The Expert Survey-Based Global Ranking of Management- and Clinical-Centered Health Informatics and IT Journals

The goal of this study is to develop an expert survey-based journal ranking for the Health Informatics & Information Technology (HIIT) field. Journal of the American Medical Informatics Association and Journal of Medical Internet Research were ranked as top HIIT management-focused journals, and BMC Medical Informatics & Decision Making and IEEE Journal of Biomedical & Health Informatics were ranked as top HIIT clinical-focused journals. This ranking benefits academics who conduct research in this field because it allows them to direct their research to appropriate journals, convey their accomplishments to tenure and promotion committees, and experience other benefits

Global Ranking of Management- and Clinical-centered E-health Journals

This study presents a ranking list of 35 management- and 28 clinical-centered e-health academic journals developed based on a survey of 398 active researchers from 46 countries. Among the management-centered journals, the researchers ranked Journal of the American Medical Informatics Association and Journal of Medical Internet Research as A+ journals; among the clinical-focused journals, they ranked BMC Medical Informatics and Decision Making and IEEE Journal of Biomedical and Health Informatics as A+ journals. We found that journal longevity (years in print) had an effect on ranking scores such that longer standing journals had an advantage over their more recent counterparts, but this effect was only moderately significant and did not guarantee a favorable ranking position. Various stakeholders may use this list to advance the state of the e-health discipline. There are both similarities and differences between the present ranking and the one developed earlier in 2010

Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts

Introduction The secondary use of Electronic Healthcare Records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, Virtual Health Records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. Our main objective is to develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. Materials and Methods We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (i.e., data level) and the rest using ontologies (i.e., knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. Results We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data has been used and the patients have been successfully classified by the risk of developing colorectal cancer. Conclusion This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies and classification rules can be designed.This work was supported by the Ministerio de Economia y Competitividad and the FEDER program through grants TIN2010-21388-C01 and TIN2010-21388-C02. MCLG was supported by the Fundacion Seneca through grant 15555/FPI/2010.Fernández-Breis, JT.; Maldonado Segura, JA.; Marcos, M.; Legaz-García, MDC.; Moner Cano, D.; Torres-Sospedra, J.; Esteban-Gil, A.... (2013). Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts. Journal of the American Medical Informatics Association. 20(E2):288-296. https://doi.org/10.1136/amiajnl-2013-001923S28829620E2Cuggia, M., Besana, P., & Glasspool, D. (2011). Comparing semi-automatic systems for recruitment of patients to clinical trials. International Journal of Medical Informatics, 80(6), 371-388. doi:10.1016/j.ijmedinf.2011.02.003Sujansky, W. (2001). Heterogeneous Database Integration in Biomedicine. Journal of Biomedical Informatics, 34(4), 285-298. doi:10.1006/jbin.2001.1024Schadow G Russler DC Mead CN . Integrating medical information and knowledge in the HL7 RIM. Proceedings of the AMIA Symposium, 2000:764–8.Johnson PD Tu SW Musen MA . A virtual medical record for guideline-based decision support. Proceedings of the AMIA 2001 Annual Symposium, 294–8.German, E., Leibowitz, A., & Shahar, Y. (2009). An architecture for linking medical decision-support applications to clinical databases and its evaluation. Journal of Biomedical Informatics, 42(2), 203-218. doi:10.1016/j.jbi.2008.10.007Peleg, M., Keren, S., & Denekamp, Y. (2008). Mapping computerized clinical guidelines to electronic medical records: Knowledge-data ontological mapper (KDOM). Journal of Biomedical Informatics, 41(1), 180-201. doi:10.1016/j.jbi.2007.05.003Maldonado, J. A., Costa, C. M., Moner, D., Menárguez-Tortosa, M., Boscá, D., Miñarro Giménez, J. A., … Robles, M. (2012). Using the ResearchEHR platform to facilitate the practical application of the EHR standards. Journal of Biomedical Informatics, 45(4), 746-762. doi:10.1016/j.jbi.2011.11.004Parker CG Rocha RA Campbell JR . Detailed clinical models for sharable, executable guidelines. Stud Health Technol Inform 2004;107:145–8.Clinical Information Modeling Initiative. http://informatics.mayo.edu/CIMI/index.php/Main_Page (accessed Jun 2013).W3C, OWL2 Web Ontology Language. http://www.w3.org/TR/owl2-overview/ (accessed Jun 2013).European Commission. Semantic interoperability for better health and safer healthcare. Deployment and research roadmap for Europe. ISBN-13: 978-92-79-11139-6, 2009.SemanticHealthNet. http://www.semantichealthnet.eu/ (accessed Jun 2013).Martínez-Costa, C., Menárguez-Tortosa, M., Fernández-Breis, J. T., & Maldonado, J. A. (2009). A model-driven approach for representing clinical archetypes for Semantic Web environments. Journal of Biomedical Informatics, 42(1), 150-164. doi:10.1016/j.jbi.2008.05.005Iqbal AM . An OWL-DL ontology for the HL7 reference information model. Toward useful services for elderly and people with disabilities Berlin: Springer, 2011:168–75.Tao, C., Jiang, G., Oniki, T. A., Freimuth, R. R., Zhu, Q., Sharma, D., … Chute, C. G. (2012). A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data. Journal of the American Medical Informatics Association, 20(3), 554-562. doi:10.1136/amiajnl-2012-001326Heymans, S., McKennirey, M., & Phillips, J. (2011). Semantic validation of the use of SNOMED CT in HL7 clinical documents. Journal of Biomedical Semantics, 2(1), 2. doi:10.1186/2041-1480-2-2Menárguez-Tortosa, M., & Fernández-Breis, J. T. (2013). OWL-based reasoning methods for validating archetypes. Journal of Biomedical Informatics, 46(2), 304-317. doi:10.1016/j.jbi.2012.11.009Lezcano, L., Sicilia, M.-A., & Rodríguez-Solano, C. (2011). Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules. Journal of Biomedical Informatics, 44(2), 343-353. doi:10.1016/j.jbi.2010.11.005Tao C Wongsuphasawat K Clark K . Towards event sequence representation, reasoning and visualization for EHR data. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI'12). New York, NY, USA: ACM:801–6.Bodenreider O . Biomedical ontologies in action: role in knowledge management, data integration and decision support. IMIA Yearbook of Medical Informatics 2008;67–79.Beale T . Archetypes. Constraint-based domain models for future-proof information systems. http://www.openehr.org/files/publications/archetypes/archetypes_beale_web_2000.pdfSNOMED-CT. http://www.ihtsdo.org/snomed-ct/ (accessed Jun 2013).UMLS Terminology Services. https://uts.nlm.nih.gov/home.html (accessed Jun 2013).The openEHR Foundation, openEHR Clinical Knowledge Manager. http://www.openehr.org/knowledge/ (accessed Jun 2013).Maldonado, J. A., Moner, D., Boscá, D., Fernández-Breis, J. T., Angulo, C., & Robles, M. (2009). LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics. International Journal of Medical Informatics, 78(8), 559-570. doi:10.1016/j.ijmedinf.2009.03.006SAXON XSLT and XQuery processor. http://saxon.sourceforge.net/ (accessed Jun 2013).NCBO Bioportal. http://bioportal.bioontology.org/ (accessed Jun 2013).The Protégé Ontology Editor and Knowledge Acquisition System. http://protege.stanford.edu/ (accessed Jun 2013).Semantic Web Integration Tool. http://sele.inf.um.es/swit (accessed Jun 2013).Hermit Reasoner. http://www.hermit-reasoner.com/ (accessed Jun 2013).The OWLAPI. http://owlapi.sourceforge.net/ (accessed Jun 2013).Institute for Health Metrics and Evaluation. Global Burden of Disease. http://www.healthmetricsandevaluation.org/gbd (accessed Jun 2013).Segnan N Patnick J von Karsa L . European guidelines for quality assurance in colorectal cancer screening and diagnosis 2010. First Edition. European Union. ISBN 978-92-79-16435-4.W3C. XQuery 1.0: An XML Query Language. http://www.w3.org/TR/xquery/ (accessed Jun 2013).DL Query. http://protegewiki.stanford.edu/wiki/DL_Query (accessed Jun 2013).SPARQL Query Language for RDF. http://www.w3.org/TR/rdf-sparql-query/ (accessed Jun 2013).Semantic Web Rule Language. http://www.w3.org/Submission/SWRL/ (accessed Jun 2013).Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Boscá, D., & Robles, M. (2013). Interoperability of clinical decision-support systems and electronic health records using archetypes: A case study in clinical trial eligibility. Journal of Biomedical Informatics, 46(4), 676-689. doi:10.1016/j.jbi.2013.05.004Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Moner, D., Boscá, D., & Robles, M. (2011). An Archetype-Based Solution for the Interoperability of Computerised Guidelines and Electronic Health Records. Lecture Notes in Computer Science, 276-285. doi:10.1007/978-3-642-22218-4_35MobiGuide: Guiding patients anytime everywhere. http://www.mobiguide-project.eu/ (accessed Jun 2013).EURECA: Enabling information re-Use by linking clinical RE search and Care. http://eurecaproject.eu/ (accessed Jun 2013).Rea, S., Pathak, J., Savova, G., Oniki, T. A., Westberg, L., Beebe, C. E., … Chute, C. G. (2012). Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project. Journal of Biomedical Informatics, 45(4), 763-771. doi:10.1016/j.jbi.2012.01.009Clinical Element Models. http://informatics.mayo.edu/sharp/index.php/CEMS (accessed Jun 2013)

