59 research outputs found
Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective
Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called "Learning Healthcare System Cycle," where healthcare practice and research are part of a unique and synergic process. In this paper we highlight how "Big Data enabled" integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrhythmogenic Diseases
An ICT infrastructure to integrate clinical and molecular data in oncology research
<p>Abstract</p> <p>Background</p> <p>The ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface.</p> <p>Methods</p> <p>Onco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system.</p> <p>Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services.</p> <p>Results</p> <p>Onco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts.</p> <p>Conclusions</p> <p>Onco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module.</p
Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis
Applying Artificial Intelligence (AI) and Machine Learning (ML) in critical
contexts, such as medicine, requires the implementation of safety measures to
reduce risks of harm in case of prediction errors. Spotting ML failures is of
paramount importance when ML predictions are used to drive clinical decisions.
ML predictive reliability measures the degree of trust of a ML prediction on a
new instance, thus allowing decision-makers to accept or reject it based on its
reliability. To assess reliability, we propose a method that implements two
principles. First, our approach evaluates whether an instance to be classified
is coming from the same distribution of the training set. To do this, we
leverage Autoencoders (AEs) ability to reconstruct the training set with low
error. An instance is considered Out-of-Distribution (OOD) if the AE
reconstructs it with a high error. Second, it is evaluated whether the ML
classifier has good performances on samples similar to the newly classified
instance by using a proxy model. We show that this approach is able to assess
reliability both in a simulated scenario and on a model trained to predict
disease progression of Multiple Sclerosis patients. We also developed a Python
package, named relAI, to embed reliability measures into ML pipelines. We
propose a simple approach that can be used in the deployment phase of any ML
model to suggest whether to trust predictions or not. Our method holds the
promise to provide effective support to clinicians by spotting potential ML
failures during deployment.Comment: 20 pages, 7 figure
A dashboard-based system for supporting diabetes care
[EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice.
Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers.
Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿<¿.01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system¿s capability of identifying and understanding the characteristics of patient subgroups treated at the center.
Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver Salcedo, V.... (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association. 25(5):538-547. https://doi.org/10.1093/jamia/ocx159S538547255Sim, I., Gorman, P., Greenes, R. A., Haynes, R. B., Kaplan, B., Lehmann, H., & Tang, P. C. (2001). Clinical Decision Support Systems for the Practice of Evidence-based Medicine. Journal of the American Medical Informatics Association, 8(6), 527-534. doi:10.1136/jamia.2001.0080527Palmer, A. J., Roze, S., Valentine, W. J., Minshall, M. E., Foos, V., Lurati, F. M., … Spinas, G. A. (2004). The CORE Diabetes Model: Projecting Long-term Clinical Outcomes, Costs and Costeffectiveness of Interventions in Diabetes Mellitus (Types 1 and 2) to Support Clinical and Reimbursement Decision-making. Current Medical Research and Opinion, 20(sup1), S5-S26. doi:10.1185/030079904x1980O’Connor, P. J., Bodkin, N. L., Fradkin, J., Glasgow, R. E., Greenfield, S., Gregg, E., … Wysham, C. H. (2011). Diabetes Performance Measures: Current Status and Future Directions. Diabetes Care, 34(7), 1651-1659. doi:10.2337/dc11-0735Donsa, K., Beck, P., Höll, B., Mader, J. K., Schaupp, L., Plank, J., … Pieber, T. R. (2016). Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study. International Journal of Medical Informatics, 90, 58-67. doi:10.1016/j.ijmedinf.2016.03.007Sáenz, A., Brito, M., Morón, I., Torralba, A., García-Sanz, E., & Redondo, J. (2012). Development and Validation of a Computer Application to Aid the Physician’s Decision-Making Process at the Start of and during Treatment with Insulin in Type 2 Diabetes: A Randomized and Controlled Trial. Journal of Diabetes Science and Technology, 6(3), 581-588. doi:10.1177/193229681200600313Ampudia-Blasco, F. J., Benhamou, P. Y., Charpentier, G., Consoli, A., Diamant, M., Gallwitz, B., … Stoevelaar, H. (2015). A Decision Support Tool for Appropriate Glucose-Lowering Therapy in Patients with Type 2 Diabetes. Diabetes Technology & Therapeutics, 17(3), 194-202. doi:10.1089/dia.2014.0260Lim, S., Kang, S. M., Shin, H., Lee, H. J., Won Yoon, J., Yu, S. H., … Jang, H. C. (2011). Improved Glycemic Control Without Hypoglycemia in Elderly Diabetic Patients Using the Ubiquitous Healthcare Service, a New Medical Information System. Diabetes Care, 34(2), 308-313. doi:10.2337/dc10-1447Lipton, J. A., Barendse, R. J., Akkerhuis, K. M., Schinkel, A. F. L., & Simoons, M. L. (2010). Evaluation of a Clinical Decision Support System for Glucose Control. Critical Pathways in Cardiology: A Journal of Evidence-Based Medicine, 9(3), 140-147. doi:10.1097/hpc.0b013e3181e7d7caNeubauer, K. M., Mader, J. K., Höll, B., Aberer, F., Donsa, K., Augustin, T., … Pieber, T. R. (2015). Standardized Glycemic Management with a Computerized Workflow and Decision Support System for Hospitalized Patients with Type 2 Diabetes on Different Wards. Diabetes Technology & Therapeutics, 17(10), 685-692. doi:10.1089/dia.2015.0027Rodbard, D., & Vigersky, R. A. (2011). Design of a Decision Support System to Help Clinicians Manage Glycemia in Patients with Type 2 Diabetes Mellitus. Journal of Diabetes Science and Technology, 5(2), 402-411. doi:10.1177/193229681100500230Augstein, P., Vogt, L., Kohnert, K.