If you would like to use the algorithms, install the toolbox using pip:
pip install pyebm
The examples to use the toolbox can be found in the jupyter notebook at: link
The event-based model (EBM) for data-driven disease progression modeling estimates the sequence in which biomarkers for a disease become abnormal. This helps in understanding the dynamics of disease progression and facilitates early diagnosis by staging patients on a disease progression timeline. A more accurate and scalable EBM algorithm (Discriminative EBM) was introduced in [1,2]. DEBM was also one of the winners of the TADPOLE prediction challenge (TADPOLE). For more details of the prediction challenge, read Razvan Marinescu et al. [5]. Using DEBM to estimate disease progression timeline in a cohort stratified into several groups was introduced in [6]
Call debm.fit to find the central ordering in a few biomarkers using method [1,2,6]
Call ebm.fit to find the central ordering in a few biomarkers using method [3]
EBM and its variants typically consists of 2 steps.
Step 1: Mixture Model to figure out biomarker distributions in Normal and Abnormal classes
Step 2: Estimating a mean ordering of biomarkers.
This toolbox supports 3 different Gaussian mixture models.
- Algorithm proposed in [2] by Vikram Venkatraghavan et. al. NeuroImage (2019) (default, and shown to be more stable in [2])
- Algorithm proposed in [1] by Vikram Venkatraghavan et. al. IPMI (2017)
- Algorithm proposed in [4] by Alexandra Young et. al. Brain (2014)
Python 3.5+ (or higher), numpy 1.16+, pandas 0.24+, sklearn 0.20+, scipy 1.2+, seaborn 0.9+, statsmodels 0.9+
String to the CSV File where the data is stored. This can also be a Pandas dataframe with necessary data. The CSV file or the dataframe must contain the following fields: PTID (Patient ID), Diagnosis (Clinical Label), Biomarkers, Confounding Factors, EXAMDATE (Date of Examination). See Data_7.csv for example.
Named Tuple with any or all of the following fields:
- MixtureModel - Choose the mixture model algorithm (Options: 'GMMvv1'[1],'GMMvv2'(default)[2], 'GMMay'[4])
- Bootstrap - Number of iterations in the bootstrapping [default - Turned Off].
- PatientStaging - Choose the patient staging algorithm, with a two element list consisting of ['exp'/'ml','p'/'l']. The first element in the list chooses 'ml' for most likely stage[1,3,4] or 'exp' for expected stage[2]. The second element in the list chooses 'l' for likelihood[1,3,4] or 'p' for posterior probability[2]. (['exp','p'] was shown to be the preferred choice in [2])
- (Only in ebm.fit) NStartpoints, Niterations and N_MCMC are algorithm specific parameters for EBM method.
Named Tuple with any or all of the following fields:
- Distributions - plots biomarker distributions [default - Turned Off]
- Ordering - plots the central ordering as a positional variance diagram [default - Turned Off].
- PlotOrder - positional variance diagram has mean positions along the main diagonal [default - Turned Off]. This is used only when Ordering is Turned on.
- WriteBootstrapData - String which specifies the location and name of the files to save the data used in different bootstrap iterations. [default - Turned Off]
- PatientStaging - plots the patient stages of subjects in different classes. [default - Turned Off]
Confounding Factors used for correcting the biomarkers. By Default, it is Age, Sex, ICV (intra-cranial volume)
Clinical list of labels in the dataset. By Default, it is CN, MCI, AD.
If given, DataTest will be used as a test-set to evaluate the disease progression model obtained using DataIn.
A stucture with the following fields:
- BiomarkerList - List of Biomarkers used in EBM
- BiomarkerParameters - Mixture Model parameters for the biomarkers
- CentralOrderings - Central Ordering in different boostrap iterations. When bootstrapping is turned off, this gives the central ordering for the entire dataset.
- MeanCentralOrdering - Mean Central Ordering among different bootstrap iterations. When bootstrapping is turned off, this is the same as CentralOrderings.
- EventCenters - Event centers which determins how close the events are to each other.
A list where each element is a pandas dataframe corresponding to different bootstrap iterations. Each dataframe consists of the the following fields :
- PTID - patient identifiers used in training
- Ordering - Subject-wise orderings of the subjects used for training the model
- Weights - Probabilistic weights for the each position in the subject-wise ordering
- Stages - Staging of each subject in the training dataset.
A list where each element is a pandas dataframe corresponding to different bootstrap iterations. Each dataframe consists of the the following fields:
- PTID - patient identifiers used in testing
- Ordering - Subject-wise orderings of the subjects used for testing the model
- Weights - Probabilistic weights for the each position in the subject-wise ordering
- Stages - Staging of each subject in the testing dataset.
[1] Venkatraghavan V., et al., ‘A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling’, IPMI (2017).
[2] Venkatraghavan V., et al., ‘Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling’, NeuroImage 186, 518 - 532 (2019).
[3] Fonteijn, H.M., et al., ‘An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease’, NeuroImage 60(3), 1880–1889 (2012).
[4] Young, A.L., et al.: ‘A data-driven model of biomarker changes in sporadic Alzheimer’s disease’, Brain 137(9), 2564–2577 (2014).
[5] Marinescu, R. et al.: ‘The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up’.
[6] Venkatraghavan V., et al., ‘Analyzing the effect of APOE on Alzheimer’s disease progression using an event-based model for stratified populations’, NeuroImage 227, 117646 (2021).
