal Disease Diagnoses in Electronic Dental Records.","authors":"Jay Sureshbhai Patel,&nbsp;Ryan Brandon,&nbsp;Marisol Tellez,&nbsp;Jasim M Albandar,&nbsp;Rishi Rao,&nbsp;Joachim Krois,&nbsp;Huanmei Wu","doi":"10.1055/s-0042-1757880","DOIUrl":"https://doi.org/10.1055/s-0042-1757880","url":null,"abstract":"<p><strong>Objective: </strong>Our objective was to phenotype periodontal disease (PD) diagnoses from three different sections (diagnosis codes, clinical notes, and periodontal charting) of the electronic dental records (EDR) by developing two automated computer algorithms.</p><p><strong>Methods: </strong>We conducted a retrospective study using EDR data of patients (<i>n</i> = 27,138) who received care at Temple University Maurice H. Kornberg School of Dentistry from January 1, 2017 to August 31, 2021. We determined the completeness of patient demographics, periodontal charting, and PD diagnoses information in the EDR. Next, we developed two automated computer algorithms to automatically diagnose patients' PD statuses from clinical notes and periodontal charting data. Last, we phenotyped PD diagnoses using automated computer algorithms and reported the improved completeness of diagnosis.</p><p><strong>Results: </strong>The completeness of PD diagnosis from the EDR was as follows: periodontal diagnosis codes 36% (<i>n</i> = 9,834), diagnoses in clinical notes 18% (<i>n</i> = 4,867), and charting information 80% (<i>n</i> = 21,710). After phenotyping, the completeness of PD diagnoses improved to 100%. Eleven percent of patients had healthy periodontium, 43% were with gingivitis, 3% with stage I, 36% with stage II, and 7% with stage III/IV periodontitis.</p><p><strong>Conclusions: </strong>We successfully developed, tested, and deployed two automated algorithms on big EDR datasets to improve the completeness of PD diagnoses. After phenotyping, EDR provided 100% completeness of PD diagnoses of 27,138 unique patients for research purposes. This approach is recommended for use in other large databases for the evaluation of their EDR data quality and for phenotyping PD diagnoses and other relevant variables.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e125-e133"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a4/d8/10-1055-s-0042-1757880.PMC9788909.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9247777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical Text Prediction and Suggestion Using Generative Pretrained Transformer Models with Dental Medical Notes.","authors":"Joseph Sirrianni,&nbsp;Emre Sezgin,&nbsp;Daniel Claman,&nbsp;Simon L Linwood","doi":"10.1055/a-1900-7351","DOIUrl":"https://doi.org/10.1055/a-1900-7351","url":null,"abstract":"<p><strong>Background: </strong>Generative pretrained transformer (GPT) models are one of the latest large pretrained natural language processing models that enables model training with limited datasets and reduces dependency on large datasets, which are scarce and costly to establish and maintain. There is a rising interest to explore the use of GPT models in health care.</p><p><strong>Objective: </strong>We investigate the performance of GPT-2 and GPT-Neo models for medical text prediction using 374,787 free-text dental notes.</p><p><strong>Methods: </strong>We fine-tune pretrained GPT-2 and GPT-Neo models for next word prediction on a dataset of over 374,000 manually written sections of dental clinical notes. Each model was trained on 80% of the dataset, validated on 10%, and tested on the remaining 10%. We report model performance in terms of next word prediction accuracy and loss. Additionally, we analyze the performance of the models on different types of prediction tokens for categories. For comparison, we also fine-tuned a non-GPT pretrained neural network model, XLNet (large), for next word prediction. We annotate each token in 100 randomly sampled notes by category (e.g., names, abbreviations, clinical terms, punctuation, etc.) and compare the performance of each model by token category.</p><p><strong>Results: </strong>Models present acceptable accuracy scores (GPT-2: 76%; GPT-Neo: 53%), and the GPT-2 model also performs better in manual evaluations, especially for names, abbreviations, and punctuation. Both GPT models outperformed XLNet in terms of accuracy. The results suggest that pretrained models have the potential to assist medical charting in the future. We share the lessons learned, insights, and suggestions for future implementations.</p><p><strong>Conclusion: </strong>The results suggest that pretrained models have the potential to assist medical charting in the future. Our study presented one of the first implementations of the GPT model used with medical notes.