-D., Heinke, P., & Salzsieder, E. (2010). Translation of Personalized Decision Support into Routine Diabetes Care. Journal of Diabetes Science and Technology, 4(6), 1532-1539. doi:10.1177/193229681000400631Reza, A. W., & Eswaran, C. (2009). A Decision Support System for Automatic Screening of Non-proliferative Diabetic Retinopathy. Journal of Medical Systems, 35(1), 17-24. doi:10.1007/s10916-009-9337-yKumar, S. J. J., & Madheswaran, M. (2012). An Improved Medical Decision Support System to Identify the Diabetic Retinopathy Using Fundus Images. Journal of Medical Systems, 36(6), 3573-3581. doi:10.1007/s10916-012-9833-3Cho, B. H., Yu, H., Kim, K.-W., Kim, T. H., Kim, I. Y., & Kim, S. I. (2008). Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. Artificial Intelligence in Medicine, 42(1), 37-53. doi:10.1016/j.artmed.2007.09.005Cleveringa, F. G. W., Gorter, K. J., van den Donk, M., & Rutten, G. E. H. M. (2008). Combined Task Delegation, Computerized Decision Support, and Feedback Improve Cardiovascular Risk for Type 2 Diabetic Patients: A cluster randomized trial in primary care. Diabetes Care, 31(12), 2273-2275. doi:10.2337/dc08-0312Haussler, B., Fischer, G. C., Meyer, S., & Sturm, D. (2007). Risk assessment in diabetes management: how do general practitioners estimate risks due to diabetes? Quality and Safety in Health Care, 16(3), 208-212. doi:10.1136/qshc.2006.019539Heselmans, A., Van de Velde, S., Ramaekers, D., Vander Stichele, R., & Aertgeerts, B. (2013). Feasibility and impact of an evidence-based electronic decision support system for diabetes care in family medicine: protocol for a cluster randomized controlled trial. Implementation Science, 8(1). doi:10.1186/1748-5908-8-83Koopman, R. J., Kochendorfer, K. M., Moore, J. L., Mehr, D. R., Wakefield, D. S., Yadamsuren, B., … Belden, J. L. (2011). A Diabetes Dashboard and Physician Efficiency and Accuracy in Accessing Data Needed for High-Quality Diabetes Care. The Annals of Family Medicine, 9(5), 398-405. doi:10.1370/afm.1286Den Ouden, H., Vos, R. C., Reidsma, C., & Rutten, G. E. (2015). Shared decision making in type 2 diabetes with a support decision tool that takes into account clinical factors, the intensity of treatment and patient preferences: design of a cluster randomised (OPTIMAL) trial. BMC Family Practice, 16(1). doi:10.1186/s12875-015-0230-0Holbrook, A., Thabane, L., Keshavjee, K., Dolovich, L., Bernstein, B., … Chan, D. (2009). Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. Canadian Medical Association Journal, 181(1-2), 37-44. doi:10.1503/cmaj.081272O’Reilly, D., Holbrook, A., Blackhouse, G., Troyan, S., & Goeree, R. (2012). Cost-effectiveness of a shared computerized decision support system for diabetes linked to electronic medical records. Journal of the American Medical Informatics Association, 19(3), 341-345. doi:10.1136/amiajnl-2011-000371Parker, R. F., Mohamed, A. Z., Hassoun, S. A., Miles, S., & Fernando, D. J. S. (2014). The Effect of Using a Shared Electronic Health Record on Quality of Care in People With Type 2 Diabetes. Journal of Diabetes Science and Technology, 8(5), 1064-1065. doi:10.1177/1932296814536880Caban, J. J., & Gotz, D. (2015). Visual analytics in healthcare - opportunities and research challenges. Journal of the American Medical Informatics Association, 22(2), 260-262. doi:10.1093/jamia/ocv006Mick, J. (2011). Data-Driven Decision Making. JONA: The Journal of Nursing Administration, 41(10), 391-393. doi:10.1097/nna.0b013e31822edb8cBatley, N. J., Osman, H. O., Kazzi, A. A., & Musallam, K. M. (2011). Implementation of an Emergency Department Computer System: Design Features That Users Value. The Journal of Emergency Medicine, 41(6), 693-700. doi:10.1016/j.jemermed.2010.05.014Sprague, A. E., Dunn, S. I., Fell, D. B., Harrold, J., Walker, M. C., Kelly, S., & Smith, G. N. (2013). Measuring Quality in Maternal-Newborn Care: Developing a Clinical Dashboard. Journal of Obstetrics and Gynaecology Canada, 35(1), 29-38. doi:10.1016/s1701-2163(15)31045-8WILBANKS, B. A., & LANGFORD, P. A. (2014). A Review of Dashboards for Data Analytics in Nursing. CIN: Computers, Informatics, Nursing, 32(11), 545-549. doi:10.1097/cin.0000000000000106Hartzler, A. L., Izard, J. P., Dalkin, B. L., Mikles, S. P., & Gore, J. L. (2015). Design and feasibility of integrating personalized PRO dashboards into prostate cancer care. Journal of the American Medical Informatics Association, 23(1), 38-47. doi:10.1093/jamia/ocv101Dixon, B. E., Jabour, A. M., Phillips, E. O., & Marrero, D. G. (2014). An informatics approach to medication adherence assessment and improvement using clinical, billing, and patient-entered data. Journal of the American Medical Informatics Association, 21(3), 517-521. doi:10.1136/amiajnl-2013-001959Murphy, S. N., Weber, G., Mendis, M., Gainer, V., Chueh, H. C., Churchill, S., & Kohane, I. (2010). Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2), 124-130. doi:10.1136/jamia.2009.000893Shahar, Y., & Musen, M. A. (1996). Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, 8(3), 267-298. doi:10.1016/0933-3657(95)00036-4Sacchi, L., Capozzi, D., Bellazzi, R., & Larizza, C. (2015). JTSA: An open source framework for time series abstractions. Computer Methods and Programs in Biomedicine, 121(3), 175-188. doi:10.1016/j.cmpb.2015.05.006Dagliati, A., Sacchi, L., Zambelli, A., Tibollo, V., Pavesi, L., Holmes, J. H., & Bellazzi, R. (2017). Temporal electronic phenotyping by mining careflows of breast cancer patients. Journal of Biomedical Informatics, 66, 136-147. doi:10.1016/j.jbi.2016.12.012Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association, 20(1), 117-121. doi:10.1136/amiajnl-2012-001145Bijlsma, M. J., Janssen, F., & Hak, E. (2015). Estimating time-varying drug adherence using electronic records: extending the proportion of days covered (PDC) method. Pharmacoepidemiology and Drug Safety, 25(3), 325-332. doi:10.1002/pds.3935Robusto, F., Lepore, V., D’Ettorre, A., Lucisano, G., De Berardis, G., Bisceglia, L., … Nicolucci, A. (2016). The Drug Derived Complexity Index (DDCI) Predicts Mortality, Unplanned Hospitalization and Hospital Readmissions at the Population Level. PLOS ONE, 11(2), e0149203. doi:10.1371/journal.pone.0149203De Berardis, G., D’Ettorre, A., Graziano, G., Lucisano, G., Pellegrini, F., Cammarota, S., … Nicolucci, A. (2012). The burden of hospitalization related to diabetes mellitus: A population-based study. Nutrition, Metabolism and Cardiovascular Diseases, 22(7), 605-612. doi:10.1016/j.numecd.2010.10.016Van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., & Seydel, E. R. (2011). A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies. Journal of Medical Internet Research, 13(4), e111. doi:10.2196/jmir.1672Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1-2), 79-133. doi:10.1016/s0004-3702(96)00025-2Tenenbaum, J. D., Avillach, P., Benham-Hutchins, M., Breitenstein, M. K., Crowgey, E. L., Hoffman, M. A., … Freimuth, R. R. (2016). An informatics research agenda to support precision medicine: seven key areas. Journal of the American Medical Informatics Association, 23(4), 791-795. doi:10.1093/jamia/ocv213Bottomly, D., McWeeney, S. K., & Wilmot, B. (2015). HitWalker2: visual analytics for precision medicine and beyond. Bioinformatics, 32(8), 1253-1255. doi:10.1093/bioinformatics/btv739Fabris, C., Facchinetti, A., Fico, G., Sambo, F., Arredondo, M. T., & Cobelli, C. (2015). Parsimonious Description of Glucose Variability in Type 2 Diabetes by Sparse Principal Component Analysis. Journal of Diabetes Science and Technology, 10(1), 119-124. doi:10.1177/1932296815596173Hassenzahl, M., Wiklund-Engblom, A., Bengs, A., Hägglund, S., & Diefenbach, S. (2015). Experience-Oriented and Product-Oriented Evaluation: Psychological Need Fulfillment, Positive Affect, and Product Perception. International Journal of Human-Computer Interaction, 31(8), 530-544. doi:10.1080/10447318.2015.106466
What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project
[EN] Background To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. Methods The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. Results Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. Conclusions By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.The research leading to these results has received funding from the European Commission under the European Union's Seventh Framework Programme (FP7/2007-2013) grant agreement no 600914.Fico, G.; Hernandez, L.; Cancela, J.; Dagliati, A.; Sacchi, L.; Martinez-Millana, A.; Posada, J.... (2019). What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project. BMC Medical Informatics and Decision Making. 19(1):1-16. https://doi.org/10.1186/s12911-019-0887-8116191World Health Statistics 2018, Monitoring health for the SDGs, World Health Organization. Available at: https://www.who.int/gho/publications/world_health_statistics/en/ , last Accessed 09 Aug 2019.Kane R, Priester R, Totten A. Meeting the challenge of chronic illness. Baltimore: The Johns Hopkins University Press; 2005.Colagiuri, S., Kent, J., Kainu, T., Sutherland, S., Vuik, S. Rising to the challenge: preventing and managing type 2 diabetes, report of the WISH diabetes forum. 2015. Available from: http://www.wish.org.qa/wp-content/uploads/.../WISH_Diabetes_Forum_08.01.15_WEB-1.pdf . Accessed 09 Aug 2019.IDFD Atlas. 2017. Available from: http://www.diabetesatlas.org/resources/2017-atlas.html . Accessed 11 Feb 2018.American Diabetes Association Consensus Panel. Guidelines for computer modeling of diabetes and its complications. Diabetes Care. 2004;27(9):2262–5.Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011;343:d7163.Abbasi A, Peelen LM, Corpeleijn E, van der Schouw YT, Stolk RP, Spijkerman AM, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 2012;345:e5900.Zarkogianni K, Litsa E, Mitsis K, Wu P, Kaddi CD, Cheng C, Wang MD, Nikita KS. A review of emerging technologies for the management of diabetes mellitus. IEEE Trans Biomed Eng. 2015;62(12):2735–49.Garg AX, Adhikari NK, McDonald H, Rosas-Arellano M, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223–38.Roshanov PS, et al. Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review. Implement Sc. 2011;6(1):92.Roshanov PS, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013;346:f657.Miller A, Moon B, Anders S, Walden R, Brown S, Montella D. Integrating computerized clinical decision support systems into clinical work: a meta-synthesis of qualitative research. Int J Med Inform. 2015;84(12):1009–18.Patel VL, Kannampallil TG. Cognitive informatics in biomedicine and healthcare. J Biomed Inform. 2015;53:3–14.Zhang J. Human-centered computing in health information systems part 1: analysis and design. J Biomed Inform. 2005;38(1):1–3.Rinkus S, Walji M, Johnson-Throop KA, Malin M, Turley JP, Smith JW, Zhang J. Human-centered design of a distributed knowledge management system. J Biomed Inform. 2005;38:4–17.Nemeth CP, Nunnally M, O’Connor M, Klock PA, Cook R. Getting to the point: developing IT for the sharp end of healthcare. J Biomed Inform. 2005;38:18–25.Xiao Y. Artifacts and collaborative work in healthcare: methodological, theoretical and technological implications of the tangible. J Biomed Inform. 2005;38:26–33.Malhotra S, Laxmisan A, Keselman A, Zhang J, Patel VL. Designing the design phase of critical care devices: a cognitive approach. J Biomed Inform. 