Abstract: 

AI is accelerating innovation in several industries and healthcare presents one of the biggest opportunities for new solutions. However, the healthcare domain also poses unique challenges. Data comes in myriad formats, is noisy and rarely labeled. Use cases may require mixing data-driven approaches with those that leverage expert domain knowledge. Few situations enable direct application of familiar machine learning methods but instead require inventive approaches of discriminant analysis and/or ensemble methods. There is also a high bar for evaluation, integration, and operationalizing of AI solutions in clinical settings. This workshop will begin with the business implications of using AI in healthcare including current trends and use cases. We will then demonstrate a number of AI applications in production today. The technical portion will provide an interactive walkthrough of applying AI to healthcare data with live coding. We’ll conclude by covering the requirements for operationalizing an AI solution including the technical architecture necessary for deploying in the wild.

General Idea of what to present:

AI Overview
AI trends in Healthcare
Current applications
Specific use cases in healthcare
Demonstration of Wolters Kluwer AI applications
Demo of pathways
Demo/presentation of HL
Further discussion of NLP use cases and applied walk through with coding
Operationalizing AI – how to put this into production

Bio: 

Krishna Srihasam is a senior data scientist at Wolters Kluwer Health. He has been applying ML and AI techniques to Health and Patient data for more than 3 years. He holds a Ph.D in Computational Neuroscience and has published several articles on applying ML techniques to neuroscience research