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

Jul 2019

22 Mon

23 Tue

24 Wed

25 Thu 09:15 AM – 05:45 PM IST

26 Fri 09:20 AM – 05:30 PM IST

27 Sat

28 Sun

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Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

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The Fifth Elephant Winter edition starts at 9:30 am; live stream for members This update is for participants only

##The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:

Model management, including data cleaning, instrumentation and productionizing data science.
Bad data and case studies of failure in building data products.
Identifying and handling fraud + data security at scale
Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
Feature engineering and ML platforms.
What it takes to create data-driven cultures in organizations of different scales.

##Highlights:

1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.

##Why you should attend?

Network with peers and practitioners from the data ecosystem
Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
Demo your ideas in the demo session
Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.

##Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule

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

Submitted May 31, 2019

Session type: Full talk of 40 mins

Abstract

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

Outline

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

Requirements

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.