2005;38:34–50.Samaras GM, Horst RL. A systems engineering perspective on the human-centered design of health information systems. J Biomed Inform. 2005;38:61–74.Johnson CM, Johnson TR, Zhang J. A user-centered framework for redesigning health care interfaces. J Biomed Inform. 2005;38:75–87.Patterson ES, Boebbeling BN, Fung CH, Militello L, Anders S, Asch SM. Identifying barriers to the effective use of clinical reminders: bootstrapping multiple methods. J Biomed Inform. 2005;38:189–99.Laxmisan A, Malhotra S, Keselman A, Johnson TR, Patel VL. Decisions about critical events in device-related scenarios as a function of expertise. J Biomed Inform. 2005;38:200–12.Ginsburg GE. Human factors engineering: a tool for medical device evaluation in hospital procurement decision-making. J Biomed Inform. 2005;38:213–9.Reddy M, McDonald DW, Pratt W, Shabot MM. Technology, work, and information flows: lessons from the implementation of a wireless alert pager system. J Biomed Inform. 2005;38:229–38.Despont-Gros C, Mueller H, Lovis C. Evaluating user interactions with clinical information systems: a model based on human–computer interaction models. J Biomed Inform. 2005;38:244–55.World Health Organization, 2009. Practical guidance for scaling up health service innovations.European Commission, 2015. European scaling up strategy on active and healthy ageing.van Gemert-Pijnen JE, Nijland N, van Limburg M, Ossebaard HC, Kelders SM, Eysenbach G, Seydel ER. A holistic framework to improve the uptake and impact of eHealth technologies. J Med Internet Res. 2011;13(4):e111.Sacchi L, Dagliati A, Segagni D, Leporati P, Chiovato L, Bellazzi R. Improving risk-stratification of diabetes complications using temporal data mining. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). United States: IEEE; 2015. p. 2131–4.Sambo F, Di Camillo B, Franzin A, Facchinetti A, Hakaste L, Kravic J, Fico G, et al. A Bayesian Network analysis of the probabilistic relations between risk factors in the predisposition to type 2 diabetes. In: 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC). United States: IEEE; 2015. p. 2119–22.Van Velsen L, van Gemert-Pijnen L, Nijland N, Beaujean D, Van Steenbergen J. Personas: the linking pin in holistic design for eHealth. proc. eTELEMED; 2012.Van Velsen L, Wentzel J, Van Gemert-Pijnen JE. Designing eHealth that matters via a multidisciplinary requirements development approach. JMIR Res Protoc. 2013;2(1):e21.Maurya A. Running lean: iterate from plan A to a plan that works. United States: O’Reilly Media, Inc.; 2012.Wentzel J, Van Limburg M, Karreman J, Hendrix R, Van Gemert-Pijnen L. Co-creation with stakeholders: a Web 2.0 Antibiotic Stewardship Program. Proceedings of The Fourth International Conference on eHealth, Telemedicine, and Social Medicine: January 30, 2012 to February 4, 2012; Valencia. 2012:196–202.Morgan DL. Focus groups as qualitative research. Thousand Oaks: Sage; 1997.Saaty T. How to structure and make choices in complex problems. Hum Syst Manag. 1982;3:255–61.Saaty TL. A scaling method for priorities in hierarchical structures. J Math Psychol. 1977;15:234–81.Pecchia L, Bath PA, Pendleton N, Bracale M: Web-based system for assessing risk factors for falls in community-dwelling elderly people using the analytic hierarchy process. International Journal of the Analytic Hierarchy Process. 2010;2(2):135–57.Fico G, Gaeta E, Arredondo MT, Pecchia L. Analytic hierarchy process to define the most important factors and related technologies for empowering elderly people in taking an active role in their health. J Med Syst. 2015;39(9):1–7.Goepel KD. Implementation of an online software tool for the Analytic Hierarchy Process (AHP-OS). Int J Anal Hierarchy Process. 2018;10(3):469–87. https://doi.org/10.13033/ijahp.v10i3.590 .Nielsen J. Ten usability heuristics. United States: Nielsen Norman Group; 2005.Brooke J. SUS-A quick and dirty usability scale. Usability Eval Ind. 1996;189(194):4–7.Hassenzahl M, Burmester M, Koller F. AttrakDiff: Ein Fragebogen zur Messung wahrgenommener hedonischer und pragmatischer Qualität. In: Mensch & computer. Germany: Vieweg+ Teubner Verlag; 2003, 2003. p. 187–96.International Organization for Standardization. Ergonomics of human-system interaction: part 210: human-centred design for interactive systems. United States: ISO; 2010.Sauro J, Lewis JR. Quantifying the user experience: practical statistics for user research. Burlington: Morgan Kaufmann; 2012.Gülcü C. The complete log4j manual. QOS. ch; 2003.Nantz B. Open source. NET development: programming with NAnt, NUnit, NDoc, and More. United States: Addison-Wesley Professional; 2004.Borsci S, Federici S, Lauriola M. On the dimensionality of the system usability scale: a test of alternative measurement models. Cogn Process. 2009;10(3):193–7.Borsci S, Federici S, Bacci S, Gnaldi M, Bartolucci F. Assessing user satisfaction in the era of user experience: comparison of the SUS, UMUX, and UMUX-LITE as a function of product experience. Int J Hum Comput Interact. 2015;31(8):484–95.Nielsen J, Landauer TK. A mathematical model of the finding of usability problems. In Proceedings of the INTERACT'93 and CHI'93 conference on Human factors in computing systems. 1993. pp. 206–13. ACM.Dagliati A, Sacchi L, Tibollo V, Cogni G, Teliti M, Martinez-Millana A, et al. A dashboard-based system for supporting diabetes care. J Am Med Inform Assoc. 2018;25(5):538–47.Fico G, et al. User requirements for incorporating diabetes modeling techniques in disease management tools. In: 6th European conference of the international federation for medical and biological engineering. Switzerland: Springer International Publishing; 2015.Cancela J, Hernandez L, Fico G, Waldmeyer MTA. Heuristic evaluation of a toolset for type 2 diabetes mellitus management. In: XIV Mediterranean conference on medical and biological engineering and computing. Switzerland: Springer International Publishing; 2016, 2016. p. 982–7.Borsci S, Uchegbu I, Buckle P, Ni Z, Walne S, Hanna GB. Designing medical technology for resilience: integrating health economics and human factors approaches. Expert Rev Med Devices. 2018;15(1):15–26
