機械学習の実装の取り込みに基づく改善された低血糖症予測のための血糖データセットの最適化 (2024)
The invention relates to a method for data set expansion for improved hypoglycaemia prediction based on classifier ingestion, and comprises the steps of: providing a raw data set for a subject, the data set comprising a plurality of BG values obtained at a given sampling rate and thereto associated time stamps over a plurality of days N, and performing data transformation by rolling scheme temporal binning of evaluation block values (eHH) as input X to create corresponding prediction values (pHH) as output Y, wherein X is created as a sliding window comprising BG values for a given past period of time T−p, and wherein Y is created as an indicator I indicating whether or not a BG value at a given future time T−f is below a given threshold indicative of a hypoglycaemic condition.