ABSTRACT
We present two ongoing projects aimed at learning from health care records. The first project, DADEL, is focusing on high-performance data mining for detrecting adverse drug events in healthcare, and uses electronic patient records covering seven years of patient record data from the Stockholm region in Sweden. The second project is focusing on heart failure and on understanding the differences in treatment between various groups of patients. It uses a Swedish administrative health register containing health care data for over two million patients.
- L. Breiman. Random forests. Machine Learning, 45(1):5--32, 2001. Google ScholarDigital Library
- A. Henelius, K. Puolamäki, H. Boström, L. Asker, and P. Papapetrou. A peek into the black box: exploring classifiers by randomization. Data Mining and Knowledge Discovery, 28(5-6):1503--1529, 2014. Google ScholarDigital Library
- I. Karlsson and H. Boström. Handling sparsity with random forests when predicting adverse drug events from electronic health records. In IEEE International Conference on Healthcare Informatics, pages 17--22, 2014. Google ScholarDigital Library
- J. Larsen, H. Stovring, J. Kragstrup, and D. G. Hansen. Can differences in medical drug compliance between european countries be explained by social factors: analyses based on data from the european social survey, round 2. BMC Public Health, 9:145, 2009.Google ScholarCross Ref
- Socialstyrelsen. Nationella riktlinjer -- Målnivåer, Hjärtsjuk-vård, 2015. Artikelnummer 2015-10-3.Google Scholar
- Socialstyrelsen. Nationella riktlinjer -- Utvärdering, Hjärtsjukvård, 2015.Google Scholar
- Socialstyrelsen. Nationella riktlinjer för hjärtsjukvård, 2015.Google Scholar
- J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for SVMs. In Advances in Neural Information Processing Systems 13, pages 668--674. MIT Press, 2000. Google ScholarDigital Library
- D. Zachariah, J. Taylor, P. Rowell, M. Mejhert, N. Szulkin, C. Spooner, and P. R. Kalra. Drug therapy for heart failure in older patients -- what do they want? Journal of Geriatric Cardiology, 12(2):165--173, 2015.Google Scholar
- R. Zarrinkoub, B. Wettermark, P. Wändell, M. Mejhert, R. Szulkin, G. Ljunggren, and T. Kahan. The epidemiology of heart failure, based on data for 2.1 million inhabitants in sweden. European Journal of Heart Failure, 15(9):995--1002, 2013.Google ScholarCross Ref
- J. Zhao, A. Henriksson, L. Asker, and H. Boström. Detecting adverse drug events with multiple representations of clinical measurements. In Proceedings of International Conference on Bioinformatics and Biomedicine: 2-5 November 2014; Belfast, UK, pages 536--543. IEEE Computer Society, 2014.Google ScholarCross Ref
- J. Zhao, A. Henriksson, and H. Boström. Detecting adverse drug events using concept hierarchies of clinical codes. In Proceedings of International Conference on Healthcare Informatics, pages 285--293. IEEE Computer Society, 2014. Google ScholarDigital Library
- J. Zhao, I. Karlsson, L. Asker, and H. Boström. Applying methods for signal detection in spontaneous reports to electronic patient records. In Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), 2013.Google Scholar
Index Terms
- Learning from Swedish Healthcare Data
Recommendations
Data Work in Healthcare: Challenges for Patients, Clinicians and Administrators
CSCW '18 Companion: Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social ComputingWith the wide adoption of information infrastructures in healthcare (IIH), citizens and healthcare professionals now carry out intensive "data work' as part of their healthcare and self-management practices. For example, citizens use mobile apps to ...
Implementing the lifelong personal health record in a regionalised health information system: The case of Lombardy, Italy
Abstract BackgroundThe use of personal health records (PHRs) can help people make better health decisions and improves the quality of care by allowing access to and use of the information needed to communicate effectively with ...
Building an Integrated Patient Information System for a Healthcare Network
The recent healthcare reform act provides incentive payments to providers for their 'meaningful use' of electronic health records to achieve significant improvements in care. The HITECH Act 2009 provides incentives payments through Medicare and Medicaid ...
Comments