Modeling and inference regression model future alsfrs

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Unformatted text preview: 38 37 36 0 0.5 1 1.5 2 Months 2.5 3 3.5 Featurizing Time Series Data ALSFRS Score 40 slope 0 slope  ­1 39 38 slope  ­2 37 36 0 0.5 1 1.5 Months 2 2.5 3 3.5 Featurizing Time Series Data Deriva;ve ;me series ALSFRS ALSFRS Score 40 Slope 0 slope 0 slope  ­1 39  ­0.5  ­1 38  ­1.5 slope  ­2 37  ­2 36  ­2.5 0 0.5 1 1.5 2 Months 2.5 3 3.5 Featurizing Time Series Data Deriva;ve ;me series ALSFRS ALSFRS Score 40 Slope 0 slope 0 slope  ­1 39  ­0.5  ­1 38  ­1.5 slope  ­2 37  ­2 36  ­2.5 0 0.5 1 1.5 2 Months 2.5 3 3.5 Featurizing Time Series Data ALSFRS Score 40 Deriva;ve ;me series ALSFRS Slope 0 Features  ­0.5 extracted Mean =  ­1  ­1 SD = 1 Max = 0 Min =  ­2  ­1.5 Last =  ­2 Slope =  ­0.5  ­2 etc. 39 38 37 36  ­2.5 0 0.5 1 1.5 2 Months 2.5 3 3.5 Featurizing Time Series Data   435 temporal features extracted   Problem: Missing data •  Average pa-ent missing 10% of features Room for •  One pa-ent missing 55% of features! improvement •  Missing values imputed using median heuris-c   Problem: Outliers •  Nonsense values: Number of liters recorded as MDMD •  Units incorrectly recorded ⇒ Wrong conversions •  Extreme values   Treated as missing if > 4 standard devia-ons from mean Open Ques;on: Regression robust to (sparse) covariate outliers? Modeling and...
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