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Wolfien M, Ahmadi N, Fitzer K, Grummt S, Heine KL, Jung IC, Krefting D, Kühn A, Peng Y, Reinecke I, Scheel J, Schmidt T, Schmücker P, Schüttler C, Waltemath D, Zoch M, Sedlmayr M. Ten Topics to Get Started in Medical Informatics Research. J Med Internet Res 2023; 25:e45948. [PMID: 37486754 PMCID: PMC10407648 DOI: 10.2196/45948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.
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Affiliation(s)
- Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
| | - Najia Ahmadi
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kai Fitzer
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
| | - Sophia Grummt
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kilian-Ludwig Heine
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ian-C Jung
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center, Goettingen, Germany
| | - Andreas Kühn
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Yuan Peng
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ines Reinecke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Tobias Schmidt
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Paul Schmücker
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Christina Schüttler
- Central Biobank Erlangen, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dagmar Waltemath
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Michele Zoch
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
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van de Burgt BWM, Wasylewicz ATM, Dullemond B, Grouls RJE, Egberts TCG, Bouwman A, Korsten EMM. Combining text mining with clinical decision support in clinical practice: a scoping review. J Am Med Inform Assoc 2022; 30:588-603. [PMID: 36512578 PMCID: PMC9933076 DOI: 10.1093/jamia/ocac240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/17/2022] [Accepted: 12/01/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE Combining text mining (TM) and clinical decision support (CDS) could improve diagnostic and therapeutic processes in clinical practice. This review summarizes current knowledge of the TM-CDS combination in clinical practice, including their intended purpose, implementation in clinical practice, and barriers to such implementation. MATERIALS AND METHODS A search was conducted in PubMed, EMBASE, and Cochrane Library databases to identify full-text English language studies published before January 2022 with TM-CDS combination in clinical practice. RESULTS Of 714 identified and screened unique publications, 39 were included. The majority of the included studies are related to diagnosis (n = 26) or prognosis (n = 11) and used a method that was developed for a specific clinical domain, document type, or application. Most of the studies selected text containing parts of the electronic health record (EHR), such as reports (41%, n = 16) and free-text narratives (36%, n = 14), and 23 studies utilized a tool that had software "developed for the study". In 15 studies, the software source was openly available. In 79% of studies, the tool was not implemented in clinical practice. Barriers to implement these tools included the complexity of natural language, EHR incompleteness, validation and performance of the tool, lack of input from an expert team, and the adoption rate among professionals. DISCUSSION/CONCLUSIONS The available evidence indicates that the TM-CDS combination may improve diagnostic and therapeutic processes, contributing to increased patient safety. However, further research is needed to identify barriers to implementation and the impact of such tools in clinical practice.
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Affiliation(s)
- Britt W M van de Burgt
- Corresponding Author: Britt W.M. van de Burgt, MSc, Department Healthcare Intelligence, Catharina Hospital Eindhoven, Michelangelolaan 2, 5623 EJ Eindhoven, The Netherlands;
| | - Arthur T M Wasylewicz
- Department Healthcare Intelligence, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Bjorn Dullemond
- Department of Mathematics and Computer Science, Technical University of Eindhoven, Eindhoven, The Netherlands
| | - Rene J E Grouls
- Department of Clinical Pharmacy, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Toine C G Egberts
- Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, the Netherlands,Department of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Arthur Bouwman
- Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, Eindhoven, The Netherlands,Department of Anesthesiology, Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Erik M M Korsten
- Department Healthcare Intelligence, Catharina Hospital Eindhoven, Eindhoven, The Netherlands,Department of Electrical Engineering, Signal Processing Group, Technical University Eindhoven, Eindhoven, The Netherlands
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Messmann H, Ebigbo A, Hassan C, Repici A, Mori Y. How to Integrate Artificial Intelligence in Gastrointestinal Practice. Gastroenterology 2022; 162:1583-1586. [PMID: 35196540 DOI: 10.1053/j.gastro.2022.02.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Affiliation(s)
- Helmut Messmann
- Department of Gastroenterology, University Hospital Augsburg, Germany.
