Step 6: Create Model
Mar 25 – Apr 8 | Create Model
Last updated
Mar 25 – Apr 8 | Create Model
Last updated
In this current Step 6 - Create Model, we will leverage the functionalities of the Learning Module to build machine learning models using the learning set obtained from Step 4 - Explore Data. In this step, we'll create two Learning scenes:
Scene 1: Time-Dependent Model Comparison
We aim to assess the impact of patient timelines on model performance, hypothesizing that the performance will increase with time, particularly nearing the last hospital stay. We will compare the best models from the following datasets:
Dataset from the data obtained at the first time point (T1_learning_modified.csv).
Dataset combining data from the first and second time points (T1_learning_modified.csv and T2_learning_modified.csv).
Dataset combining data from the first, second, and third time points (T1_learning_modified.csv, T2_learning_modified.csv, and T3_learning_modified.csv).
Dataset combining data from all time points (T1_learning_modified.csv, T2_learning_modified.csv, T3_learning_modified.csv, and T4_learning_modified.csv).
Scene 2: Variable-Dependent Model Comparison
This scene aims to assess the impact of considered variables on model performance. We will use data from the first two time points (T1_learning_modified.csv and T2_learning_modified.csv), assuming that models involving data from the last time points might make predictions too late in a patient's timeline. We'll compare the best models from the following datasets:
All demographic and time-series data (tslab, tsprocedure, and tschart classes) from T1_learning_modified.csv and T2_learning_modified.csv.
All demographic and notes data (ndischarge and nradiology) from T1_learning_modified.csv and T2_learning_modified.csv.
All demographic and image data from T1_learning_modified.csv and T2_learning_modified.csv.
Selected variables from various data types based on observations made using the first three pipelines, aiming to obtain the best possible model.
These scenes are designed to provide a comprehensive comparison of models under different temporal and variable considerations.
Before proceeding with Step 6 - Create Model of the MEDomicsLab Testing Phase, we recommend consulting the documentation of the Learning Module.
Content
Intro 0:00
First Pipeline 1:09
Explanations about PyCaret 5:37
Scene 1: Time-Dependent Model Comparison 7:35
Scene 2: Variable-Dependent Model Comparison 17:12
You are welcome to use this step to conduct your own experiments and explore the functionalities of the Learning Module. However, please note that there are some missing options and tooltips that we haven't implemented yet, and we intend to address these before Step 8 - Challenge.