Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data

• URL: http://arxiv.org/abs/2112.14314v1
• Date: Tue, 28 Dec 2021 22:23:08 GMT
• Title: Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data
• Authors: Sharath Koorathota, Arunesh Mittal, Richard P. Sloan, Paul Sajda
• Abstract summary: We used data from an observational, cohort study, Midlife in the United States (MIDUS) to model executive function and episodic memory measures. Deep neural network (DNN) models consistently ranked highest in all of the cognitive performance prediction tasks.
• Score: 2.867517731896504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cognition in midlife is an important predictor of age-related mental decline and statistical models that predict cognitive performance can be useful for predicting decline. However, existing models struggle to capture complex relationships between physical, sociodemographic, psychological and mental health factors that effect cognition. Using data from an observational, cohort study, Midlife in the United States (MIDUS), we modeled a large number of variables to predict executive function and episodic memory measures. We used cross-sectional and longitudinal outcomes with varying sparsity, or amount of missing data. Deep neural network (DNN) models consistently ranked highest in all of the cognitive performance prediction tasks, as assessed with root mean squared error (RMSE) on out-of-sample data. RMSE differences between DNN and other model types were statistically significant (T(8) = -3.70; p < 0.05). The interaction effect between model type and sparsity was significant (F(9)=59.20; p < 0.01), indicating the success of DNNs can partly be attributed to their robustness and ability to model hierarchical relationships between health-related factors. Our findings underscore the potential of neural networks to model clinical datasets and allow better understanding of factors that lead to cognitive decline.

Related papers

• Cognitive Networks and Performance Drive fMRI-Based State Classification Using DNN Models [0.0]
  We employ two structurally different and complementary DNN-based models to classify individual cognitive states. We show that despite the architectural differences, both models consistently produce a robust relationship between prediction accuracy and individual cognitive performance.
  arXiv  Detail & Related papers  (2024-08-14T15:25:51Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Towards a Transportable Causal Network Model Based on Observational Healthcare Data [1.333879175460266]
  We propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability.
  arXiv  Detail & Related papers  (2023-11-13T13:23:31Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features [119.45320143101381]
  Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks. We have studied GCNs with covariance matrices as graphs in the form of coVariance neural networks (VNNs) VNNs inherit the scale-free data processing architecture from GCNs and here, we show that VNNs exhibit transferability of performance over datasets whose covariance matrices converge to a limit object.
  arXiv  Detail & Related papers  (2023-05-02T22:15:54Z)
• Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability [15.078314022161237]
  We explore the relationship between interpretability and data augmentation strategy in which models are trained. Experiments show that models trained with mixed sample data augmentation show lower interpretability.
  arXiv  Detail & Related papers  (2023-03-26T03:01:39Z)
• Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data [65.28160163774274]
  We apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.
  arXiv  Detail & Related papers  (2022-11-09T14:48:13Z)
• Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series [4.125698836261585]
  We present the Interpretable-RNN (I-RNN) that balances model complexity and accuracy by forcing the relationship between variables in the model to be additive. I-RNN specifically captures the unique characteristics of clinical time series, which are unevenly sampled in time, asynchronously acquired, and have missing data. We evaluate the I-RNN model on the Physionet 2012 Challenge dataset to predict in-hospital mortality, and on a real-world clinical decision support task: predicting hemodynamic interventions in the intensive care unit.
  arXiv  Detail & Related papers  (2021-09-15T22:30:19Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Hi-CI: Deep Causal Inference in High Dimensions [23.588253984635987]
  We propose Hi-CI, a deep neural network (DNN) based framework for estimating causal effects. We demonstrate the efficacy of causal effect prediction of the proposed Hi-CI network using synthetic and real-world NEWS datasets.
  arXiv  Detail & Related papers  (2020-08-22T15:41:59Z)

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.