Commit 4cb7801f authored by Shengpu Tang (tangsp)'s avatar Shengpu Tang (tangsp)
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parent 7f800c92
......@@ -5,13 +5,13 @@ FIDDLE – <b>F</b>lex<b>I</b>ble <b>D</b>ata-<b>D</b>riven pipe<b>L</b>in<b>E</
Required packages and versions are listed in `requirements.txt`. Older versions may still work but have not been tested.
## Usage Notes
FIDDLE generates feature vectors based on data within the observation period $t\in[0,T]$. This feature representation can be used to make predictions of adverse outcomes at t=T. More specifically, FIDDLE outputs a set of binary feature vectors for each example $i$, $\{(s_i,x_i)\ \text{for}\ i=1 \dots N\}$ where $s_i \in R^d$ contains time-invariant features and $x_i \in R^{L \times D}$ contains time-dependent features.
FIDDLE generates feature vectors based on data within the observation period $`t\in[0,T]`$. This feature representation can be used to make predictions of adverse outcomes at t=T. More specifically, FIDDLE outputs a set of binary feature vectors for each example $`i`$, $`\{(s_i,x_i)\ \text{for}\ i=1 \dots N\}`$ where $`s_i \in R^d`$ contains time-invariant features and $`x_i \in R^{L \times D}`$ contains time-dependent features.
Input:
- formatted EHR data, `.csv` or `.p`/`.pickle` files, table with 4 columns: \[`ID`, `t`, `variable_name`, `variable_value`\]
- population file: a list of unique `ID`s you want processed
- arguments:
- T: the prediction time. Time-dependent features will be generated using data in $t\in[0,T]$.
- T: the prediction time. Time-dependent features will be generated using data in $`t\in[0,T]`$.
- dt: the temporal granularity at which to "window" time-dependent data.
- theta_1
- theta_2
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