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Data Mining in Establishing the Indirect Reference Intervals of Biochemical and Haematological Assays in the Paediatric Population: A Review

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Advances in Visual Informatics (IVIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14322))

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Abstract

Reference intervals (RIs) are fundamental values accompanying medical laboratory results that allow interpretation by medical practitioners, thus influencing patient management. Traditionally, RIs are established by recruiting 120 healthy reference individuals and applying statistical analysis to the results. This method is challenging due to the technical and ethical issues involved. Therefore, many laboratories either adapt RIs provided by the manufacturers of their analytical platforms or the results of RI studies done in other countries. The advent of data mining technology has allowed an alternative method, the indirect RIs (IRIs) approach, which applies appropriate statistical techniques to patient data stored in the laboratory electronic medical records to establish the IRIs. This review briefly highlights the historical aspect of IRI determination, provides a general outline of the steps involved and reviews publications that have used data mining to establish the paediatric IRI over the past ten years.

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Author information

Authors and Affiliations

  1. Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Dian N. Nasuruddin, Ely Salwana & Mahidur R. Sarker

  2. Paediatrics Department, Pusat Perubatan Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia

    Adli Ali

  3. Department of Laboratory Medicine, National University Hospital, Lower Kent Ridge Road, Singapore

    Tze Ping Loh

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  1. Dian N. Nasuruddin
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  2. Ely Salwana
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  3. Mahidur R. Sarker
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  4. Adli Ali
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  5. Tze Ping Loh
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Correspondence to Dian N. Nasuruddin .

Editor information

Editors and Affiliations

  1. Universiti Tenaga Nasional, Kajang, Selangor, Malaysia

    Halimah Badioze Zaman

  2. University of Cambridge, Cambridge, UK

    Peter Robinson

  3. Dublin City University, Dublin, Ireland

    Alan F. Smeaton

  4. MIT Sloan School of Management, Asia School of Business, Cambridge, MA, USA

    Renato Lima De Oliveira

  5. University of Southern Denmark, Odense, Denmark

    Bo Nørregaard Jørgensen

  6. National Central University, Jhongli, Taiwan

    Timothy K. Shih

  7. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Rabiah Abdul Kadir

  8. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Ummul Hanan Mohamad

  9. Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Mohammad Nazir Ahmad

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Nasuruddin, D.N., Salwana, E., Sarker, M.R., Ali, A., Loh, T.P. (2024). Data Mining in Establishing the Indirect Reference Intervals of Biochemical and Haematological Assays in the Paediatric Population: A Review. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2023. Lecture Notes in Computer Science, vol 14322. Springer, Singapore. https://doi.org/10.1007/978-981-99-7339-2_41

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