Launching the "Journal of Biomedical Discovery and Collaboration"

The Journal of Biomedical Discovery and Collaboration was created to provide, for the first time, a unified forum to consider all factors that affect scientific practice and scientific discovery – with an emphasis on the changing face of contemporary biomedical science. In this endeavor we are bringing together three different groups of scholars: a) laboratory investigators, who make the discoveries that are the currency of the scientific enterprise; b) computer science and informatics investigators, who devise tools for data analysis, mining, visualization and integration; and c) social scientists, including sociologists, historians, and philosophers, who study scientific practice, collaboration, and information needs. We will publish original research articles, case studies, focus pieces, reviews, and software articles. All articles in the Journal of Biomedical Discovery and Collaboration will be peer reviewed, published immediately upon acceptance, freely available online via open access, and archived in PubMed Central and other international full-text repositories

Selected papers from the 15th and 16th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics

Funding Information: CIBB 2019 was held at the Department of Human and Social Sciences of the University of Bergamo, Italy, from the 4th to the 6th of September 2019 []. The organization of this edition of CIBB was supported by the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy, and by the Institute of Biomedical Technologies of the National Research Council, Italy. Besides the papers focused on computational intelligence methods applied to open problems of bioinformatics and biostatistics, the works submitted to CIBB 2019 dealt with algebraic and computational methods to study RNA behaviour, intelligence methods for molecular characterization and dynamics in translational medicine, modeling and simulation methods for computational biology and systems medicine, and machine learning in healthcare informatics and medical biology. A supplement published in BMC Medical Informatics and Decision Making journal [] collected three revised and extended papers focused on the latter topic.publishersversionpublishe

