TY - JOUR
T1 - Predictive models in SMA II natural history trajectories using machine learning: A proof of concept study
AU - Coratti, Giorgia
AU - Lenkowicz, Jacopo
AU - Patarnello, Stefano
AU - Gullì, Consolato
AU - Pera, Maria Carmela
AU - Masciocchi, Carlotta
AU - Rinaldi, Riccardo
AU - Lovato, Valeria
AU - Leone, Antonio
AU - Cesario, Alfredo
AU - Mercuri, Eugenio Maria
PY - 2022
Y1 - 2022
N2 - It is known from previous literature that type II Spinal Muscular Atrophy (SMA) patients generally, after the age of 5 years, presents a steep deterioration until puberty followed by a relative stability, as most abilities have been lost. Although it is possible to identify points of slope indicating early improvement, steep decline and relative stabilizations, there is still a lot of variability within each age group and it’s not always possible to predict individual trajectories of progression from age only. The aim of the study was to develop a predictive model based on machine learning using an XGBoost algorithm for regression and report, explore and quantify, in a single centre longitudinal natural history study, the influence of clinical variables on the 6/12-months Hammersmith Motor Functional Scale Expanded score prediction (HFMSE). This study represents the first approach to artificial intelligence and trained models for the prediction of individualized trajectories of HFMSE disease progression using individual characteristics of the patient. The application of this method to larger cohorts may allow to identify different classes of progression, a crucial information at the time of the new commercially available therapies.
AB - It is known from previous literature that type II Spinal Muscular Atrophy (SMA) patients generally, after the age of 5 years, presents a steep deterioration until puberty followed by a relative stability, as most abilities have been lost. Although it is possible to identify points of slope indicating early improvement, steep decline and relative stabilizations, there is still a lot of variability within each age group and it’s not always possible to predict individual trajectories of progression from age only. The aim of the study was to develop a predictive model based on machine learning using an XGBoost algorithm for regression and report, explore and quantify, in a single centre longitudinal natural history study, the influence of clinical variables on the 6/12-months Hammersmith Motor Functional Scale Expanded score prediction (HFMSE). This study represents the first approach to artificial intelligence and trained models for the prediction of individualized trajectories of HFMSE disease progression using individual characteristics of the patient. The application of this method to larger cohorts may allow to identify different classes of progression, a crucial information at the time of the new commercially available therapies.
KW - Artificial Intelligence
KW - Child, Preschool
KW - Humans
KW - Machine Learning
KW - Muscular Atrophy, Spinal
KW - Proof of Concept Study
KW - Spinal Muscular Atrophies of Childhood
KW - Artificial Intelligence
KW - Child, Preschool
KW - Humans
KW - Machine Learning
KW - Muscular Atrophy, Spinal
KW - Proof of Concept Study
KW - Spinal Muscular Atrophies of Childhood
UR - http://hdl.handle.net/10807/210782
U2 - 10.1371/journal.pone.0267930
DO - 10.1371/journal.pone.0267930
M3 - Article
SN - 1932-6203
VL - 17
SP - N/A-N/A
JO - PLoS One
JF - PLoS One
ER -