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Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality

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Abstract

Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen–Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.

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Notes

  1. Note that Jensen–Shannon distances are not euclidean, hence, compatible clustering methods or euclidean transformations should be used.

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Acknowledgments

The work by C Sáez has been supported by an Erasmus Lifelong Learning Programme 2013 Grant. This work has been supported by own IBIME funds. The authors thank Dr. Gregor Stiglic, from the Univeristy of Maribor, Slovenia, for his support on the NHDS data.

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Authors and Affiliations

  1. Grupo de Informática Biomédica (IBIME), Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain

    Carlos Sáez, Montserrat Robles & Juan M. García-Gómez

  2. Center for Health Technology and Services Research (CINTESIS), Faculdade de Medicina da, Universidade do Porto, Rua Dr. Plácido da Costa s/n, 4200-450, Porto, Portugal

    Carlos Sáez & Pedro Pereira Rodrigues

  3. Laboratório de Inteligência Artificial e Apoio à Decisão (LIAAD)-INESC, Universidade do Porto, Porto, Portugal

    Pedro Pereira Rodrigues & João Gama

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  1. Carlos Sáez
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  2. Pedro Pereira Rodrigues
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  3. João Gama
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  4. Montserrat Robles
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  5. Juan M. García-Gómez
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Correspondence to Carlos Sáez.

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Responsible editors: Fei Wang, Gregor Stiglic, Ian Davidson and Zoran Obradovic.

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Sáez, C., Rodrigues, P.P., Gama, J. et al. Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Min Knowl Disc 29, 950–975 (2015). https://doi.org/10.1007/s10618-014-0378-6

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