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

[EN] In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?Gatta, R.; Vallati, M.; Fernández Llatas, C.; Martinez-Millana, A.; Orini, S.; Sacchi, L.; Lenkowicz, J.... (2020). What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper. International Journal of Environmental research and Public Health (Online). 17(18):1-19. https://doi.org/10.3390/ijerph17186616S1191718Guyatt, G. (1992). Evidence-Based Medicine. JAMA, 268(17), 2420. doi:10.1001/jama.1992.03490170092032Hripcsak, G., Ludemann, P., Pryor, T. A., Wigertz, O. B., & Clayton, P. D. (1994). Rationale for the Arden Syntax. Computers and Biomedical Research, 27(4), 291-324. doi:10.1006/cbmr.1994.1023Peleg, M. (2013). Computer-interpretable clinical guidelines: A methodological review. Journal of Biomedical Informatics, 46(4), 744-763. doi:10.1016/j.jbi.2013.06.009Van de Velde, S., Heselmans, A., Delvaux, N., Brandt, L., Marco-Ruiz, L., Spitaels, D., … Flottorp, S. (2018). A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implementation Science, 13(1). doi:10.1186/s13012-018-0790-1Rawson, T. M., Moore, L. S. P., Hernandez, B., Charani, E., Castro-Sanchez, E., Herrero, P., … Holmes, A. H. (2017). A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately? Clinical Microbiology and Infection, 23(8), 524-532. doi:10.1016/j.cmi.2017.02.028Greenes, R. A., Bates, D. W., Kawamoto, K., Middleton, B., Osheroff, J., & Shahar, Y. (2018). Clinical decision support models and frameworks: Seeking to address research issues underlying implementation successes and failures. Journal of Biomedical Informatics, 78, 134-143. doi:10.1016/j.jbi.2017.12.005Garcia, C. dos S., Meincheim, A., Faria Junior, E. R., Dallagassa, M. R., Sato, D. M. V., Carvalho, D. R., … Scalabrin, E. E. (2019). Process mining techniques and applications – A systematic mapping study. Expert Systems with Applications, 133, 260-295. doi:10.1016/j.eswa.2019.05.003Bhargava, A., Kim, T., Quine, D. B., & Hauser, R. G. (2019). A 20-Year Evaluation of LOINC in the United States’ Largest Integrated Health System. Archives of Pathology & Laboratory Medicine, 144(4), 478-484. doi:10.5858/arpa.2019-0055-oaLee, D., de Keizer, N., Lau, F., & Cornet, R. (2014). Literature review of SNOMED CT use. Journal of the American Medical Informatics Association, 21(e1), e11-e19. doi:10.1136/amiajnl-2013-001636TROTTI, A., COLEVAS, A., SETSER, A., RUSCH, V., JAQUES, D., BUDACH, V., … COLEMAN, C. (2003). CTCAE v3.0: development of a comprehensive grading system for the adverse effects of cancer treatment. Seminars in Radiation Oncology, 13(3), 176-181. doi:10.1016/s1053-4296(03)00031-6Daniel, C., & Kalra, D. (2019). Clinical Research Informatics: Contributions from 2018. Yearbook of Medical Informatics, 28(01), 203-205. doi:10.1055/s-0039-1677921Marco-Ruiz, L., Pedrinaci, C., Maldonado, J. A., Panziera, L., Chen, R., & Bellika, J. G. (2016). Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach. Journal of Biomedical Informatics, 62, 243-264. doi:10.1016/j.jbi.2016.07.011Marcos, C., González-Ferrer, A., Peleg, M., & Cavero, C. (2015). Solving the interoperability challenge of a distributed complex patient guidance system: a data integrator based on HL7’s Virtual Medical Record standard. Journal of the American Medical Informatics Association, 22(3), 587-599. doi:10.1093/jamia/ocv003Wulff, A., Haarbrandt, B., Tute, E., Marschollek, M., Beerbaum, P., & Jack, T. (2018). An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR. Artificial Intelligence in Medicine, 89, 10-23. doi:10.1016/j.artmed.2018.04.012Chen, C., Chen, K., Hsu, C.-Y., Chiu, W.-T., & Li, Y.-C. (Jack). (2010). A guideline-based decision support for pharmacological treatment can improve the quality of hyperlipidemia management. Computer Methods and Programs in Biomedicine, 97(3), 280-285. doi:10.1016/j.cmpb.2009.12.004Anani, N., Mazya, M. V., Chen, R., Prazeres Moreira, T., Bill, O., Ahmed, N., … Koch, S. (2017). Applying openEHR’s Guideline Definition Language to the SITS international stroke treatment registry: a European retrospective observational study. BMC Medical Informatics and Decision Making, 17(1). doi:10.1186/s12911-016-0401-5Eddy, D. M. (1982). Clinical Policies and the Quality of Clinical Practice. New England Journal of Medicine, 307(6), 343-347. doi:10.1056/nejm198208053070604Guyatt, G. H. (1990). Then-of-1 Randomized Controlled Trial: Clinical Usefulness. Annals of Internal Medicine, 112(4), 293. doi:10.7326/0003-4819-112-4-293CHALMERS, I. (1993). The Cochrane Collaboration: Preparing, Maintaining, and Disseminating Systematic Reviews of the Effects of Health Care. Annals of the New York Academy of Sciences, 703(1 Doing More Go), 156-165. doi:10.1111/j.1749-6632.1993.tb26345.xWoolf, S. H., Grol, R., Hutchinson, A., Eccles, M., & Grimshaw, J. (1999). Clinical guidelines: Potential benefits, limitations, and harms of clinical guidelines. BMJ, 318(7182), 527-530. doi:10.1136/bmj.318.7182.527Grading quality of evidence and strength of recommendations. (2004). BMJ, 328(7454), 1490. doi:10.1136/bmj.328.7454.1490Guyatt, G. H., Oxman, A. D., Vist, G. E., Kunz, R., Falck-Ytter, Y., Alonso-Coello, P., & Schünemann, H. J. (2008). GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 