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Baum L, Johns M, Müller A, Abu Attieh H, Prasser F. HERALD: A domain-specific query language for longitudinal health data analytics. Int J Med Inform 2024; 192:105646. [PMID: 39393126 DOI: 10.1016/j.ijmedinf.2024.105646] [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: 06/02/2024] [Revised: 10/02/2024] [Accepted: 10/04/2024] [Indexed: 10/13/2024]
Abstract
BACKGROUND Large-scale health data has significant potential for research and innovation, especially with longitudinal data offering insights into prevention, disease progression, and treatment effects. Yet, analyzing this data type is complex, as data points are repeatedly documented along the timeline. As a consequence, extracting cross-sectional tabular data suitable for statistical analysis and machine learning can be challenging for medical researchers and data scientists alike, with existing tools lacking balance between ease-of-use and comprehensiveness. OBJECTIVE This paper introduces HERALD, a novel domain-specific query language designed to support the transformation of longitudinal health data into cross-sectional tables. We describe the basic concepts, the query syntax, a graphical user interface for constructing and executing HERALD queries, as well as an integration into Informatics for Integrating Biology and the Bedside (i2b2). METHODS The syntax of HERALD mimics natural language and supports different query types for selection, aggregation, analysis of relationships, and searching for data points based on filter expressions and temporal constraints. Using a hierarchical concept model, queries are executed individually for the data of each patient, while constructing tabular output. HERALD is closed, meaning that queries process data points and generate data points. Queries can refer to data points that have been produced by previous queries, providing a simple, but powerful nesting mechanism. RESULTS The open-source implementation consists of a HERALD query parser, an execution engine, as well as a web-based user interface for query construction and statistical analysis. The implementation can be deployed as a standalone component and integrated into self-service data analytics environments like i2b2 as a plugin. HERALD can be valuable tool for data scientists and machine learning experts, as it simplifies the process of transforming longitudinal health data into tables and data matrices. CONCLUSION The construction of cross-sectional tables from longitudinal data can be supported through dedicated query languages that strike a reasonable balance between language complexity and transformation capabilities.
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Affiliation(s)
- Lena Baum
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany.
| | - Marco Johns
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany
| | - Armin Müller
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany
| | - Hammam Abu Attieh
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany
| | - Fabian Prasser
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Center of Health Data Science, Berlin, Germany
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Lanzola G, Polce F, Parimbelli E, Gabetta M, Cornet R, de Groot R, Kogan A, Glasspool D, Wilk S, Quaglini S. The Case Manager: An Agent Controlling the Activation of Knowledge Sources in a FHIR-Based Distributed Reasoning Environment. Appl Clin Inform 2023; 14:725-734. [PMID: 37339683 PMCID: PMC10499504 DOI: 10.1055/a-2113-4443] [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: 11/15/2022] [Accepted: 05/12/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines. OBJECTIVES As in many multi-agent systems we needed to coordinate the activities of all agents involved. Moreover, since the agents share a common blackboard where all patients' data are stored, we also needed to implement a mechanism for the prompt notification of each agent upon addition of new information potentially triggering its activation. METHODS The communication needs have been investigated and modeled using the HL7-FHIR (Health Level 7-Fast Healthcare Interoperability Resources) standard to ensure proper semantic interoperability among agents. Then a syntax rooted in the FHIR search framework has been defined for representing the conditions to be monitored on the system blackboard for activating each agent. RESULTS The Case Manager (CM) has been implemented as a dedicated component playing the role of an orchestrator directing the behavior of all agents involved. Agents dynamically inform the CM about the conditions to be monitored on the blackboard, using the syntax we developed. The CM then notifies each agent whenever any condition of interest occurs. The functionalities of the CM and other actors have been validated using simulated scenarios mimicking the ones that will be faced during pilot studies and in production. CONCLUSION The CM proved to be a key facilitator for properly achieving the required behavior of our multi-agent system. The proposed architecture may also be leveraged in many clinical contexts for integrating separate legacy services, turning them into a consistent telemedicine framework and enabling application reusability.
