Standardizing Biochemistry Dataset for Medical Research
Wilfred Bonney, Alexander Doney, Emily Jefferson
2014
Abstract
Harnessing clinical datasets from the repository of electronic health records for research and medical intelligence has become the norm of the 21st century. Clinical datasets present a great opportunity for medical researchers and data analysts to perform cohort selections and data linkages to support better informed clinical decision-making and evidence-based medicine. This paper utilized Logical Observation Identifiers Names and Codes (LOINC®) encoding methodology to encode the biochemistry tests in the anonymized biochemistry dataset obtained from the Health Informatics Centre (HIC) at the University of Dundee. Preliminary results indicated that the encoded dataset was flexible in supporting statistical analysis and data mining techniques. Moreover, the results indicated that the LOINC codes cover most of the biochemistry tests used in National Health Service (NHS) Tayside, Scotland.
References
- Abhyankar, S., Demner-Fushman, D., & McDonald, C. J. (2012). Standardizing clinical laboratory data for secondary use. Journal of Biomedical Informatics, 45(4), 642-50. doi: 10.1016/j.jbi.2012.04.012.
- AHIMA. (2011). Data mapping best practices. Journal of AHIMA, 82(4), 46-52. Retrieved September 12, 2013, from http://healthdataanalysisupdate.org/?p=97.
- Berwick, D. M. (2002). A user's manual for the IOM's 'Quality Chasm' report. Health Affairs, 21, 80-90.
- Bonney, W. (2011). Impacts and risks of adopting clinical decision support systems. In C. S. Jao (Ed.), Efficient Decision Support Systems: Practice and Challenges In Biomedical Related Domain (pp. 21-30). Rijeka, Croatia: In-Tech. doi: 10.5772/16265.
- Bonney, W. (2013). Applicability of business intelligence in electronic health record. Procedia - Social and Behavioral Sciences, 73, 257-262. doi: 10.1016/j.sbspro.2013.02.050.
- Cios K. J., & Moore, G. W. (2002). Uniqueness of medical data mining. Artificial Intelligence in Medicine. 26(1-2), 1-24.
- Fidahussein, M., Friedlin, J., & Grannis, S. (2011). Practical challenges in the secondary use of real-world data: The notifiable condition detector. AMIA Annual Symposium Proceedings, 2011, 402-408.
- Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). San Francisco: Morgan Kaufmann.
- Khan, A. N., Griffith, S. P., Moore, C., Russell, D., Rosario, A. C. J., & Bertolli, J. (2006). Standardizing laboratory data by mapping to LOINC. Journal of the American Medical Informatics Association, 13(3), 353-355. doi: 10.1197/jamia.M1935.
- Lamont, J. (2006). Business intelligence: The text analysis strategy. KM World, 15(10), 8-10.
- Lee, D. H., Lau, F. Y., & Quan, H. (2010). A method for encoding clinical datasets with SNOMED CT. BMC Medical Informatics and Decision Making 2010, 10:53.
- Lin, J-H., & Haug, P. J. (2006). Data preparation framework for preprocessing clinical data in data mining. AMIA Annual Symposium Proceedings, 2006, 489-493.
- Lin, M. C., Vreeman, D. J., & Huff, S. M. (2011). Investigating the semantic interoperability of laboratory data exchanged using LOINC codes in three large institutions. AMIA Annual Symposium Proceedings, 2011, 805-814.
- LOINC. (2013). Logical Observation Identifiers Names and Codes (LOINC®). Retrieved August 12, 2013, from http://loinc.org.
- McDonald, C. J., Huff, S. M., Suico, J. G., Hill, G., Leavelle, D., Aller, R., Forrey, A., Mercer, K., DeMoor, G., Hook, J., Williams, W., Case, J., & Maloney, P. (2003). LOINC, a universal standard for identifying laboratory observations: A 5-year update. Clinical Chemistry, 49, 624-633.
- Razavi, A. R., Gill, H., Åhlfeldt, H., & Shahsavar, N. (2005). A Data Pre-processing method to increase efficiency and accuracy in data mining. In S. Miksch et al. (Eds.): AIME 2005, LNAI 3581 (pp. 434-443). Berlin: Springer-Verlag.
- RELMA. (2013). Regenstrief LOINC Mapping Assistant (RELMA) Users' Guide. Retrieved August 12, 2013, from http://loinc.org.
- Sanders, C. M., Saltzstein, S. L., Schultzel, M. M., Nguyen, D. H., Stafford, H. S., & Sadler, G. R. (2012). Understanding the limits of large datasets. Journal of Cancer Education, 27(4), 664-669.
- Svensson-Ranallo, P. A., Adam, T. J., & Sainfort, F. (2011). A Framework and standardized methodology for developing minimum clinical datasets. AMIA Summits on Translational Science Proceedings, 2011, 54-58.
- University of Dundee (n.d.). Tayside Bioresource. Retrieved September 17, 2013, from http://medicine.dundee.ac.uk/tayside-bioresource.
- van Vlymen, J., & de Lusignan, S. (2005). A system of metadata to control the process of query, aggregating, cleaning and analysing large datasets of primary care data. Informatics in Primary Care, 13, 281-291.
- Vreeman, D., J., Chiaravalloti, M. T., Hook, J., & McDonald, C. J. (2012). Enabling international adoption of LOINC through translation. Journal of Biomedical Informatics, 45(4), 667-673.
- Wilson, P. S., & Scichilone, R. A. (2011). LOINC as a data standard: How LOINC can be used in electronic environments. Journal of AHIMA, 82(7), 44-47.
- Wirtschafter, D. D., & Mesel, E. (1976). A strategy for redesigning the medical record for quality assurance. Medical Care, 14(1), 68-76.
Paper Citation
in Harvard Style
Bonney W., Doney A. and Jefferson E. (2014). Standardizing Biochemistry Dataset for Medical Research . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014) ISBN 978-989-758-010-9, pages 205-210. DOI: 10.5220/0004745802050210
in Bibtex Style
@conference{healthinf14,
author={Wilfred Bonney and Alexander Doney and Emily Jefferson},
title={Standardizing Biochemistry Dataset for Medical Research},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)},
year={2014},
pages={205-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004745802050210},
isbn={978-989-758-010-9},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)
TI - Standardizing Biochemistry Dataset for Medical Research
SN - 978-989-758-010-9
AU - Bonney W.
AU - Doney A.
AU - Jefferson E.
PY - 2014
SP - 205
EP - 210
DO - 10.5220/0004745802050210