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EPMA-World Congress 2015: Bonn, Germany. 3-5 September 2015
Table of contents A1 Predictive and prognostic biomarker panel for targeted application of radioembolisation improving individual outcomes in hepatocellular carcinoma Jella-Andrea Abraham, Olga Golubnitschaja A2 Integrated market access approach amplifying value of “Rx-CDx” Ildar Akhmetov A3 Disaster response: an opportunity to improve global healthcare Russell J. Andrews, Leonidas Quintana A4 USA PPPM: proscriptive, profligate, profiteering medicine-good for 1 % wealthy, not for 99 % unhealthy Russell J. Andrews A5 The role of IDO in a murine model of gingivitis: predictive and therapeutic potentials Babak Baban, Jun Yao Liu, Xu Qin, Tailing Wang, Mahmood S. Mozaffari A6 Specific diets for personalised treatment of diabetes type 2 Viktoriia V. Bati, Tamara V. Meleshko, Olga B. Levchuk, Nadiya V. Boyko A7 Towards personalized physiotherapeutic approach Joanna Bauer, Ewa Boerner, Halina Podbielska A8 Cells, animal, SHIME and in silico models for detection and verification of specific biomarkers of non-communicable chronic diseases Alojz Bomba, Viktor O. Petrov, Volodymyr G. Drobnych, Rostyslav V. Bubnov, Oksana M. Bykova, Nadiya V. Boyko A9 INTERACT-chronic care model: Self-treatment by patients with decision support e-Health solution Hans-Peter Brunner-La Rocca, Lutz Fleischhacker, Olga Golubnitschaja, Frank Heemskerk, Thomas Helms, Tiny Jaarsma, Judita Kinkorova, Jan Ramaekers, Peter Ruff, Ivana Schnur, Emilio Vanoli, Jose Verdu A10 PPPM in cardiovascular medicine in 2015 Hans-Peter Brunner-La Rocca A11 Magnetic resonance imaging of nanoparticles in mice, potential for theranostic and contrast media development – pilot results Rostyslav V. Bubnov, Sergiy A. Grabovetskyi, Olena M. Mykhalchenko, Natalia O. Tymoshok, Oleksandr B. Shcherbakov, Igor P. Semeniv, Mykola Y. Spivak A12 Ultrasound diagnosis for diabetic neuropathy - comparative study Rostyslav V. Bubnov, Tetyana V. Ostapenko A13 Ultrasound for stratification patients with diabetic foot ulcers for prevention and personalized treatment - pilot results Rostyslav V. Bubnov, Nazarii M. Kobyliak, Nadiya M. Zholobak, Mykola Ya. Spivak A14 Project ImaGenX – designing and executing a questionnaire on environment and lifestyle risk of breast cancer John Paul Cauchi A15 Genomics – a new structural brand of predictive, preventive and personalized medicine or the new driver as well? Dmitrii Cherepakhin, Marina Bakay, Artem Borovikov, Sergey Suchkov A16 Survey of questionnaires for evaluation of the quality of life in various medical fields Barbara Cieślik, Agnieszka Migasiewicz, Maria-Luiza Podbielska, Markus Pelleter, Agnieszka Giemza, Halina Podbielska A17 Personalized molecular treatment for muscular dystrophies Sebahattin Cirak A18 Secondary mutations in circulating tumour DNA for acquired drug resistance in patients with advanced ALK + NSCLC Marzia Del Re, Paola Bordi, Valentina Citi, Marta Palombi, Carmine Pinto, Marcello Tiseo, Romano Danesi A19 Recombinant species-specific FcεRI alpha proteins for diagnosis of IgE-mediated allergies in dogs, cats and horses Lukas Einhorn, Judit Fazekas, Martina Muhr, Alexandra Schoos, Lucia Panakova, Ina Herrmann, Krisztina Manzano-Szalai, Kumiko Oida, Edda Fiebiger, Josef Singer, Erika Jensen-Jarolim A20 Global methodology for developmental neurotoxicity testing in humans and animals early and chronically exposed to chemical contaminants Arpiné A. Elnar, Nadia Ouamara, Nadiya Boyko, Xavier Coumoul, Jean-Philippe Antignac, Bruno Le Bizec, Gauthier Eppe, Jenny Renaut, Torsten Bonn, Cédric Guignard, Margherita Ferrante, Maria Liusa Chiusano, Salvatore Cuzzocrea, Gerard O'Keeffe, John Cryan, Michelle Bisson, Amina Barakat, Ihsane Hmamouchi, Nasser Zawia, Anumantha Kanthasamy, Glen E. Kisby, Rui Alves, Oscar Villacañas Pérez, Kim Burgard, Peter Spencer, Norbert Bomba, Martin Haranta, Nina Zaitseva, Irina May, Stéphanie Grojean, Mathilde Body-Malapel, Florencia Harari, Raul Harari, Kristina Yeghiazaryan, Olga Golubnitschaja, Vittorio Calabrese, Christophe Nemos, Rachid Soulimani A21 Mental indicators at young people with attributes hypertension and pre-hypertension Maria E. Evsevyeva, Elena A. Mishenko, Zurida V. Kumukova, Evgeniy V. Chudnovsky, Tatyana A. Smirnova A22 On the approaches to the early diagnosis of stress-induced hypertension in young employees of State law enforcement agencies Maria E. Evsevyeva, Ludmila V. Ivanova, Michail V. Eremin, Maria V. Rostovtseva A23 Сentral aortic pressure and indexes of augmentation in young persons in view of risk factors Maria E. Evsevyeva, Michail V. Eremin, Vladimir I. Koshel, Oksana V. Sergeeva, Nadesgda M. Konovalova A24 Breast cancer prediction and prevention: Are reliable biomarkers in horizon? Shantanu Girotra, Olga Golubnitschaja A25 Flammer Syndrome and potential formation of pre-metastatic niches: A multi-centred study on phenotyping, patient stratification, prediction and potential prevention of aggressive breast cancer and metastatic disease Olga Golubnitschaja, Manuel Debald, Walther Kuhn, Kristina Yeghiazaryan, Rostyslav V. Bubnov, Vadym M. Goncharenko, Ulyana Lushchyk, Godfrey Grech, Katarzyna Konieczka A26 Innovative tools for prenatal diagnostics and monitoring: improving individual pregnancy outcomes and health-economy in EU Olga Golubnitschaja, Jan Jaap Erwich, Vincenzo Costigliola, Kristina Yeghiazaryan, Ulrich Gembruch A27 