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 5-06","pages":"195-200"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9254100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3LGM2IHE: Requirements for Data-Protection-Compliant Research Infrastructures-A Systematic Comparison of Theory and Practice-Oriented Implementation.","authors":"Robert Gött,&nbsp;Sebastian Stäubert,&nbsp;Alexander Strübing,&nbsp;Alfred Winter,&nbsp;Angela Merzweiler,&nbsp;Björn Bergh,&nbsp;Knut Kaulke,&nbsp;Thomas Bahls,&nbsp;Wolfgang Hoffmann,&nbsp;Martin Bialke","doi":"10.1055/a-1950-2791","DOIUrl":"https://doi.org/10.1055/a-1950-2791","url":null,"abstract":"<p><strong>Objectives: </strong>The TMF (Technology, Methods, and Infrastructure for Networked Medical Research) Data Protection Guide (TMF-DP) makes path-breaking recommendations on the subject of data protection in research projects. It includes comprehensive requirements for applications such as patient lists, pseudonymization services, and consent management services. Nevertheless, it lacks a structured, categorized list of requirements for simplified application in research projects and systematic evaluation. The 3LGM2IHE (\"Three-layer Graphbased meta model - Integrating the Healthcare Enterprise [IHE] \" ) project is funded by the German Research Foundation (DFG). 3LGM2IHE aims to define modeling paradigms and implement modeling tools for planning health care information systems. In addition, one of the goals is to create and publish 3LGM² information system architecture design patterns (short \"design patterns\") for the community as design models in terms of a framework. A structured list of data protection-related requirements based on the TMF-DP is a precondition to integrate functions (3LGM² Domain Layer) and building blocks (3LGM² Logical Tool Layer) in 3LGM² design patterns.</p><p><strong>Methods: </strong>In order to structure the continuous text of the TMF-DP, requirement types were defined in a first step. In a second step, dependencies and delineations of the definitions were identified. In a third step, the requirements from the TMF-DP were systematically extracted. Based on the identified lists of requirements, a fourth step included the comparison of the identified requirements with exemplary open source tools as provided by the \"Independent Trusted Third Party of the University Medicine Greifswald\" (TTP tools).</p><p><strong>Results: </strong>As a result, four lists of requirements were created, which contain requirements for the \"patient list\", the \"pseudonymization service\", and the \"consent management\", as well as cross-component requirements from the TMF-DP chapter 6 in a structured form. Further to requirements (1), possible variants (2) of implementations (to fulfill a single requirement) and recommendations (3) were identified. A comparison of the requirements lists with the functional scopes of the open source tools E-PIX (record linkage), gPAS (pseudonym management), and gICS (consent management) has shown that these fulfill more than 80% of the requirements.</p><p><strong>Conclusions: </strong>A structured set of data protection-related requirements facilitates a systematic evaluation of implementations with respect to the fulfillment of the TMF-DP guidelines. These re-usable lists provide a decision aid for the selection of suitable tools for new research projects. As a result, these lists form the basis for the development of data protection-related 3LGM² design patterns as part of the 3LGM2IHE project.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e134-e148"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/d1/2e/10-1055-a-1950-2791.PMC9788907.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9259948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Testing Requirements for an Integrated Maternal and Child Health Information System in Iran: A Design Thinking Case Study.","authors":"Zahra Meidani,&nbsp;Alireza Moravveji,&nbsp;Shirin Gohari,&nbsp;Hamideh Ghaffarian,&nbsp;Sahar Zare,&nbsp;Fatemeh Vaseghi,&nbsp;Gholam Abbas Moosavi,&nbsp;Ali Mohammad Nickfarjam,&nbsp;Felix Holl","doi":"10.1055/a-1860-8618","DOIUrl":"https://doi.org/10.1055/a-1860-8618","url":null,"abstract":"<p><strong>Background: </strong>Management of child health care can be negatively affected by incomplete recording, low data quality, and lack of data integration of health management information systems to support decision making and public health program needs. Given the importance of identifying key determinants of child health via capturing and integrating accurate and high-quality information, we aim to address this gap through the development and testing requirements for an integrated child health information system.</p><p><strong>Subjects and methods: </strong>A five-phase design thinking approach including empathizing, defining, ideation, prototyping, and testing was applied. We employed observations and interviews with the health workers at the primary health care network to identify end-users' challenges and needs using tools in human-centered design and focus group discussion. Then, a potential solution to the identified problems was developed as an integrated maternal and child health information system (IMCHIS) prototype and tested using Software Quality Requirements and Evaluation Model (SQuaRE) ISO/IEC 25000.