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Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

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Abstract

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

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Acknowledgments

The authors would like to thank FAPESP and RUSP for the financial support. The authors would also like to acknowledge Professor Evandro Ruiz, Gisele Miranda, Professor Joaquim Felipe, Renato Pedigoni, Professor Rudinei Goularte, Thiago da Costa, and everyone at Vila Lobato.

Conflict of interest statement

All authors declare that they have no conflicts of interest.

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Authors and Affiliations

  1. Department of Computer Science and Mathematics, FFCLRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil, 14040-901

    Juliana T. Pollettini, Renato Tinós, José A. Baranauskas & Alessandra A. Macedo

  2. Department of Pediatrics, FMRP, University of São Paulo, Ribeirão Preto, São Paulo, Brazil, 14049-900

    Sylvia R. G. Panico & Julio C. Daneluzzi

  3. Av. Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo, Brazil, CEP 14040-90

    Alessandra A. Macedo

Authors
  1. Juliana T. Pollettini
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  2. Sylvia R. G. Panico
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  3. Julio C. Daneluzzi
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  4. Renato Tinós
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  5. José A. Baranauskas
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  6. Alessandra A. Macedo
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Corresponding author

Correspondence to Alessandra A. Macedo.

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Pollettini, J.T., Panico, S.R.G., Daneluzzi, J.C. et al. Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records. J Med Syst 36, 3861–3874 (2012). https://doi.org/10.1007/s10916-012-9859-6

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  • DOI: https://doi.org/10.1007/s10916-012-9859-6

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