Pubblicazioni

Autori:

Golovco, Stela; Mantovani, Matteo; Combi, Carlo; Holmes, John H.

Titolo:

Acute Kidney Injury Prediction with Gradient Boosting Decision Trees enriched with Temporal Features

Anno:

2022

Tipologia prodotto:

Contributo in atti di convegno

Tipologia ANVUR:

Contributo in Atti di convegno

Lingua:

Inglese

Formato:

Elettronico

Titolo del Convegno:

2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)

Luogo:

Rochester, MN, USA

Periodo:

11-14 June 2022

ISBN:

978-1-6654-6845-9

Intervallo pagine:

669-676

Parole chiave:

AKI , KDIGO , MIMIC , temporal windows , prediction windows , machine learning , trend-event features

Breve descrizione dei contenuti:

This paper aims to predict the risk of Acute Kidney Injury (AKI) in intensive care units (ICUs) using machine learning techniques and statistical approaches. The data used in the study are derived from Medical Information Mart for Intensive Care (MIMIC) III, which is a freely accessible database of de-identified ICU-related data. The paper focuses on two different phases. The first one consists of a scrupulous phase of extraction and transformation of MIMIC data to retrieve all the criteria specified in the Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline definition of AKI. The main features included are demographics, medications, comorbidities, charted vital signs, and laboratory events. In the second phase, we used several different techniques already used to predict AKI, and we also added a complex temporal feature, called Trend-Event Feature (TE-F). The prediction was performed using a rolling observational window design that includes the data collection window (length: 1 to 6 days) and the prediction window (7 days). The Gradient Boosting Decision Trees (GBDT) method was used, with different groups of features, to predict the risk of AKI. To evaluate the GBDT performances, we used the area under the ROC curve (AUROC), Recall, Precision, and F-score measures. We observed that lab parameters contribute the most to the prediction of the AKI risk. Moreover, adding TE-Fs to different groups of features leads to better performance results.

Id prodotto:

129504

Handle IRIS:

11562/1074186

ultima modifica:

22 settembre 2024

Citazione bibliografica:

Golovco, Stela; Mantovani, Matteo; Combi, Carlo; Holmes, John H., Acute Kidney Injury Prediction with Gradient Boosting Decision Trees enriched with Temporal Features  in 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI) , Atti di "2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)" , Rochester, MN, USA , 11-14 June 2022 , 2022 , pp. 669-676

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