Site Risk prediction for Drug Discovery

Problem Statement

A leading Healthcare industry in the US was looking for

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Avoid Millions of Dollars lost due to Adverse Event (AE) delayed reporting
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Forecast AE reporting risk at site level with the predefined list of clinical studies
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Reduce cost for performing SDV (Site Data Verification). Currently SDV is performed on 100% of sites resulting in huge cost.
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Achieve a better prediction model than what is available in the market through H2O.AI, DataRobot and RapidMiner (current accuracy – ~72%)

Solution

Sentienz proposed a prediction model by performing the following…

  • Feature analysis – Resulting in the best feature list to create the prediction model by performing advanced feature engineering.
  • Created detailed models and analyzed results and outputs, to evaluate and re-evaluate the metrics and identify optimum outcomes
  • Stacked Ensemble model combining various Deep learning (DL) and Machine learning (ML) Models
  • Principal Component analysis to understand the spread of training and test data.
  • Provide explanation of how the predictions are done listing top 10 explanations along with their impact rating.
  • Applied Deep Learning models with complex network architectures.
  • Performed detailed False Positive (FP) and False Negative (FN) analysis to maximize recall / sensitivity, allowing for maximum accuracy with a minimal impact on the business.

Actions

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Implemented Streamsets

For ingesting patient data to data platform
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Implemented DataRobot and H20 for generating Base AI and ML models

  • Using the training dataset the Base Models for improved
  • Ensemble of Models were used to improve the accuracy of the results

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Implemented Streamsets

For ingesting patient data to data platform
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Implemented DataRobot and H20 for generating Base AI and ML models

  • Using the training dataset the Base Models for improved
  • Ensemble of Models were used to improve the accuracy of the results

Business Benefits

  • 85%
    Accuracy
  • 90%
    TPR
  • 88%
    AUC
  • 60%
    Site Data Verification cost reduced

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