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DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS.

Zexi Cai1, Donglin Zeng2, Karen S Marder3

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View abstract on PubMed

Summary
This summary is machine-generated.

Predicting disease progression is difficult without true disease states. This study introduces a new model combining generative and discriminative approaches, improving Alzheimer's disease (AD) and Lewy body dementia (LBD) distinction.

Keywords:
Alzheimer’s diseaseDisease progressionGenerative-Discriminative modelHidden Markov modelLatent states

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Area of Science:

  • Biostatistics
  • Neurology
  • Machine Learning

Background:

  • Disease progression prediction is challenging due to unknown true disease states and diagnostic limitations.
  • Distinguishing Alzheimer's disease (AD) from related dementias like Lewy body dementia (LBD) is difficult without gold-standard diagnoses.
  • Existing models often make unrealistic assumptions or suffer from misspecification when using surrogate labels and health markers.

Purpose of the Study:

  • To develop a novel statistical model for disease progression prediction using surrogate labels and health markers.
  • To improve the accuracy of distinguishing between AD and LBD by addressing limitations of existing generative models.
  • To integrate generative and discriminative approaches for robust disease state modeling.

Main Methods:

  • Proposed a hybrid model integrating a hidden Markov model (generative) with a time-varying discriminative classification model.
  • Developed an adaptive forward-backward algorithm with subjective labels for estimation.
  • Utilized modified posterior and Viterbi algorithms for future state prediction using objective markers.

Main Results:

  • The proposed adaptive method eliminates the need to model the marginal distribution of longitudinal markers.
  • Simulation studies demonstrated significant improvements in finite samples compared to traditional algorithms.
  • Analysis of the National Alzheimer's Coordinating Center (NACC) dataset showed improved accuracy in distinguishing LBD from AD.

Conclusions:

  • The integrated model effectively handles potentially misspecified surrogate labels and incorporates disease progression markers.
  • The novel approach offers a more robust and accurate method for disease progression prediction, particularly in complex neurological conditions.
  • This method enhances diagnostic accuracy for conditions like LBD versus AD, aiding clinical decision-making.