Electronic Health Records Aggregators (EHR_agg )

Abstract

Background Integration of health data systems is an open problem. Most of the active initiatives are based on the use of standards. However, achieving a widely and generalized compliment of such standards still seems a costly task that will take a long time to be completed. Even more, most of the standards are proposed for a specific use, without integrating other needs.

Objectives We propose an alternative to get a unified view of health-related data, valid for several uses, that unites heterogeneous data sources.

Methods Our proposal integrates developments made so far to automatically learn how to extract and convert data from different health-related systems. It enables the creation of a single multipurpose point of access.

Results We present the EhR_agg notion and its related concepts. EHR_agg is defined as a middleware that, following the FAIR principles, integrates health data sources offering a unified view over them.

Keywords

health data - interoperability - FAIR principles - text mining

• References

• 1 World Health Organization. International Classification of Functioning, Disability and Health (ICF). Accessed June 8, 2020 at: https://www.who.int/classifications/icf/en/
• 2 World Health Organization. ICD-10: International Statistical Classification of Diseases and Related Health Problems (10th revision, 2nd ed.). Accessed June 8, 2020 at: https://apps.who.int/iris/handle/10665/42980
• 3 World Health Organization. International Classification of Health Interventions (ICHI). Accessed June 8, 2020 at: https://www.who.int/classifications/ichi/en/
• 4 Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004; 32 (Database issue): D267-D270
• 5 HL7 International. Standard HL7. Accessed June 8, 2020 at: http://www.HL7.org
• 6 Open-EHR Open Electronical Health Rercords. Accessed June 8, 2020 at: http://www.openehr.org
• 7 ISO-13606. ISO 13606:2019: Health informatics—Electronic health record communication. Accessed June 8, 2020 at: https://www.iso.org/standard/67868.html
• 8 Lozano-Rubí R, Muñoz Carrero A, Serrano Balazote P, Pastor X, Onto CR. OntoCR: a CEN/ISO-13606 clinical repository based on ontologies. J Biomed Inform 2016; 60: 224-233
• 9 Xu L, Monika S, Bojana K. et al. What clinics are expecting from data scientists? A review on data oriented studies through qualitative and quantitative approaches. IEEE Access 2019; 7: 641-654
• 10 Health EU. Health EU—human avatars to prevent and cure diseases. Accessed June 8, 2020 at: https://www.health-eu.eu/
• 11 European Union. Future health. Accessed June 8, 2020 at: https://www.futurehealtheurope.eu/
• 12 National Institutes of Health (USA). NIH strategic plan for data science Accessed June 8, 2020 at: https://datascience.nih.gov
• 13 Oliveira JL, Trifan A, Bastião Silva LA. EMIF catalogue: a collaborative platform for sharing and reusing biomedical data. Int J Med Inform 2019; 126: 35-45
• 14 Margolis R, Derr L, Dunn M. et al. The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data. J Am Med Inform Assoc 2014; 21 (06) 957-958
• 15 Schulz S, Stegwee R, Chronaki C. Standards in Healthcare Data. In: Kubben Pieter, Dumontier Michel, Dekker Andre. , eds. Fundamentals of Clinical Data Science. Cham: Springer International Publishing; 2019: 19-36
• 16 Sathyanarayana A, Srivastava J, Fernandez-Luque L. The science of sweet dreams: predicting sleep efficiency from wearable device data. Computer 2017; 50: 30-38
• 17 Julie V, Zhi Q, Zhang W. Smart shoes with adaptive sampling for outpatient daily health monitoring. Paper presented at: Design of Medical Devices Conference; April 15–18, 2019 Minneapolis, MN:
• 18 Guo HW, Huang YS, Chien JC, Shieh JS. Short-term analysis of heart rate variability for emotion recognition via a wearable ECG device. Paper presented at: International Conference on Intelligent Informatics and Biomedical Sciences; 2015 Okinawa:
• 19 Wilkinson MD, Dumontier M, Aalbersberg IJ. et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data 2016; 3: 160018
• 20 Martínez-Costa C, Menárguez-Tortosa M, Fernández-Breis JT. An approach for the semantic interoperability of ISO EN 13606 and Open EHR archetypes. J Biomed Inform 2010; 43 (05) 736-746
• 21 Legaz-García MdelC, Menárguez-Tortosa M, Fernández-Breis JT, Chute CG, Tao C. Transformation of standardized clinical models based on OWL technologies: from CEM to OpenEHR archetypes. J Am Med Inform Assoc 2015; 22 (03) 536-544
• 22 OpenCEM. Open Clinical Element Models (OpenCEM). Accessed July, 2019 at: http://www.opencem.org/
• 23 Michael S, Ayush S, Lu Z. Text mining for precision medicine: bringing structure to EHRs and biomedical literature to understand genes and health. In: Shen B, Tang H, Jiang X. , eds. Translational Biomedical Informatics: A Precision Medicine Perspective. Singapore: Springer Singapore; 2016: 139-166
• 24 Rajvir K. A Comparative Analysis of Selected Set of Natural Language Processing (NLP) and Machine Learning (ML) Algorithms for Clinical Coding Using Clinical Classification Standards. [Ph.D. dissertation]. Sydney: Western Sydney University; 2018
• 25 Chen L, Song L, Shao Y, Li D, Ding K. Using natural language processing to extract clinically useful information from Chinese electronic medical records. Int J Med Inform 2019; 124: 6-12
• 26 Clinical Data Interchange Standards Consortium. Therapeutic area (TA) standards. Accessed June 8, 2020 at: https://www.cdisc.org/standards/therapeutic-areas
• 27 Dreisbach C, Koleck TA, Bourne PE, Bakken S. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. Int J Med Inform 2019; 125: 37-46