Fig. 6: Prediction of clinical diagnosis progression with composite biomarkers. | Nature Communications

Fig. 6: Prediction of clinical diagnosis progression with composite biomarkers.

From: A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure

Fig. 6

Data from 1194 ADNI participants with Abeta/pTau measures. a Biomarkers were added successively into features set based an order of accessibility. Concordance Index (CI) measures the performance of Cox-proportional-hazard model in predicting clinical conversion time (from CN to MCI and MCI to Dementia) Different sets of biomarkers are utilized as features of the model for evaluation of their predictive powers. (Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; points, outliers). b Survival curves stratified by composite scores (A, T, Pattern, ADAS-Cog jointly predicting outcome in cross-validated fashion) for one randomly split validation set. 95% confidence intervals are shown with estimated survival curves as centres. (A: Abeta; T: pTau, P: Pattern, Cog: ADAS-Cog score) Source data are provided as a Source Data file.

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