Immunohistochemical assessment of APUD cells in endometriosis Vadym M. Goncharenko, Vasyl O. Beniuk, Olga V. Kalenska, Rostyslav V. Bubnov A28 Updating personalized management algorithm of endometrial hyperplasia in pre-menopause women Vadym M. Goncharenko, Vasyl O. Beniuk, Rostyslav V. Bubnov, Olga Melnychuk A29 The personified treatment approach of polimorbid patients with periodontal inflammatory diseases Irina A. Gorbacheva, Lyudmila Y. Orekhova, Vadim V. Tachalov A30 Ukrainian experience in hybrid war – the challenge to update algorithms for personalized care and early prevention of different military injuries Olena I. Grechanyk, Rizvan Ya. Abdullaiev, Rostyslav V. Bubnov A31 Tear fluid biomarkers: a comparison of tear fluid sampling and storage protocols Suzanne Hagan, Eilidh Martin, Ian Pearce, Katherine Oliver A32 The correlation of dietary habits with gingival problems during menstruation Cenk Haytac, Fariz Salimov, Servin Yoksul, Anatoly A. Kunin, Natalia S. Moiseeva A33 Genomic medicine in a contemporary Spanish population of prostate cancer: our experience Bernardo Herrera-Imbroda, Sergio del Río-González, Maria Fernanda Lara, Antonia Angulo, Francisco Javier Machuca Santa-Cruz A34 Challenges, opportunities and collaborations for personalized medicine applicability in uro-oncological disease Bernardo Herrera-Imbroda, Sergio del Río-González, Maria Fernanda Lara A35 Metabolic hallmarks of cancer as targets for a personalized therapy John Ionescu A36 Influence of genetic polymorphism as a predictor of the development of periodontal disease in patients with gastric ulcer and 12 duodenal ulcer Alfiya Z. Isamulaeva, Anatoly A. Kunin, Shamil Sh. Magomedov, Aida I. Isamulaeva A37 Challenges in diabetic macular edema Tatjana Josifova A38 Overview of the EPMA strategies in laboratory medicine relevant for PPPM Marko Kapalla, Juraj Kubáň, Olga Golubnitschaja, Vincenzo Costigliola A39 EPMA initiative for effective organization of medical travel: European concepts and criteria Vincenzo Costigliola, Marko Kapalla, Juraj Kubáň, Olga Golubnitschaja A40 Design and innovation in e-textiles: implications for PPPM Anthony Kent, Tom Fisher, Tilak Dias A41 Biobank in Pilsen as a member of national node BBMRI_CZ Judita Kinkorová, Ondřej Topolčan A42 Big data in personalized medicine: hype and hope Matthias Kohl A43 The 3P approach as the platform of the European Dentistry Department (DPPPD) Anatoly A. Kunin, Natalia S. Moiseeva A44 The endometrium cytokine patterns for predictive diagnosis of proliferation severity and cancer prevention Andrii I. Kurchenko, Vasyl A. Beniuk, Vadym M. Goncharenko, Rostyslav V. Bubnov, Nadiya V. Boyko, Andriy M. Strokan A45 A monocyte-based in-vitro system for testing individual responses to the implanted material: future for personalized implant construction Julia Kzhyshkowska, Alexandru Gudima, Ksenia S. Stankevich, Victor D. Filimonov4, Harald Klüter, Evgeniya M. Mamontova, Sergei I. Tverdokhlebov A46 Prediction and prevention of adverse health effects by meteorological factors: Biomarker patterns and creation of a device for self-monitoring and integrated care Ulyana B. Lushchyk, Viktor V. Novytskyy, Igor P. Babii, Nadiya G. Lushchyk, Lyudmyla S. Riabets, Ivanna I. Legka A47 Targeting "disease signatures" towards personalized healthcare Mira Marcus-Kalish, Alexis Mitelpunkt, Tal Galili, Neta Shachar, Yoav Benjamini A48 Influence of the skin imperfection on the personal quality of life and possible tools for objective diagnosis Agnieszka Migasiewicz, Markus Pelleter, Joanna Bauer, Ewelina Dereń, Halina Podbielska A49 The new direction in caries prevention based on the ultrastructure of dental hard tissues and filling materials Natalia S. Moiseeva, Anatoly A. Kunin, Dmitry A. Kunin A50 The use of LED radiation in prevention of dental diseases Natalia S. Moiseeva, Yury A. Ippolitov, Dmitry A. Kunin, Alexei N. Morozov, Natalia V. Chirkova, Nakhid T. Aliev A51 Status of endothelial progenitor cells in diabetic nephropathy: predictive and preventive potentials Mahmood S. Mozaffari, Jun Yao Liu, Babak Baban A52 The status of glucocorticoid-induced leucine zipper protein in salivary gland in Sjögren’s syndrome: predictive and personalized treatment potentials Mahmood S. Mozaffari, Jun Yao Liu, Rafik Abdelsayed, Xing-Ming Shi, Babak Baban A53 Maximal aerobic capacity - important quality marker of health Jaroslav Novák, Milan Štork, Václav Zeman A54 The EMPOWER project: laboratory medicine and Horizon 2020 Wytze P. Oosterhuis, Elvar Theodorsson A55 Personality profile manifestations in patient’s attitude to oral care and adherence to doctor’s prescriptions Lyudmila Y. Orekhova, Tatyana V. Kudryavtseva, Elena R. Isaeva, Vadim V. Tachalov, Ekaterina S. Loboda A56 Results of an European survey on personalized medicine addressed to directions of laboratory medicine Mario Pazzagli, Francesca Malentacchi, Irene Mancini, Ivan Brandslund, Pieter Vermeersch, Matthias Schwab, Janja Marc, Ron H.N. van Schaik, Gerard Siest, Elvar Theodorsson, Chiara Di Resta A57 MCI or early dementia predictive speech based diagnosis techniques Matus Pleva, Jozef Juhar A58 Personalized speech based mobile application for eHealth Matus Pleva, Jozef Juhar A59 Circulating tumor cell-free DNA as the biomarker in the management of cancer patients Jiří Polívka jr., Filip Janků, Martin Pešta, Jan Doležal, Milena Králíčková, Jiří Polívka A60 Complex stroke care – educational programme in Stroke Centre University Hospital Plzen Jiří Polívka, Alena Lukešová, Nina Müllerová, Petr Ševčík, Vladimír Rohan A61 Sleep apnea and sleep fragmentation contribute to brain aging Kneginja Richter, Lence Miloseva, Günter Niklewski A62 Personalised approach for sleep disturbances in shift workers Kneginja Richter, Jens Acker, Guenter Niklewski A63 Medical travel and innovative