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Affiliation(s)
- Giordano Lanzola
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Francesca Polce
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Enea Parimbelli
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Matteo Gabetta
- Research and Development Division, Biomeris S.r.l, Pavia, Italy
| | - Ronald Cornet
- Medical Informatics, Amsterdam Public Health Institute, Methodology & Digital Health, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Rowdy de Groot
- Medical Informatics, Amsterdam Public Health Institute, Methodology & Digital Health, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Alexandra Kogan
- Department of Information Systems, University of Haifa, Haifa, Israel
| | | | - Szymon Wilk
- Research and Development Division, Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Silvana Quaglini
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
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Grout RW, Buchhalter J, Patel AD, Brin A, Clark AA, Holmay M, Story TJ, Downs SM. Improving Patient-Centered Communication about Sudden Unexpected Death in Epilepsy through Computerized Clinical Decision Support. Appl Clin Inform 2021; 12:90-99. [PMID: 33598905 DOI: 10.1055/s-0040-1722221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
BACKGROUND Sudden unexpected death in epilepsy (SUDEP) is a rare but fatal risk that patients, parents, and professional societies clearly recommend discussing with patients and families. However, this conversation does not routinely happen. OBJECTIVES This pilot study aimed to demonstrate whether computerized decision support could increase patient communication about SUDEP. METHODS A prospective before-and-after study of the effect of computerized decision support on delivery of SUDEP counseling. The intervention was a screening, alerting, education, and follow-up SUDEP module for an existing computerized decision support system (the Child Health Improvement through Computer Automation [CHICA]) in five urban pediatric primary care clinics. Families of children with epilepsy were contacted by telephone before and after implementation to assess if the clinician discussed SUDEP at their respective encounters. RESULTS The CHICA-SUDEP module screened 7,154 children age 0 to 21 years for seizures over 7 months; 108 (1.5%) reported epilepsy. We interviewed 101 families after primary care encounters (75 before and 26 after implementation) over 9 months. After starting CHICA-SUDEP, the number of caregivers who reported discussing SUDEP with their child's clinician more than doubled from 21% (16/75) to 46% (12/26; p = 0.03), and when the parent recalled who brought up the topic, 80% of the time it was the clinician. The differences between timing and sampling methodologies of before and after intervention cohorts could have led to potential sampling and recall bias. CONCLUSION Clinician-family discussions about SUDEP significantly increased in pediatric primary care clinics after introducing a systematic, computerized screening and decision support module. These tools demonstrate potential for increasing patient-centered education about SUDEP, as well as incorporating other guideline-recommended algorithms into primary and subspecialty cares. CLINICAL TRIAL REGISTRATION clinicaltrials.gov, NCT03502759.
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Affiliation(s)
- Randall W Grout
- Department of Pediatrics, Children's Health Services Research, Indiana University, Indianapolis, Indiana, United States.,Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, United States
| | - Jeffrey Buchhalter
- Department of Pediatrics, University of Calgary, Section of Neurology, Alberta Children's Hospital, Calgary, Canada
| | - Anup D Patel
- Division of Neurology, Nationwide Children's Hospital and Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio, United States
| | - Amy Brin
- Child Neurology Foundation, Minneapolis, Minnesota, United States
| | - Ann A Clark
- Department of Pediatrics, Children's Health Services Research, Indiana University, Indianapolis, Indiana, United States
| | - Mary Holmay
- Greenwich Biosciences, Carlsbad, California, United States (at the time of this study)
| | - Tyler J Story
- Greenwich Biosciences, Carlsbad, California, United States (at the time of this study).,UCB, Inc., Smyrna, Georgia, United States
| | - Stephen M Downs
- Department of Pediatrics, Children's Health Services Research, Indiana University, Indianapolis, Indiana, United States.,Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, United States
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Kraus S, Toddenroth D, Staudigel M, Rödle W, Unberath P, Griebel L, Prokosch HU, Mate S. Mapping the Entire Record-An Alternative Approach to Data Access from Medical Logic Modules. Appl Clin Inform 2020; 11:342-349. [PMID: 32403139 DOI: 10.1055/s-0040-1709708] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
OBJECTIVES This study aimed to describe an alternative approach for accessing electronic medical records (EMRs) from clinical decision support (CDS) functions based on Arden Syntax Medical Logic Modules, which can be paraphrased as "map the entire record." METHODS Based on an experimental Arden Syntax processor, we implemented a method to transform patient data from a commercial patient data management system (PDMS) to tree-structured documents termed CDS EMRs. They are encoded in a specific XML format that can be directly transformed to Arden Syntax data types by a mapper natively integrated into the processor. The internal structure of a CDS EMR reflects the tabbed view of an EMR in the graphical user interface of the PDMS. RESULTS The study resulted in an architecture that provides CDS EMRs in the form of a network service. The approach enables uniform data access from all Medical Logic Modules and requires no mapping parameters except a case number. Measurements within a CDS EMR can be addressed with straightforward path expressions. The approach is in routine use at a German university hospital for more than 2 years. CONCLUSION This practical approach facilitates the use of CDS functions in the clinical routine at our local hospital. It is transferrable to standard-compliant Arden Syntax processors with moderate effort. Its comprehensibility can also facilitate teaching and development. Moreover, it may lower the entry barrier for the application of the Arden Syntax standard and could therefore promote its dissemination.