336(7650), 924-926. doi:10.1136/bmj.39489.470347.adHill, J., Bullock, I., & Alderson, P. (2011). A Summary of the Methods That the National Clinical Guideline Centre Uses to Produce Clinical Guidelines for the National Institute for Health and Clinical Excellence. Annals of Internal Medicine, 154(11), 752. doi:10.7326/0003-4819-154-11-201106070-00007Qaseem, A. (2012). Guidelines International Network: Toward International Standards for Clinical Practice Guidelines. Annals of Internal Medicine, 156(7), 525. doi:10.7326/0003-4819-156-7-201204030-00009Rosenfeld, R. M., Nnacheta, L. C., & Corrigan, M. D. (2015). Clinical Consensus Statement Development Manual. Otolaryngology–Head and Neck Surgery, 153(2_suppl), S1-S14. doi:10.1177/0194599815601394De Boeck, K., Castellani, C., & Elborn, J. S. (2014). Medical consensus, guidelines, and position papers: A policy for the ECFS. Journal of Cystic Fibrosis, 13(5), 495-498. doi:10.1016/j.jcf.2014.06.012Clinical Practical Guidelineshttp://www.openclinical.org/guidelines.htmlHaynes, A. B., Weiser, T. G., Berry, W. R., Lipsitz, S. R., Breizat, A.-H. S., Dellinger, E. P., … Gawande, A. A. (2009). A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population. New England Journal of Medicine, 360(5), 491-499. doi:10.1056/nejmsa0810119Grigg, E. (2015). Smarter Clinical Checklists. Anesthesia & Analgesia, 121(2), 570-573. doi:10.1213/ane.0000000000000352Hales, B., Terblanche, M., Fowler, R., & Sibbald, W. (2007). Development of medical checklists for improved quality of patient care. International Journal for Quality in Health Care, 20(1), 22-30. doi:10.1093/intqhc/mzm062Greenfield, S. (2017). Clinical Practice Guidelines. JAMA, 317(6), 594. doi:10.1001/jama.2016.19969Vegting, I. L., van Beneden, M., Kramer, M. H. H., Thijs, A., Kostense, P. J., & Nanayakkara, P. W. B. (2012). How to save costs by reducing unnecessary testing: Lean thinking in clinical practice. European Journal of Internal Medicine, 23(1), 70-75. doi:10.1016/j.ejim.2011.07.003Drummond, M. (2016). Clinical Guidelines: A NICE Way to Introduce Cost-Effectiveness Considerations? Value in Health, 19(5), 525-530. doi:10.1016/j.jval.2016.04.020Prior, M., Guerin, M., & Grimmer-Somers, K. (2008). The effectiveness of clinical guideline implementation strategies - a synthesis of systematic review findings. Journal of Evaluation in Clinical Practice, 14(5), 888-897. doi:10.1111/j.1365-2753.2008.01014.xWatts, C. G., Dieng, M., Morton, R. L., Mann, G. J., Menzies, S. W., & Cust, A. E. (2014). Clinical practice guidelines for identification, screening and follow-up of individuals at high risk of primary cutaneous melanoma: a systematic review. British Journal of Dermatology, 172(1), 33-47. doi:10.1111/bjd.13403Woolf, S., Schünemann, H. J., Eccles, M. P., Grimshaw, J. M., & Shekelle, P. (2012). Developing clinical practice guidelines: types of evidence and outcomes; values and economics, synthesis, grading, and presentation and deriving recommendations. Implementation Science, 7(1). doi:10.1186/1748-5908-7-61Legido-Quigley, H., Panteli, D., Brusamento, S., Knai, C., Saliba, V., Turk, E., … Busse, R. (2012). Clinical guidelines in the European Union: Mapping the regulatory basis, development, quality control, implementation and evaluation across member states. Health Policy, 107(2-3), 146-156. doi:10.1016/j.healthpol.2012.08.004Rashidian, A., Eccles, M. P., & Russell, I. (2008). Falling on stony ground? A qualitative study of implementation of clinical guidelines’ prescribing recommendations in primary care. Health Policy, 85(2), 148-161. doi:10.1016/j.healthpol.2007.07.011Yang, W.-S., & Hwang, S.-Y. (2006). A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications, 31(1), 56-68. doi:10.1016/j.eswa.2005.09.003Kose, I., Gokturk, M., & Kilic, K. (2015). An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance. Applied Soft Computing, 36, 283-299. doi:10.1016/j.asoc.2015.07.018Pryor, T. A., Gardner, R. M., Clayton, P. D., & Warner, H. R. (1983). The HELP system. Journal of Medical Systems, 7(2), 87-102. doi:10.1007/bf00995116Shahar, Y., Miksch, S., & Johnson, P. (1998). The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artificial Intelligence in Medicine, 14(1-2), 29-51. doi:10.1016/s0933-3657(98)00015-3Boxwala, A. A., Peleg, M., Tu, S., Ogunyemi, O., Zeng, Q. T., Wang, D., … Shortliffe, E. H. (2004). GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. Journal of Biomedical Informatics, 37(3), 147-161. doi:10.1016/j.jbi.2004.04.002Terenziani, P., Molino, G., & Torchio, M. (2001). A modular approach for representing and executing clinical guidelines. Artificial Intelligence in Medicine, 23(3), 249-276. doi:10.1016/s0933-3657(01)00087-2Sutton, D. R., & Fox, J. (2003). The Syntax and Semantics of the PROformaGuideline Modeling Language. Journal of the American Medical Informatics Association, 10(5), 433-443. doi:10.1197/jamia.m1264Musen, M. A., Tu, S. W., Das, A. K., & Shahar, Y. (1996). EON: A Component-Based Approach to Automation of Protocol-Directed Therapy. Journal of the American Medical Informatics Association, 3(6), 367-388. doi:10.1136/jamia.1996.97084511Ciccarese, P., Caffi, E., Quaglini, S., & Stefanelli, M. (2005). Architectures and tools for innovative Health Information