MEDomicsLab-docs
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  • Recommendations
  • Instructions for Step 7 - Evaluate & Apply Model
  1. Testing Phase with MIMIC

Step 7: Evaluate & Apply Model

Apr 8 – Apr 22 | Evaluate & Apply Model

PreviousStep 6: Create ModelNextStep 8: Challenge

Before proceeding to Step 7 - Evaluate & Apply Model, you will need to download the models folder (MEDomicsLab_TestingPhase_Step7.zip) into your EXPERIMENTS folder. This folder contains the models we prepared for you. One of the models corresponds to the model you created during . However, for consistency across all participants of the Testing Phase, we recommend using the model we provided specifically for Step 7 - Evaluate & Apply Model.

Additionally, for this step, you will also need the data we sent you for (MEDomicsLab_TestingPhase_Step6.zip), which contains the holdout patient set we will use to evaluate our models.

An invitation to access the MEDomicsLab_TestingPhase_Step7.zip data has been sent to you via email.

Model 1: ExtraTrees Classifier

  • tslab_|_attr_MCHC__maximum_T1

  • nradiology_|_attr4_T1

  • nradiology_|_attr6_T1

  • image_|_attr5(3)_T1

  • image_|_attr7(3)_T1

  • demographics_|_anchor_age_T1

  • nradiology_|_attr4_T2

  • nradiology_|_attr6_T2

  • tslab_|_attr_Platelet_Count__mean_T2

  • tslab_|_attr_MCHC__maximum_T2

  • tslab_|_attr_MCH__maximum_T2

  • image_|_attr5(3)_T2

  • image_|_attr7(3)_T2

Model 2: Random Forest Classifier

We created this model specifically for this step. Like the previous model, it was trained on Time point 1 and Time point 2 from the learning set, using the same columns.

Apply Model

This section involves selecting a model and applying it to a new patient. You will need to enter the patient's required information, based on the columns the model was trained on, and observe the prediction the model makes for this new patient.

Recommendations

Instructions for Step 7 - Evaluate & Apply Model

Content

In this current Step 7 - Evaluate & Apply Model, we will explore the functionalities of the by evaluating two machine learning models on our holdout set. As the models were created using only Time point 1 and Time point 2 from the learning set (see for more details), we are going to evaluate them on Time point 1 and Time point 2 from the holdout set.

Additionally, we will explore the functionalities of the by applying one of the models to a single patient from the holdout set.

Documentation related to this model is available . In our experiment, we kept the model's default values.

This model is the one we obtained at the end of . It is trained on Time point 1 and Time point 2 from our learning set, using the following columns (T1 and T2 suffix refers to T1 and T2 datasets):

Documentation related to this model is available . In our experiment, we kept the model's default values.

Please note that the dashboard is a basic implementation of the . Additionally, if you are seeking information about the dashboard elements, you may find it in the .

Also, if you want to fully understand how ExplainerDashboard works in the background, it is an open-source library, and the code is available on .

Before proceeding with Step 7 - Evaluate & Apply Model of the MEDomicsLab Testing Phase, we recommend consulting the documentation of the and the .

Intro

Evaluate 1st model

Evaluate 2nd model

Apply model

📄
Evaluation Module
Step 6 - Create Model
Application Module
here
Step 6 - Create Model
here
Evaluation Module
ExplainerDashboard Python library
ExplainerDashboard documentation
GitHub
Evaluation Module
Application Module
Evaluation Module
Application Module
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Step 6 - Create Model
Step 6 - Create Model
Step 7 - Evaluate & Apply Model