</p><p><strong>Results: </strong>IMCHIS was developed as a web-based system with 74 data elements and seven maternal and child health care requirements. The requirements of \"child disease\" with weight (0.26), \"child nutrition\" with weight (0.20), and \"prenatal care\" with weight (0.16) acquired the maximum weight coefficient. In the testing phase, the highest score with the weight coefficient of 0.48 and 0.73 was attributed to efficiency and functionality characteristics, focusing on software capability to fulfill the tasks that meet users' needs.</p><p><strong>Conclusion: </strong>Implementing a successful child health care system integrates both maternal and child health care information systems to track the effect of maternal conditions on child health and support managing performance and optimizing service delivery. The highest quality score of IMCHIS in efficiency and functionality characteristics confirms that it owns the capability to identify key determinants of child health.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e64-e72"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c1/3d/10-1055-a-1860-8618.PMC9788911.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9247243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using an Ontology to Derive a Sharable and Interoperable Relational Data Model for Heterogeneous Healthcare Data and Various Applications.","authors":"Christina Khnaisser,&nbsp;Luc Lavoie,&nbsp;Benoit Fraikin,&nbsp;Adrien Barton,&nbsp;Samuel Dussault,&nbsp;Anita Burgun,&nbsp;Jean-François Ethier","doi":"10.1055/a-1877-9498","DOIUrl":"https://doi.org/10.1055/a-1877-9498","url":null,"abstract":"<p><strong>Background: </strong>A large volume of heavily fragmented data is generated daily in different healthcare contexts and is stored using various structures with different semantics. This fragmentation and heterogeneity make secondary use of data a challenge. Data integration approaches that derive a common data model from sources or requirements have some advantages. However, these approaches are often built for a specific application where the research questions are known. Thus, the semantic and structural reconciliation is often not reusable nor reproducible. A recent integration approach using knowledge models has been developed with ontologies that provide a strong semantic foundation. Nonetheless, deriving a data model that captures the richness of the ontology to store data with their full semantic remains a challenging task.</p><p><strong>Objectives: </strong>This article addresses the following question: How to design a sharable and interoperable data model for storing heterogeneous healthcare data and their semantic to support various applications?</p><p><strong>Method: </strong>This article describes a method using an ontological knowledge model to automatically generate a data model for a domain of interest. The model can then be implemented in a relational database which efficiently enables the collection, storage, and retrieval of data while keeping semantic ontological annotations so that the same data can be extracted for various applications for further processing.</p><p><strong>Results: </strong>This article (1) presents a comparison of existing methods for generating a relational data model from an ontology using 23 criteria, (2) describes standard conversion rules, and (3) presents <i>O n t o R e l a</i> , a prototype developed to demonstrate the conversion rules.</p><p><strong>Conclusion: </strong>This work is a first step toward automating and refining the generation of sharable and interoperable relational data models using ontologies with a freely available tool. The remaining challenges to cover all the ontology richness in the relational model are pointed out.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e73-e88"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9788910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9247255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Approach to Configuring MetaMap for Optimal Performance.","authors":"Xia Jing,&nbsp;Akash Indani,&nbsp;Nina Hubig,&nbsp;Hua Min,&nbsp;Yang Gong,&nbsp;James J Cimino,&nbsp;Dean F Sittig,&nbsp;Lior Rennert,&nbsp;David Robinson,&nbsp;Paul Biondich,&nbsp;Adam Wright,&nbsp;Christian Nøhr,&nbsp;Timothy Law,&nbsp;Arild Faxvaag,&nbsp;Ronald Gimbel","doi":"10.1055/a-1862-0421","DOIUrl":"https://doi.org/10.1055/a-1862-0421","url":null,"abstract":"<p><strong>Background: </strong>MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward.</p><p><strong>Objective: </strong>To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance.</p><p><strong>Methods: </strong>MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (<i>β</i> =1) were calculated.</p><p><strong>Results: </strong>The percentages of exact matches and missing gold-standard terms were 0.6-0.79 and 0.09-0.3 for one behavior option, and 0.56-0.8 and 0.09-0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively.</p><p><strong>Conclusion: </strong>We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e51-e63"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/95/91/10-1055-a-1862-0421.PMC9788913.