Systems: The Guide Project. International Journal of Medical Informatics, 74(7-8), 553-562. doi:10.1016/j.ijmedinf.2005.02.001Shiffman, R. N., & Greenes, R. A. (1994). Improving Clinical Guidelines with Logic and Decision-table Techniques. Medical Decision Making, 14(3), 245-254. doi:10.1177/0272989x9401400306Peleg, M., Tu, S., Bury, J., Ciccarese, P., Fox, J., Greenes, R. A., … Stefanelli, M. (2003). Comparing Computer-interpretable Guideline Models: A Case-study Approach. Journal of the American Medical Informatics Association, 10(1), 52-68. doi:10.1197/jamia.m1135Karadimas, H., Ebrahiminia, V., & Lepage, E. (2018). User-defined functions in the Arden Syntax: An extension proposal. Artificial Intelligence in Medicine, 92, 103-110. doi:10.1016/j.artmed.2015.11.003Peleg, M., Keren, S., & Denekamp, Y. (2008). Mapping computerized clinical guidelines to electronic medical records: Knowledge-data ontological mapper (KDOM). Journal of Biomedical Informatics, 41(1), 180-201. doi:10.1016/j.jbi.2007.05.003German, E., Leibowitz, A., & Shahar, Y. (2009). An architecture for linking medical decision-support applications to clinical databases and its evaluation. Journal of Biomedical Informatics, 42(2), 203-218. doi:10.1016/j.jbi.2008.10.007Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Boscá, D., & Robles, M. (2013). Interoperability of clinical decision-support systems and electronic health records using archetypes: A case study in clinical trial eligibility. Journal of Biomedical Informatics, 46(4), 676-689. doi:10.1016/j.jbi.2013.05.004Marco-Ruiz, L., Moner, D., Maldonado, J. A., Kolstrup, N., & Bellika, J. G. (2015). Archetype-based data warehouse environment to enable the reuse of electronic health record data. International Journal of Medical Informatics, 84(9), 702-714. doi:10.1016/j.ijmedinf.2015.05.016Gooch, P., & Roudsari, A. (2011). Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. Journal of the American Medical Informatics Association, 18(6), 738-748. doi:10.1136/amiajnl-2010-000033Quaglini, S., Stefanelli, M., Cavallini, A., Micieli, G., Fassino, C., & Mossa, C. (2000). Guideline-based careflow systems. Artificial Intelligence in Medicine, 20(1), 5-22. doi:10.1016/s0933-3657(00)00050-6Schadow, G., Russler, D. C., & McDonald, C. J. (2001). Conceptual alignment of electronic health record data with guideline and workflow knowledge. International Journal of Medical Informatics, 64(2-3), 259-274. doi:10.1016/s1386-5056(01)00196-4González-Ferrer, A., ten Teije, A., Fdez-Olivares, J., & Milian, K. (2013). Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks. Artificial Intelligence in Medicine, 57(2), 91-109. doi:10.1016/j.artmed.2012.08.008Shabo, A., Parimbelli, E., Quaglini, S., Napolitano, C., & Peleg, M. (2016). Interplay between Clinical Guidelines and Organizational Workflow Systems. Methods of Information in Medicine, 55(06), 488-494. doi:10.3414/me16-01-0006Mulyar, N., van der Aalst, W. M. P., & Peleg, M. (2007). A Pattern-based Analysis of Clinical Computer-interpretable Guideline Modeling Languages. Journal of the American Medical Informatics Association, 14(6), 781-787. doi:10.1197/jamia.m2389Grando, M. A., Glasspool, D., & Fox, J. (2012). A formal approach to the analysis of clinical computer-interpretable guideline modeling languages. Artificial Intelligence in Medicine, 54(1), 1-13. doi:10.1016/j.artmed.2011.07.001Kaiser, K., & Marcos, M. (2015). Leveraging workflow control patterns in the domain of clinical practice guidelines. BMC Medical Informatics and Decision Making, 16(1). doi:10.1186/s12911-016-0253-zMartínez-Salvador, B., & Marcos, M. (2016). Supporting the Refinement of Clinical Process Models to Computer-Interpretable Guideline Models. Business & Information Systems Engineering, 58(5), 355-366. doi:10.1007/s12599-016-0443-3Decision Model and Notation Version 1.0https://www.omg.org/spec/DMN/1.0Ghasemi, M., & Amyot, D. (2016). Process mining in healthcare: a systematised literature review. International Journal of Electronic Healthcare, 9(1), 60. doi:10.1504/ijeh.2016.078745Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Rovani, M., Maggi, F. M., de Leoni, M., & van der Aalst, W. M. P. (2015). Declarative process mining in healthcare. Expert Systems with Applications, 42(23), 9236-9251. doi:10.1016/j.eswa.2015.07.040Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Lenkowicz, J., Gatta, R., Masciocchi, C., Casà, C., Cellini, F., Damiani, A., … Valentini, V. (2018). Assessing the conformity to clinical guidelines in oncology. Management Decision, 56(10), 2172-2186. doi:10.1108/md-09-2017-0906Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Qu, G., Liu, Z., Cui, S., & Tang, J. (2013). Study on Self-Adaptive Clinical Pathway Decision Support System Based on Case-Based Reasoning. Frontier and Future Development of Information Technology in Medicine and Education, 969-978. doi:10.1007/978-94-007-7618-0_95Van de Velde, S., Roshanov, P., Kortteisto, T., Kunnamo, I., Aertgeerts, B., Vandvik, P. O., & Flottorp, S. (2015). Tailoring implementation strategies for evidence-based recommendations using computerised clinical decision support systems: protocol for the development of the GUIDES tools. Implementation Science, 11(1). doi:10.1186/s13012-016-0393-

