Accurate personalized survival prediction for amyotrophic lateral sclerosis patients

Abstract

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.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

“…A multiparametric set of variables (including the predictive value of microstructural integrity) could be used for association with clinical phenotype features like cognitive performance ( Power et al, 2019 ), the improvement of diagnostic accuracy ( Piersson et al, 2021 ), or to assess the predictive value on survival ( Agosta et al, 2019 ; Kuan et al, 2023 ) in NDD.…”

“…Thus, ML and AI are exponentially improving medical imaging and diagnosis: by ML techniques (especially SVM or RFM), the combination of multiparametric/multimodal imaging data allow for a multidimensional analysis beyond (simple) association analysis ( Tae et al, 2018 ; Weston et al, 2023 ; Takahashi et al, 2024 ). Moreover, CNNs might enable estimations of the risk status, and thus, prediction of the disease course including survival in NDDs ( Agosta et al, 2019 ; Kuan et al, 2023 ). Altogether, it seems safe to expect that ML methods will help to define the future of DTI-based analysis of WM integrity in the brain and its affectations by NDD.…”

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

“…Heart diseases, such as coronary artery disease, myocardial infarction, and congestive heart failure, are the most common and life-threatening of these disorders. The frequency of these diseases highlights the critical need for accurate and prompt diagnosis in order to enhance patient treatment and public health outcomes 1,2,3,4,5,6,7,8,9,10 . Medical surveys have been an important source of data for cardiovascular research, allowing for the gathering of a wide range of health indicators, lifestyle habits, genetic variables, and patients’ past medical records 11,12 .…”