| | - Alanna Ebigbo
- Department of Gastroenterology, University Hospital Augsburg, Germany
| | - Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center -IRCCS-, Endoscopy Unit, Rozzano, Italy
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway; Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
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Shen L, Levie A, Singh H, Murray K, Desai S. Harnessing Event Report Data to Identify Diagnostic Error During the COVID-19 Pandemic. Jt Comm J Qual Patient Saf 2022; 48:71-80. [PMID: 34844874 PMCID: PMC8553646 DOI: 10.1016/j.jcjq.2021.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/12/2021] [Accepted: 10/14/2021] [Indexed: 10/26/2022]
Abstract
INTRODUCTION COVID-19 exposed systemic gaps with increased potential for diagnostic error. This project implemented a new approach leveraging electronic safety reporting to identify and categorize diagnostic errors during the pandemic. METHODS All safety event reports from March 1, 2020, to February 28, 2021, at an academic medical center were evaluated using two complementary pathways (Pathway 1: all reports with explicit mention of COVID-19; Pathway 2: all reports without explicit mention of COVID-19 where natural language processing [NLP] plus logic-based stratification was applied to identify potential cases). Cases were evaluated by manual review to identify diagnostic error/delay and categorize error type using a recently proposed classification framework of eight categories of pandemic-related diagnostic errors. RESULTS A total of 14,230 reports were included, with 95 (0.7%) identified as cases of diagnostic error/delay. Pathway 1 (n = 1,780 eligible reports) yielded 45 reports with diagnostic error/delay (positive predictive value [PPV] = 2.5%), of which 35.6% (16/45) were attributed to pandemic-related strain. In Pathway 2, the NLP-based algorithm flagged 110 safety reports for manual review from 12,450 eligible reports. Of these, 50 reports had diagnostic error/delay (PPV = 45.5%); 94.0% (47/50) were related to strain. Errors from all eight categories of the taxonomy were found on analysis. CONCLUSION An event reporting-based strategy including use of simple-NLP-identified COVID-19-related diagnostic errors/delays uncovered several safety concerns related to COVID-19. An NLP-based approach can complement traditional reporting and be used as a just-in-time monitoring system to enable early detection of emerging risks from large volumes of safety reports.
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Li Z, Zhong Q, Yang J, Duan Y, Wang W, Wu C, He K. DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications. Bioinformatics 2021; 38:1477-1479. [PMID: 34788369 PMCID: PMC8689937 DOI: 10.1093/bioinformatics/btab767] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/11/2021] [Accepted: 11/01/2021] [Indexed: 01/05/2023] Open
Abstract
SUMMARY DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research. To improve the performance of DeepKG, a cascaded hybrid information extraction framework is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144 900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7980 entities and 43 760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform. AVAILABILITY AND IMPLEMENTATION All the results are publicly available at the website (http://covidkg.ai/). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zongren Li
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing 100039, China,Medical Artificial Intelligence Research Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Qin Zhong
- The Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing 100039, China
| | - Jing Yang
- The Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing 100039, China
| | - Yongjie Duan
- The Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing 100039, China
| | - Wenjun Wang
- Bio-engineering Research Center, Chinese PLA General Hospital, Beijing 100039, China
| | - Chengkun Wu
- State Key Laboratory of High-Performance Computing, School of Computer Science, National University of Defense Technology, Hunan, Changsha, 410073, China,To whom correspondence should be addressed. E-mail: or
| | - Kunlun He
- Medical Big Data Research Center, Chinese PLA General Hospital, Beijing 100039, China,To whom correspondence should be addressed. E-mail: or
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Durrani MIA, Naz T, Atif M, Khalid N, Amelio A. A Semantic-Based Framework for Verbal Autopsy to Identify the Cause of Maternal Death. Appl Clin Inform 2021; 12:910-923. [PMID: 34553359 PMCID: PMC8458039 DOI: 10.1055/s-0041-1735180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/17/2021] [Indexed: 10/20/2022] Open
Abstract
OBJECTIVE Verbal autopsy is a technique used to collect information about a decedent from his/her family members using questionnaires, conducting interviews, making observations, and sampling. In substantial parts of the world, particularly in Africa and Asia, many deaths are unrecorded. In 2017, globally pregnant women were dying daily around 810 and 295,000 in a year because of pregnancy-related problems, pointed out by World Health Organization. Identifying the cause of a death is a complex process which requires in-depth medical knowledge and practical experience. Generally, medical practitioners possess different knowledge levels, set of abilities, and problem-solving skills. Additionally, the medical negligence plays a significant part in further worsening the situation. Accurate identification of the cause of death can help a government to take strategic measures to focus on, particularly increasing the death rate in a specific region. METHODS This research provides a solution by introducing a semantic-based verbal autopsy framework for maternal death (SVAF-MD) to identify the cause of death. The proposed framework consists of four main components as follows: (1) clinical practice guidelines, (2) knowledge collection, (3) knowledge modeling, and (4) knowledge codification. Maternal ontology for the framework is developed using Protégé knowledge editor. Resource description framework application programming interface (API) for PHP (RAP) is used as a Semantic Web toolkit along with Simple Protocol and RDF Query Language (SPARQL) is used for querying with ontology to retrieve data. RESULTS The results show that 92% of maternal causes of deaths assigned using SVAF-MD correctly matched manual reports already prepared by gynecologists. CONCLUSION SVAF-MD, a semantic-based framework for the verbal autopsy of maternal deaths, assigns the cause of death with minimum involvement of medical practitioners. This research helps the government to ease down the verbal autopsy process, overcome the delays in reporting, and facilitate in terms of accurate results to devise the policies to reduce the maternal mortality.
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Affiliation(s)
| | - Tabbasum Naz
- Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan
| | - Muhammad Atif
- Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan
| | - Numra Khalid
- Department of Computer Science & IT, The University of Lahore, Lahore, Pakistan
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