PPPM clusters: new concept of integration Olga Safonicheva, Vincenzo Costigliola A64 Medical travel and women health Olga Safonicheva A65 Continuity of generations in the training of specialists in the field of reconstructive microsurgery Maxim Sautin, Janna Sinelnikova, Sergey Suchkov A66 Telemonitoring of stroke patients – empirical evidence of individual risk management results from an observational study in Germany Songül Secer, Stephan von Bandemer A67 Women’s increasing breast cancer risk with n-6 fatty acid intake explained by estrogen-fatty acid interactive effect on DNA damage: implications for gender-specific nutrition within personalized medicine Niva Shapira A68 Cytobacterioscopy of the gingival crevicular fluid as a method for preventive diagnosis of periodontal diseases Aleksandr Shcherbakov, Anatoly A. Kunin, Natalia S. Moiseeva A69 Use of specially treated composites in dentistry to avoid violations of aesthetics Bogdan R. Shumilovich, Zhanna Lipkind, Yulia Vorobieva, Dmitry A. Kunin, Anastasiia V. Sudareva A70 National eHealth system – platform for preventive, predictive and personalized diabetes care Ivica Smokovski, Tatjana Milenkovic A72 The common energy levels of Prof. Szent-Györgyi, the intrinsic chemistry of melanin, and the muscle physiopathology. Implications in the context of Preventive, Predictive, and Personalized Medicine Arturo Solís-Herrera, María del Carmen Arias-Esparza, Sergey Suchkov A73 Plurality and individuality of hepatocellular carcinoma: PPPM perspectives Krishna Chander Sridhar, Olga Golubnitschaja A74 Strategic aspects of higher medical education reforms to secure newer educational platforms for getting biopharma professionals matures Maria Studneva, Sihong Song, James Creeden, Мark Мandrik, Sergey Suchkov A75 Overview of the strategies and activities of the European Federation of Clinical Chemistry and Laboratory Medicine, (EFLM) Elvar Theodorsson, EFLM A76 New spectroscopic techniques for point of care label free diagnostics Syed A. M. Tofail A77 Tumor markers for personalized medicine and oncology - the role of Laboratory Medicine Ondřej Topolčan, Judita Kinkorová, Ondřej Fiala, Marie Karlíková, Šárka Svobodová, Radek Kučera, Radka Fuchsová, Vladislav Třeška, Václav Šimánek, Ladislav Pecen, Jan Šoupal, Štěpán Svačina2 A78 Modern medical terminology (MMT) as a driver of the global educational reforms Evgeniya Tretyak, Maria Studneva, Sergey Suchkov A79 Juvenile hypertension; the relevance of novel predictive, preventive and personalized assessment of its determinants Francesca M. Trovato, G. Fabio Martines, Daniela Brischetto, Daniela Catalano, Giuseppe Musumeci, Guglielmo M. Trovato A80 Proteomarkers Biotech George Th. Tsangaris, Athanasios K. Anagnostopoulos A81 Proteomics and mass spectrometry based non-invasive prenatal testing of fetal health and pregnancy complications George Th. Tsangaris, Athanasios K. Anagnostopoulos A82 Integrated Ecosystem for an Integrated Care model for Heart Failure (HF) patients including related comorbidities (ZENITH) José Verdú, German Gutiérrez, Jordi Rovira, Marta Martinez, Lutz Fleischhacker, Donna Green, Arthur Garson, Elena Tamburini, Stefano Cuomo, Juan Martinez-Leon, Teresa Abrisqueta, Hans-Peter Brunner-La Rocca, Tiny Jaarsma, Teresa Arredondo, Cecilia Vera, Giuseppe Fico, Olga Golubnitschaja, Fernando Arribas, Martina Onderco, Isabel Vara, on behalf of ZENITH consortium A83 Predictive, preventive and personalized medicine in diabetes onset and complication (MOSAIC project) José Verdú, Francesco Sambo, Barbara Di Camillo, Claudio Cobelli, Andrea Facchinetti, Giuseppe Fico, Riccardo Bellazzi, Lucia Sacchi, Arianna Dagliati, Daniele Segnani, Valentina Tibollo, Manuel Ottaviano, Rafael Gabriel, Leif Groop, Jacqueline Postma, Antonio Martinez, Liisa Hakaste, Tiinamaija Tuomi, Konstantia Zarkogianni, on behalf of MOSAIC consortium A84 Possibilities for personalized therapy of diabetes using in vitro screening of insulin and oral hypoglycemic agents Igor Volchek, Nina Pototskaya, Andrey Petrov A85 The innovative technology for personalized therapy of human diseases based on in vitro drug screening Igor Volchek, Nadezhda Pototskaya, Andrey Petrov A86 Bone destruction and temporomandibular joint: predictive markers, pathogenetic aspects and quality of life Ülle Voog-Oras, Oksana Jagur, Edvitar Leibur, Priit Niibo, Triin Jagomägi, Minh Son Nguyen, Chris Pruunsild, Dagmar Piikov, Mare Saag A87 Sub-optimal health management – global vision for concepts in medical travel Wei Wang A88 Sub-optimal health management: synergic PPPM-TCAM approach Wei Wang A89 Innovative technologies for minimal invasive diagnostics Andreas Weinhäusel, Walter Pulverer, Matthias Wielscher, Manuela Hofner, Christa Noehammer, Regina Soldo, Peter Hettegger, Istvan Gyurjan, Ronald Kulovics, Silvia Schönthaler, Gabriel Beikircher, Albert Kriegner, Stephan Pabinger, Klemens Vierlinger A90 Rare disease diobanks for personalized medicine Ayşe Yüzbaşıoğlu, Meral Özgüç, Member of EuroBioBank - European Network of DNA, Cell and Tissue Banks for Rare Disease
LONGITUDINAL DATA ANALYTICS FOR CLINICAL DECISION SUPPORT IN TYPE 2 DIABETES
Type 2 Diabetes Mellitus (T2DM) is assuming epidemic proportions, which will progressively worsen as the population ages. Managing T2DM is a complex task, such complexity being embodied in long clinical histories, lasting longer than 10 years and characterized by substantial variability in the type and frequency of clinical events that are manifested across the population and within a single patient history. In addition, the pathology itself entails a number of complications and comorbidities. These issues suggest the difficulty in managing T2DM chronic patients (World Health Organization 2016).
