Explainable models of disease progression in ALS: Learning from longitudinal clinical data with recurrent neural networks and deep model explanation
Background and Objectives Deep neural networks recently become a popular tool in medical research to predict disease progression and unveil its underlying temporal phenotypes. While being well suited to study longitudinal clinical data and learn disease progression models, its application in clinica...
Main Authors: | Marcel Müller, Marta Gromicho, Mamede de Carvalho, Sara C. Madeira |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-01-01
|
Series: | Computer Methods and Programs in Biomedicine Update |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666990021000173 |
Similar Items
-
Prediction of survival in amyotrophic lateral sclerosis: a nationwide, Danish cohort study
by: Anne-Lene Kjældgaard, et al.
Published: (2021-04-01) -
Combinatory Biomarker Use of Cortical Thickness, MUNIX, and ALSFRS-R at Baseline and in Longitudinal Courses of Individual Patients With Amyotrophic Lateral Sclerosis
by: Anna M. Wirth, et al.
Published: (2018-07-01) -
Mountain Ginseng Pharmacopuncture Treatment on Three Amyotrophic Lateral Sclerosis Patients -Case Report-
by: Ryu Young-jin, et al.
Published: (2010-12-01) -
How COVID-19 pandemic changed our management strategies for amyotrophic lateral sclerosis (ALS) patients: Egyptian study
by: Hebatallah R. Rashed
Published: (2021-10-01) -
Amyotrophic lateral sclerosis: combined nutritional, respiratory and functional assessment Esclerose lateral amiotrófica: correlações dos indicadores da avaliação nutricional, funcional e respiratória
by: Luciano Bruno de Carvalho Silva, et al.
Published: (2008-06-01)