Ontology Enrichment from Free-text Clinical Documents: A Comparison of Alternative Approaches

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships, as well as difficulty in updating the ontology as domain knowledge changes. Methodologies developed in the fields of Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), and Machine Learning (ML) provide techniques for automating the enrichment of ontology from free-text documents. In this dissertation, I extended these methodologies into biomedical ontology development. First, I reviewed existing methodologies and systems developed in the fields of NLP, IR, and IE, and discussed how existing methods can benefit the development of biomedical ontologies. This previously unconducted review was published in the Journal of Biomedical Informatics. Second, I compared the effectiveness of three methods from two different approaches, the symbolic (the Hearst method) and the statistical (the Church and Lin methods), using clinical free-text documents. Third, I developed a methodological framework for Ontology Learning (OL) evaluation and comparison. This framework permits evaluation of the two types of OL approaches that include three OL methods. The significance of this work is as follows: 1) The results from the comparative study showed the potential of these methods for biomedical ontology enrichment. For the two targeted domains (NCIT and RadLex), the Hearst method revealed an average of 21% and 11% new concept acceptance rates, respectively. The Lin method produced a 74% acceptance rate for NCIT; the Church method, 53%. As a result of this study (published in the Journal of Methods of Information in Medicine), many suggested candidates have been incorporated into the NCIT; 2) The evaluation framework is flexible and general enough that it can analyze the performance of ontology enrichment methods for many domains, thus expediting the process of automation and minimizing the likelihood that key concepts and relationships would be missed as domain knowledge evolves

