Data Mining and Knowledge Discovery

, Volume 24, Issue 1, pp 195–217 | Cite as

Data mining from a patient safety database: the lessons learned

  • James Bentham
  • David J. Hand


The issue of patient safety is an extremely important one; each year in the UK, hundreds of thousands of people suffer due to some sort of incident that occurs whilst they are in National Health Service care. The National Patient Safety Agency (NPSA) works to try to reduce the scale of the problem. One of its major projects is to collect a very large dataset, the Reporting and Learning System (RLS), which describes several million of these incidents. The RLS is used as the basis for research by the NPSA. However, the NPSA has identified a gap in their work between high-level quantitative analysis and detailed, manual analysis of small samples. This paper describes the lessons learned from a knowledge discovery process that attempted to fill this gap. The RLS contains a free text description of each incident. A high dimensional model of the text is calculated, using the vector space model with term weighting applied. Dimensionality reduction techniques are used to produce the final models of the text. These models are examined using an anomaly detection tool to find groups of incidents that should be coherent in meaning, and that might be of interest to the NPSA. A three stage process is developed for assessing the results. The first stage uses a quantitative measure based on the use of planted groups of known interest, the second stage involves manual filtering by a non-expert, and the third stage is assessment by clinical experts.


Knowledge Discovery Data Mining Information Extraction Patient Safety 


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  1. Aggarwal CC, Hinneburg A, Keim DA (2000) On the surprising behavior of distance metrics in high dimensional spaces. Lect Notes Comput Sci 1973: 420–434CrossRefGoogle Scholar
  2. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: COLT: proceedings of the workshop on Computational learning theory, pp 92–100Google Scholar
  3. Department of Health Expert Group (2000) An organisation with a memory. Department of Health, LondonGoogle Scholar
  4. Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, pp 226–231Google Scholar
  5. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM 39(11): 27–34CrossRefGoogle Scholar
  6. Foster J, Wagner J, van Genabith J (2008) Adapting a WSJ-trained parser to grammatically noisy text. In: Proceedings of the 46th annual meeting of the association for computational linguistics on Human lanauge technologies, pp 221–224, 16–17 June 2008Google Scholar
  7. Hinneburg A, Aggarwal CC, Keim DA (2000) What is the nearest neighbor in high dimensional spaces. In: Proceedings of the 26th VLDB conference, Egypt, pp 506–515Google Scholar
  8. Honnibal M, Nothman J, Curran JR (2009) Evaluating a statistical CCG parser on wikipedia. In: Proceedings of the 2009 workshop on the People’s web meets NLP: collaboratively constructed semantic resources, pp 38–41, AugustGoogle Scholar
  9. Hyvärinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New YorkCrossRefGoogle Scholar
  10. Lease M, Charniak E (2005) Parsing biomedical literature. In: Second international joint conference on Natural language processing, Jeju Island, pp 58–69Google Scholar
  11. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on Mathematical statistics and probability. University of California Press, Berkeley, pp 281–297Google Scholar
  12. Mannila H (1996) Data mining: machine learning, statistics, and databases. In: Proceedings of the eighth international conference on Scientific and statistical database management, 18–20 June 1996, pp 2–9Google Scholar
  13. Manning C, Raghavan P, Schuetze H (2008) Introduction to information retrieval. Cambridge University Press, CambridgezbMATHGoogle Scholar
  14. Rasmussen C (2000) The infinite Gaussian mixture model. Adv Neural Inf Process Syst 12: 554–560Google Scholar
  15. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, CambridgezbMATHGoogle Scholar
  16. Saad FH, de la Iglesia B (2006) A comparison of two document clustering approaches for clustering medical documents. In: Proceedings of the 2006 international conference on Data Mining, Las Vegas, USAGoogle Scholar
  17. Salakhutdinov R, Hinton G (2009) Semantic hashing. Int J Approx Reason 50(7): 969–978CrossRefGoogle Scholar
  18. Salton G, McGill MJ (1983) Introduction to modern information retrieval. McGraw-Hill, New YorkzbMATHGoogle Scholar
  19. Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11): 613–620CrossRefzbMATHGoogle Scholar
  20. Zhang Z, Hand DJ (2005) Detecting groups of anomalously similar objects in large data sets. In: Proceedings of LNCS. Springer, Heidelberg, pp 509–519Google Scholar
  21. Zhang X, Jing L, Hu X, Ng MK, Jiangxi JX, Zhou X (2008) Medical document clustering using ontology-based term similarity measures. IJDWM 4(1): 62–73Google Scholar

Copyright information

© The Author(s) 2011

Authors and Affiliations

  1. 1.Department of Medical and Molecular GeneticsKing’s CollegeLondonUK
  2. 2.Department of MathematicsImperial CollegeLondonUK
  3. 3.Institute for Mathematical SciencesImperial CollegeLondonUK

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