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9809549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maturity Level of Digital Reproductive, Maternal, Newborn, and Child Health Initiatives in Jordan and Palestine.","authors":"Mohammad S Alyahya,&nbsp;Niveen M E Abu-Rmeileh,&nbsp;Yousef S Khader,&nbsp;Maysaa Nemer,&nbsp;Nihaya A Al-Sheyab,&nbsp;Alexandrine Pirlot de Corbion,&nbsp;Laura Lazaro Cabrera,&nbsp;Sundeep Sahay","doi":"10.1055/s-0042-1756651","DOIUrl":"https://doi.org/10.1055/s-0042-1756651","url":null,"abstract":"<p><strong>Background: </strong>While there is a rapid increase in digital health initiatives focusing on the processing of personal data for strengthening the delivery of reproductive, maternal, newborn, and child health (RMNCH) services in fragile settings, these are often unaccompanied at both the policy and operational levels with adequate legal and regulatory frameworks.</p><p><strong>Objective: </strong>The main aim was to understand the maturity level of digital personal data initiatives for RMNCH services within fragile contexts. This aim was performed by choosing digital health initiatives from each country (two in Jordan and three in Palestine) based on RMNCH.</p><p><strong>Methods: </strong>A qualitative study design was adopted. We developed a digital maturity assessment tool assessing two maturity levels: the information and communications technology digital infrastructure, and data governance and interoperability in place for the five selected RMNCH initiatives in Jordan and Palestine.</p><p><strong>Results: </strong>Overall, the digital infrastructure and technological readiness components are more advanced and show higher maturity levels compared with data governance and interoperability components in Jordan and Palestine. In Jordan, the overall Jordan stillbirths and neonatal deaths surveillance initiative maturity indicators are somehow less advanced than those of the Electronic Maternal and Child Health Handbook-Jordan (EMCH-J) application. In Palestine, the Electronic Maternal and Child Health-registry initiative maturity indicators are more advanced than both Avicenna and EMCH-Palestine initiatives.</p><p><strong>Conclusion: </strong>The findings highlighted several challenges and opportunities around the application and implementation of selected digital health initiatives in the provision of RMNCH in Jordan and Palestine. Our findings shed lights on the maturity level of these initiatives within fragile contexts. The maturity level of the five RMNCH initiatives in both countries is inadequate and requires further advancement before they can be scaled up and scaled out. Taking the World Health Organization recommendations into account when developing, implementing, and scaling digital health initiatives in low- and middle-income countries can result in successful and sustainable initiatives, thus meeting health needs and improving the quality of health care received by individuals especially those living in fragile contexts.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 5-06","pages":"139-154"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9609024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse.","authors":"Miguel Pedrera-Jiménez,&nbsp;Noelia García-Barrio,&nbsp;Paula Rubio-Mayo,&nbsp;Alberto Tato-Gómez,&nbsp;Juan Luis Cruz-Bermúdez,&nbsp;José Luis Bernal-Sobrino,&nbsp;Adolfo Muñoz-Carrero,&nbsp;Pablo Serrano-Balazote","doi":"10.1055/s-0042-1757763","DOIUrl":"https://doi.org/10.1055/s-0042-1757763","url":null,"abstract":"<p><strong>Background: </strong>During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable.</p><p><strong>Objectives: </strong>This study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization.</p><p><strong>Methods: </strong>The proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML.</p><p><strong>Results: </strong>First, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined.</p><p><strong>Conclusions: </strong>This study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e89-e102"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/54/b2/10-1055-s-0042-1757763.PMC9788916.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9253954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online.","authors":"Toralf Kirsten,&nbsp;Frank Meineke,&nbsp;Henry Löffler-Wirth,&nbsp;Alexandr Uciteli,&nbsp;Christoph Beger,&nbsp;Sebastian Stäubert,&nbsp;Matthias Löbe,&nbsp;Rene Hänsel,&nbsp;Franziska G Rauscher,&nbsp;Judith Christina Schuster,&nbsp;Thomas Peschel,&nbsp;Heinrich Herre,&nbsp;Jonas Wagner,&nbsp;Silke Zachariae,&nbsp;Christoph Engel,&nbsp;Markus Scholz,&nbsp;Erhard Rahm,&nbsp;Hans Binder,&nbsp;Markus Löffler","doi":"10.1055/a-1914-1985","DOIUrl":"https://doi.org/10.1055/a-1914-1985","url":null,"abstract":"<p><strong>Background: </strong>Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains.</p><p><strong>Objective: </strong>The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects.</p><p><strong>Methods: </strong>Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups.</p><p><strong>Results: </strong>The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e103-e115"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bf/4e/10-1055-a-1914-1985.PMC9788914.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9308245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}