Longitudinal Missing Data Imputation for Predicting Disability Stage of Patients with Multiple Sclerosis
- URL: http://arxiv.org/abs/2501.12927v1
- Date: Wed, 22 Jan 2025 14:56:19 GMT
- Title: Longitudinal Missing Data Imputation for Predicting Disability Stage of Patients with Multiple Sclerosis
- Authors: Mahin Vazifehdan, Pietro Bosoni, Daniele Pala, Eleonora Tavazzi, Roberto Bergamaschi, Riccardo Bellazzi, Arianna Dagliati,
- Abstract summary: Multiple Sclerosis (MS) is a chronic disease characterized by progressive or alternate impairment of neurological functions.
MS progression is measured through the Expanded Disability Status Scale (EDSS), which quantifies and monitors disability in MS over time.
- Score: 0.34473740271026115
- License:
- Abstract: Multiple Sclerosis (MS) is a chronic disease characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, and cognitive). Predicting disease progression with a probabilistic and time-dependent approach might help in suggesting interventions that can delay the progression of the disease. However, extracting informative knowledge from irregularly collected longitudinal data is difficult, and missing data pose significant challenges. MS progression is measured through the Expanded Disability Status Scale (EDSS), which quantifies and monitors disability in MS over time. EDSS assesses impairment in eight functional systems (FS). Frequently, only the EDSS score assigned by clinicians is reported, while FS sub-scores are missing. Imputing these scores might be useful, especially to stratify patients according to their phenotype assessed over the disease progression. This study aimed at i) exploring different methodologies for imputing missing FS sub-scores, and ii) predicting the EDSS score using complete clinical data. Results show that Exponential Weighted Moving Average achieved the lowest error rate in the missing data imputation task; furthermore, the combination of Classification and Regression Trees for the imputation and SVM for the prediction task obtained the best accuracy.
Related papers
- SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
Sepsis is the leading cause of in-hospital mortality in the USA.
Existing predictive models are usually trained on high-quality data with few missing information.
For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
arXiv Detail & Related papers (2024-07-24T04:47:36Z) - Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs [6.5527554277858275]
We present the first causal temporal framework to model the continuous temporal evolution of disease progression via Neural Differential Equations (NSDE)
Our results present the first successful uncertainty-based causal Deep Learning (DL) model to accurately predict future patient MS disability (e.g. EDSS) and treatment effects.
arXiv Detail & Related papers (2024-06-18T17:22:55Z) - A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics.
We generate high-quality synthetic fMRI data based on user-supplied demographics.
arXiv Detail & Related papers (2024-05-13T17:49:20Z) - Conditional Score-Based Diffusion Model for Cortical Thickness
Trajectory Prediction [29.415616701032604]
Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals.
We propose a conditional score-based diffusion model to generate CTh trajectories with the given baseline information.
Our model has a near-zero bias with narrow confidential 95% interval compared to the ground-truth CTh in 6-36 months.
arXiv Detail & Related papers (2024-03-11T17:26:18Z) - Predicting multiple sclerosis disease severity with multimodal deep
neural networks [10.599189568556508]
We describe a pilot effort to leverage structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient's MS disease severity.
The proposed pipeline demonstrates up to 25% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data.
arXiv Detail & Related papers (2023-04-08T16:23:18Z) - Personalized Longitudinal Assessment of Multiple Sclerosis Using
Smartphones [9.186241234772702]
We design a novel longitudinal model to map individual disease trajectories using sensor data that may contain missing values.
parameters learned from multiple training datasets are ensembled to form a simple, unified predictive model.
The results show that the proposed model is promising to achieve personalized longitudinal MS assessment.
arXiv Detail & Related papers (2022-09-20T12:56:29Z) - To Impute or not to Impute? -- Missing Data in Treatment Effect
Estimation [84.76186111434818]
We identify a new missingness mechanism, which we term mixed confounded missingness (MCM), where some missingness determines treatment selection and other missingness is determined by treatment selection.
We show that naively imputing all data leads to poor performing treatment effects models, as the act of imputation effectively removes information necessary to provide unbiased estimates.
Our solution is selective imputation, where we use insights from MCM to inform precisely which variables should be imputed and which should not.
arXiv Detail & Related papers (2022-02-04T12:08:31Z) - Predicting Parkinson's Disease with Multimodal Irregularly Collected
Longitudinal Smartphone Data [75.23250968928578]
Parkinsons Disease is a neurological disorder and prevalent in elderly people.
Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests.
We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
arXiv Detail & Related papers (2020-09-25T01:50:15Z) - Hemogram Data as a Tool for Decision-making in COVID-19 Management:
Applications to Resource Scarcity Scenarios [62.997667081978825]
COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure.
This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients.
Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
arXiv Detail & Related papers (2020-05-10T01:45:03Z) - Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using
Deep Multiple-Instance Learning [59.74684475991192]
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old.
PD symptoms include tremor, rigidity and braykinesia.
We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
arXiv Detail & Related papers (2020-05-06T09:02:30Z) - Deep Recurrent Model for Individualized Prediction of Alzheimer's
Disease Progression [4.034948808542701]
Alzheimer's disease (AD) is one of the major causes of dementia and is characterized by slow progression over several years.
We propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status.
arXiv Detail & Related papers (2020-05-06T08:08:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.