Married 6-year olds and other diseases of data

Presented at the National data integrity conference: enabling research: new challenges & opportunities held on May 7-8, 2015 at Colorado State University, Fort Collins, Colorado. Researchers, administrators and integrity officers are encountering new challenges regarding research data and integrity. This conference aims to provide attendees with both a high level understanding of these challenges and impart practical tools and skills to deal with them. Topics will include data reproducibility, validity, privacy, security, visualization, reuse, access, preservation, rights and management.Michael Kahn is the Associate Professor of Epidemiology, Department of Pediatrics, University of Colorado, Denver, Co-Director of the Colorado Clinical and Translational Sciences Institute (CCTSI) and Director of Clinical Informatics in the Department of Quality & Patient Safety. His research interests include real-time clinical decision support linked to clinical outcomes monitoring, clinical data warehouses for both operational and retrospective research support, integration of electronic medical records with prospective research, and translational research informatics for both T1 (bench to bedside) and T2 (bedside to community) translational settings. Prior to his current positions, Dr. Kahn was faculty in the Departments of Medicine, Computer Sciences, and Biomedical Engineering at Washington University School of Medicine in St Louis, Director of Advanced Clinical Systems at BJC Health Systems, and was in the commercial clinical trials software industry before returning to academics. Dr. Kahn has been a member of the board of directors of the American Medical Informatics Association, the Board of Scientific Counselors of the National Library of Medicine and the editorial boards of the Journal of the American Medical Informatics Association and the International Journal of Medical Informatics. He is a member of the American College of Medical Informatics.PowerPoint presentation given on May 8, 2015.Funding was provided by a contract from AcademyHealth. Additional support was provided by AHRQ 1R01HS019912-01 (Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 (Colorado Clinical and Translational Sciences Institute)