Individual Patient Outcome Prediction
Table of Contents
Making individual outcome predictions is the basic AI for healthcare task. We need to specify the target response to predict, e.g., mortality , readmission , length of stay , transform the input trial patient records to either static descriptive features where is the number of features or sequential event features where is the number of visits, then predict the target using the processed data.
Such that, depending on the input patient data format: tabular or sequence, we have the following two subtasks: indiv_outcome.tabular
and indiv_outcome.sequence
.
Tabular Patient: Index
Here is the list of colab examples on each model for this task.
Sequential Patient: Index
Here is the list of colab examples on each model for this task.
Tabular: Example
Here, we highlight the usage of indiv_outcome.tabular.transtab
model for this task. Besides learning and predicting on a single tabular dataset, transtab
shows promising performances
on learning across different datasets. It sheds light on training a fundational model for clinical trials.
Sequence: Example
Here, we highlight the usage of indiv_outcome.sequence.rnn
model for this task. Other more advanced models have the similar pipeline.