A major source of complexity in the management of T2DM patients arises from events such as hospital admissions, follow-up clinic visits, laboratory tests, and therapy changes. During these events, patients are treated by many different health professionals. These events are stored in different data repositories using different formats and occurring in temporal sequences that represent the patient careflow.
Although these data are distributed in sources such as the Electronic Health Record (EHRs) and, Administrative Data Warehouses, new data management technologies are able to gather and merge them, and consequently enable researchers and other to access a huge quantity of complex data for the interpretation and exploitation of these data for a management of chronic diseases. The application of longitudinal analysis and careflow discovery to these data enable the recognition of hidden temporal patterns, population stratification and cohorts’ identification, and phenotypes definition.
Temporal data analysis and careflow mining techniques can automatically detect the most frequent patterns and careflows from routinely collected data. Once identified, the enacted careflows might be used for comparison with clinical protocols to check their adherence to best practices, but they can be also exploited to identify different sub-groups of individuals in large cohorts of patients. These temporal data mining techniques can be used as a type of electronic phenotyping., which has been defined as the detection of computable phenotypes through query to EHRs and clinical data repository using specific data elements and logical expressions.
Clinical guidelines and health care protocols are well-established tools used to improve and standardize health care services. Nevertheless, in the absence of effective technology-based solutions to automatically extract frequent patterns and careflows, it is often impossible to measure their implementation. Patients’ management processes can be improved through an overall system that integrates longitudinal heterogeneous data, and implements temporal data mining methods that illustrate the evolution of the disease and the individual and population variability. The detection of temporal patterns makes possible to reconstruct clinical pathways and forecast the complications that might arise during the process of care, to identify interesting clusters of patients with similar care histories and re-assess their risk profiles accordingly. The identification of healthcare pathways through methods derived from temporal and careflow mining research can be used for Decision Support.
These facts suggest the need to investigate novel methods for improving the clinical decision support in T2DM and the utility of creating an analytics methodological framework. This is the overall goal of this dissertation, which was successfully retained completing these three specific aims: (i) To implement a system that integrates a large amount of unstructured and structured data from heterogeneous sources; (ii) To extend longitudinal analytic approaches to enable recognition of trending patterns and enhance temporal electronic phenotypes description; (iii) To create an expansion of existing methods for clinical decision support that is based on a more complete and easily understood description of patient health status
Big Data Technologies: New Opportunities for Diabetes Management
The so-called big data revolution provides substantial opportunities to diabetes management. At least 3 important directions are currently of great interest. First, the integration of different sources of information, from primary and secondary care to administrative information, may allow depicting a novel view of patient's care processes and of single patient's behaviors, taking into account the multifaceted nature of chronic care. Second, the availability of novel diabetes technologies, able to gather large amounts of real-time data, requires the implementation of distributed platforms for data analysis and decision support. Finally, the inclusion of geographical and environmental information into such complex IT systems may further increase the capability of interpreting the data gathered and extract new knowledge from them. This article reviews the main concepts and definitions related to big data, it presents some efforts in health care, and discusses the potential role of big data in diabetes care. Finally, as an example, it describes the research efforts carried on in the MOSAIC project, funded by the European Commission
Learning T2D evolving complexity from EMR and administrative data by means of Continuous time Bayesian networks
Predicting the complexity level (i.e. the number of complications and their related hospitalizations) in a T2D cohort is a critical step in prevention, resource optimization and overall patient management. Our data set was obtained by monitoring a T2D diabetic cohort along up to 10 years through electronic medical records of a local healthcare agency data warehouse. In order to conveniently handle temporarily sparse data, we designed a model describing the cohort evolution with Continuous Time Bayesian Networks (CTBN). The network structure and its parameters are entirely data driven. Compared to traditional Bayesian Networks, CTBNs admit cycles. As consequence, CTBNs fit the complexity of chronic metabolic syndromes where variables show a reciprocal influence. Network nodes represent metabolic (glycated hemoglobin, lipid profile (cholesterol, triglycerides), and biometric (BMI) data. We observed how these variables directly or indirectly affect the disease level of complexity, and how the variables influence the cumulative adverse events a patient undergoes
Integrated multi-omics analyses in oncology: a review of machine learning methods and tools
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