The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

Deep Diagnosis:How is Deep Learning Impacting Medical Domain and Saving Lives

Submitted by Raghav Bali (@baliraghav) on May 31, 2019

Session type: Full talk of 40 mins Status: Rejected



The field of Deep Learning is making huge inroads in almost all spheres. What caught the world by a storm, surpassing human level performance with image classification, has today matured into a powerful tool to solve real-world problems. Today, Deep Learning is not just a research area limited to academics but a powerful tool utilized and improved by different companies/labs/institutions across the world.

Medical domain is no exception when it comes to utilizing Deep Learning and Machine Learning algorithms to solve issues. Some of the recent research work involves using such techniques to surpass doctors in identifying heart failures, identifying tumours, bone fractures, etc.

Medical domain has a ton of peripheral issues which are important and need to be addressed before tools like Deep Learning can be leveraged in the real world. There are many issues like privacy, efficacy, correctness, completeness/bias and so on. The issues are both technical as well as social in nature. Deep Learning models have largely been black-boxes which JUST work. It isn’t magic, but the theory behind their success has been fuzzy, until now. These models are so complex that it makes them difficult to understand. Thus, impacting their utilization in real-world medical scenarios.

In this talk, we would showcase how we utilize a deep learning model and overcome some of the limitations. We address the most important factor, the interpretability of our deep learning models. The research into interpretability in the recent year has made some real good progress. We particularly delve into the interpretability of attention based models without impacting the performance.

Key Takeaways from this talk

  • Learn about the issues associated with Deep Learning Models in real-world
  • Understand how attention based models work towards interpretability
  • Understand how a real use case utilized this framework to predict future diagnosis

Target Audience

Data Scientists, Engineers, Managers, AI Enthusiasts


The focus of this session is to present an interpretable deep learning model for disease prediction. The session explains how this model leverages attention to provide insights into prediction of future diagnosis. We also discuss how the model provides capabilities into identification of contributing events and factors. We present how operational and other medical concerns were addressed and our future steps on the same.

Section 1 (Introduction): Deep Learning/Machine Learning in Medical Domain

  • Present different use cases addressed in the medical domain using AI

Section 2 (Use Case): Interpretable Patient Diagnosis Framework

  • Problem statement
  • Brief introduction about research into attention based interpretable models
  • Implementation of attention based deep learning model for disease prediction
  • Inference Interpretation and its impact

Section 3 : Future Steps and Conclusion

  • Improving model performance without impacting interpretability
  • Impact of such models in practice/real-world


Participants should have a fair understanding of Machine Learning and Deep Learning(especially). Basics of deep learning would be helpful in appreciating the advanced concepts of attention, etc.

Speaker bio

Raghav Bali

Raghav Bali is a Senior Data Scientist at one the world’s largest health care organizations. His work involves research & development of enterprise level solutions based on Machine Learning, Deep Learning and Natural Language Processing for Healthcare & Insurance related use cases. Raghav has also authored multiple books with leading publishers, the recent one on latest in advancements in Transfer Learning research.



Preview video


  • Abhishek Balaji (@booleanbalaji) a year ago

    Hi Raghav/Harsha,

    Thank you for submitting a proposal.As per the speaker policy at HasGeek, we allow only one speaker on stage for the presentation. Please decide among yoursleves who would be on stage and update the bio accordingly. We need to see detailed slides and a preview video to evaluate your proposal. Your slides must cover the following:

    • Problem statement/context, which the audience can relate to and understand. The problem statement has to be a problem (based on this context) that can be generalized for all.
    • What were the tools/frameworks available in the market to solve this problem? How did you evaluate these, and what metrics did you use for the evaluation? Why did you pick the option that you did?
    • Explain how the situation was before the solution you picked/built and how it changed after implementing the solution you picked and built? Show before-after scenario comparisons & metrics.
    • What compromises/trade-offs did you have to make in this process?
    • What is the one takeaway that you want participants to go back with at the end of this talk? What is it that participants should learn/be cautious about when solving similar problems?

    Your preview video is a 2 minute self recorded video of the presenter where you outline what you’d be covering in your talk. We need your updated slides and preview video by Jun 17, 2019 to evaluate your proposal. If we do not receive an update, we’d be moving your proposal for evaluation under a future event.

  • Abhishek Balaji (@booleanbalaji) a year ago

    Rejected since proposer hasnt responded. Will be considered for a future even if content is uploaded.

    • Raghav Bali (@baliraghav) Proposer a year ago

      Hi Abhishek,
      We were under the assumption that we have deadline till 17th EOD, I am in the process of uploading the content.
      Kindly allow a couple of hours to do so.

      Apologies for the delay
      Awaiting your positive response

      • Abhishek Balaji (@booleanbalaji) a year ago

        Sure, it will be considered if the content is updated.

        • Raghav Bali (@baliraghav) Proposer a year ago

          Hi Abhishek,

          The deck and corresponding preview video have been updated. Kindly review.

  • Raghav Bali (@baliraghav) Proposer a year ago

    Hi Abhishek,

    Are there any review comments and next steps on the submission?

    Thanks and Regards
    Raghav Bali

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