Identifying and Predicting Parkinson's Disease Subtypes through Trajectory Clustering via Bipartite Networks

06/12/2019
by   Sanjukta Krishnagopal, et al.
0

Parkinson's disease (PD) is a common neurodegenerative disease with a high degree of heterogeneity in its clinical features, rate of progression, and change of variables over time. In this work, we present a novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of PD subtypes and 2) early prediction of disease progression in individual patients. Our subtype identification is based not only on PD variables, but also on their complex patterns of progression, providing a useful tool for the analysis of large heterogenous, longitudinal data. Specifically, we cluster patients based on the similarity of their trajectories through a time series of bipartite networks connecting patients to demographic, clinical, and genetic variables. We apply this approach to demographic and clinical data from the Parkinson's Progression Markers Initiative (PPMI) dataset and identify 3 patient clusters, consistent with 3 distinct PD subtypes, each with a characteristic variable progression profile. Additionally, TPC predicts an individual patient's subtype and future disease trajectory, based on baseline assessments. Application of our approach resulted in 74 Furthermore, we show that genetic variability can be integrated seamlessly in our TPC approach. In summary, using PD as a model for chronic progressive diseases, we show that TPC leverages high-dimensional longitudinal datasets for subtype identification and early prediction of individual disease subtype. We anticipate this approach will be broadly applicable to multidimensional longitudinal datasets in diverse chronic diseases.

READ FULL TEXT

page 7

page 9

research
12/03/2018

Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data

Alzheimer's disease (AD) is a degenerative brain disease impairing a per...
research
08/16/2016

Scalable Modeling of Multivariate Longitudinal Data for Prediction of Chronic Kidney Disease Progression

Prediction of the future trajectory of a disease is an important challen...
research
03/02/2018

Clinically Meaningful Comparisons Over Time: An Approach to Measuring Patient Similarity based on Subsequence Alignment

Longitudinal patient data has the potential to improve clinical risk str...
research
03/17/2022

DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Clinical Applications

The ability to predict the future trajectory of a patient is a key step ...
research
03/13/2018

A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome

In this work, we consider the problem of predicting the course of a prog...
research
09/29/2021

Stroke recovery phenotyping through network trajectory approaches and graph neural networks

Stroke is a leading cause of neurological injury characterized by impair...
research
11/28/2021

Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization

Precision medicine is a clinical approach for disease prevention, detect...

Please sign up or login with your details

Forgot password? Click here to reset