Globally 264 million people suffer from depression. Depression and anxiety cost $1 trillion each year in lost productivity. Here we are proposing the prediction of onset of depression using commercial medical claims data. We are using 2297 medical indicators for the 2 years of observation window and predicting depression risk probability for one year of prediction window. We developed a CNN + BiLSTM + Attention neural network architecture and trained it on 7.2 million patients' claims. This and another previous study suggests that medical claims data holds information to predict depression. Using the medical claims dataset predicts depression is an inexpensive method and a good representative of the population due to its ease of gathering and ubiquity. 

The structure of the talk would be as follows: 

  • Domain background
  • Data used and how it is architected for modeling
  • Network architecture; Sparse multidimensional time-series for binary class classification
  • Evaluation techniques
  • Conclusion; Future work and other application of the work

Instructor's Bio

Behlool Sabir, Lead Data Scientist at Fidelity Investments

Behlool is a data scientist at Fidelity Investments. He holds a master’s degree in Physics and has more than 7 years of industry experience in AI/ML. He is currently focusing on predictive analytics in the healthcare space. Interest & experience spans from research publications to business implementations and deployment. Experienced in working in Agile (Scrum) project management framework.
Currently enjoying as a Lead Data Scientist in an AI CoE. Solving the firm’s most stubborn problems using data science. Responsible for understanding business requirements, architecting and developing the AI systems and pushing it for production.


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    Depression Onset Prediction on a Large Medical Claims Data Using Deep Learning

    • Webinar Link