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Affiliation(s)
- Stefan Kraus
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dennis Toddenroth
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Martin Staudigel
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Wolfgang Rödle
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Philipp Unberath
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Lena Griebel
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Sebastian Mate
- Medical Centre for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany
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Comprehensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines. Artif Intell Med 2020; 103:101741. [PMID: 31928849 DOI: 10.1016/j.artmed.2019.101741] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 10/02/2019] [Accepted: 10/04/2019] [Indexed: 12/29/2022]
Abstract
BACKGROUND The over-use of antibiotics in clinical domains is causing an alarming increase in bacterial resistance, thus endangering their effectiveness as regards the treatment of highly recurring severe infectious diseases. Whilst Clinical Guidelines (CGs) focus on the correct prescription of antibiotics in a narrative form, Clinical Decision Support Systems (CDSS) operationalize the knowledge contained in CGs in the form of rules at the point of care. Despite the efforts made to computerize CGs, there is still a gap between CGs and the myriad of rule technologies (based on different logic formalisms) that are available to implement CDSSs in real clinical settings. OBJECTIVE To helpCDSS designers to determine the most suitable rule-based technology (medical-oriented rules, production rules and semantic web rules) with which to model knowledge from CGs for the prescription of antibiotics. We propose a framework of criteria for this purpose that is extensible to more generic CGs. MATERIALS AND METHODS Our proposal is based on the identification of core technical requirements extracted from both literature and the analysis of CGs for antibiotics, establishing three dimensions for analysis: language expressivity, interoperability and industrial aspects. We present a case study regarding the John Hopkins Hospital (JHH) Antibiotic Guidelines for Urinary Tract Infection (UTI), a highly recurring hospital acquired infection. We have adopted our framework of criteria in order to analyse and implement these CGs using various rule technologies: HL7 Arden Syntax, general-purpose Production Rules System (Drools), HL7 standard Rule Interchange Format (RIF), Semantic Web Rule Language (SWRL) and SParql Inference Notation (SPIN) rule extensions (implementing our own ontology for UTI). RESULTS We have identified the main criteria required to attain a maintainable and cost-affordable computable knowledge representation for CGs. We have represented the JHH UTI CGs knowledge in a total of 12 Arden Syntax MLMs, 81 Drools rules and 154 ontology classes, properties and individuals. Our experiments confirm the relevance of the proposed set of criteria and show the level of compliance of the different rule technologies with the JHH UTI CGs knowledge representation. CONCLUSIONS The proposed framework of criteria may help clinical institutions to select the most suitable rule technology for the representation of CGs in general, and for the antibiotic prescription domain in particular, depicting the main aspects that lead to Computer Interpretable Guidelines (CIGs), such as Logic expressivity (Open/Closed World Assumption, Negation-As-Failure), Temporal Reasoning and Interoperability with existing HIS and clinical workflow. Future work will focus on providing clinicians with suggestions regarding new potential steps for CGs, considering process mining approaches and CGs Process Workflows, the use of HL7 FHIR for HIS interoperability and the representation of Knowledge-as- a-Service (KaaS).
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