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This work was supported by Instituto de Salud Carlos III, Spain (ISCIII) [project PI21/00064] and co-funded by the European Union; Universitat Rovira i Virgili (URV) [projects number 2023PFR-URV-114, 2022PFR-URV-41]; ITAKA funding from AGAUR [2021-SGR-00114]; and the first author had a pre-doctoral FI grant from Generalitat de Catalunya and Fons Social Europeu [grant number 2022 FI_B1 00036].

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Multivariate data binning and examples generation to build a Diabetic Retinopathy classifier based on temporal clinical and analytical risk factors

Publicat a:Knowledge-Based Systems. 300 112154- - 2024-09-27 300(), DOI: 10.1016/j.knosys.2024.112154

Autors: Pascual-Fontanilles, Jordi; Valls, Aida; Romero-Aroca, Pedro

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In this paper, we explore the possibility of exploiting retrospective clinical data from Electronic Health Records (EHR) for classification tasks in chronic patients. The different intervals, short length and high class imbalance make it unfeasible to use traditional time series techniques. The first contribution of the paper is a preprocessing method to construct a multivariate time series dataset using EHR data, which infers missing data and regularizes the data frequency. The second contribution addresses class imbalance by using domain knowledge and existing short EHR series. We synthetically extrapolate patients' data by using similar long time series and a fuzzy-based approach. The paper addresses the problem of detection of Diabetic Retinopathy (DR). Expert domain knowledge from ophthalmologists has been used in the proposed techniques to guide the processing of time series. The novelty in that case study consists in not using eye-fundus image analysis. Instead, the proposed methods are based solely on EHR data. Several multivariate multiclass time series classifiers are used to detect the four levels of DR severity from the pre -processed data sequences. Experiments prove the quality of the sequence preprocessing techniques proposed for EHR data. Results indicate that the TapNet classifier is the best one for DR grading. Despite being tested for DR detection, the proposed data preparation methods are applicable to other diseases with similar characteristics.

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