Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2022

Guazzo, Alessandro; Trescato, Isotta; Longato, Enrico; Hazizaj, Enidia; Dosso, Dennis; Faggioli, Guglielmo; Nunzio, Giorgio Maria Di; Silvello, Gianmaria; Vettoretti, Martina; Tavazzi, Erica; Roversi, Chiara; Fariselli, Piero; Madeira, Sara C.; Carvalho, Mamede de; Gromicho, Marta; Chiò, Adriano; Manera, Umberto; Dagliati, Arianna; Birolo, Giovanni; Aidos, Helena; Camillo, Barbara Di; Ferro, Nicola

doi:10.1007/978-3-031-13643-6_25

Cited by 9 publications

(1 citation statement)

References 9 publications

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“…Corrado et al 13 proposed machine learning methods capable of addressing competing risks and censoring for the Intelligent Disease Progression Prediction (IDPP) challenge dataset. 14 These ML techniques produce an average C-index of around 0.70 and 0.74, utilizing data from the first visit and 6 months later, respectively, to predict competing risks such as non-invasive ventilation (NIV), percutaneous endoscopic gastrostomy (PEG) and death. They report 0.86 specificity but low sensitivity for predicting the time of event occurrence.…”

Section: Survival Prediction For Alsmentioning

confidence: 99%

Accurate personalized survival prediction for amyotrophic lateral sclerosis patients

Kuan,

Parnianpour,

Kushol

et al. 2023

Sci Rep

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Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease. Accurately predicting the survival time for ALS patients can help patients and clinicians to plan for future treatment and care. We describe the application of a machine-learned tool that incorporates clinical features and cortical thickness from brain magnetic resonance (MR) images to estimate the time until a composite respiratory failure event for ALS patients, and presents the prediction as individual survival distributions (ISDs). These ISDs provide the probability of survival (none of the respiratory failures) at multiple future time points, for each individual patient. Our learner considers several survival prediction models, and selects the best model to provide predictions. We evaluate our learned model using the mean absolute error margin (MAE-margin), a modified version of mean absolute error that handles data with censored outcomes. We show that our tool can provide helpful information for patients and clinicians in planning future treatment.

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Section: Survival Prediction For Alsmentioning

confidence: 99%