Autoencoders for health improvement by compressing the set of patient features
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10.1109/EMBC44109.2020.9176209
Inclou dades d'ús des de 2022
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hdl:2117/330823
Tipus de documentComunicació de congrés
Data publicació2020
EditorInstitute of Electrical and Electronics Engineers (IEEE)
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Abstract
A challenge to solve when analyzing multimorbidity patterns in elderly people is the management of a high number of characteristics associated with each patient. The main variables to study multimorbidity are diseases, however other variables should be considered to better classify the people included in each pattern. Age, sex, social class and medication are frequently used in the typing of each multimorbidity pattern. Subsequently the cardinality of the set of features that characterize a patient is very high and normally, the set is compressed to obtain a patient vector of new variables whose dimension is noticeably smaller than that of the initial set. To minimize the loss of information by compression, traditionally Principal Component Analysis (PCA) based projection techniques have been used, which although they are generally a good option, the projection is linear, which somehow reduces its flexibility and limits the performance. As an alternative to the PCA based techniques, in this paper, it is proposed to use autoencoders, and it is shown the improvement in the obtained multimorbidity patterns from the compressed database, when the registered data on about a million patients (5 years' follow-up) are processed. This work demonstrates that autoencoders retain a larger amount of information in each pattern and results are more consistent with clinical experience than other approaches frequently found in the literature.Clinical relevance- From an epidemiological perspective, the contribution is relevant, since it allows for a more precise analysis of multimorbidity patterns, leading to better approaches to patient health strategies.
CitacióCabrera-Bean, M. [et al.]. Autoencoders for health improvement by compressing the set of patient features. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "EMBC'20: 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: 20-24 July 2020: Montreal, Canada". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 5917-5920. ISBN 978-1-7281-1991-5. DOI 10.1109/EMBC44109.2020.9176209.
ISBN978-1-7281-1991-5
Versió de l'editorhttps://ieeexplore.ieee.org/document/9176209
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