Abstract
Factor analysis of mixed data (FAMD) is an important statistical technique that not only enables the visualization of large data but also helps to select subgroups of relevant information for a given patient. While such analyses are well-known in the medical domain, they have to satisfy new data governance constraints if reference data is distributed, notably in the context of large consortia developing the coming generation of personalised medicine analyses.
In this paper we motivate the use of distributed implementations for FAMD analyses in the context of the development of a personalised medicine application called KiTapp. We present a new distribution method for FAMD and evaluate its implementation in a multi-site setting based on real data. Finally we study how individual reference data is used to substantiate decision making, while enforcing a high level of usage control and data privacy for patients.
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Sayadi, S., Geffard, E., Südholt, M., Vince, N., Gourraud, PA. (2021). Secure Distribution of Factor Analysis of Mixed Data (FAMD) and Its Application to Personalized Medicine of Transplanted Patients. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_44
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DOI: https://doi.org/10.1007/978-3-030-75100-5_44
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