Knowledge-infused Learning for Healthcare

PyData Salamanca is proud to announce a new meetup!

Due to the current circumstances, we have decided to go online!

Youtube Live link:
https://youtu.be/AVEXOtEL0-A

AGENDA:

19:05 am - Keynote
Knowledge-infused Learning for Healthcare (English)
Speaker: Manas Gaur - Ph.D. student @ AI Institute - University of South Carolina (USA)

Abstract:
Statistical AI has been successfully applied in healthcare services (e.g. electronic health records) and practices (e.g. counseling conversation in mental health) both in online and clinical settings. However, several concerns remain that prevent the broader adoption of AI techniques. These include interpretability, traceability, and explainability of recognized patterns and their inter-relationships, which improves clinical decision making. While learning the attribute-level characteristics of the data, a data-driven model is affected by inherent sparsity, lack of variation, and ambiguity in the data. This limits the generalizability of the model (e.g. exposure bias, vanishing gradient). Further, in the healthcare domain, most of the decisions are determined through domain knowledge, experience (including precedence), and reasoning. This can be incorporated by adding a semantics-driven approach that incorporates logical constraints together with domain knowledge to identify latent relationships in data. This allows the model to remove ambiguity, augment relevant information to resolve sparsity, and perform stratified learning. Thus, our task is to efficiently integrate top-down and bottom-up learning paradigms to facilitate interpretable and explainable intelligent systems. Infusion of relevant semantic information to input, latent, and output layers of deep nets through weight-customization of knowledge graph (e.g DBpedia, ConceptNet, SNOMED-CT, DataMed) could provide answers to the following four questions in healthcare infrastructure: (a) Do we really need a powerful and complex learning mechanism in developing early intervention system in healthcare? (b) How to understand and visualize the learning from a model at a conceptual level, (c) Did the model give appropriate weights to concepts/relation that interest end-user clinicians?, and (d) Why should I learn from data when I already have structural knowledge that captures it. An inquiry into these questions, allowed us to investigate three different knowledge-infusion paradigms: (a) Shallow Infusion, (b) Semi-Deep Infusion, and (c) Deep Infusion of knowledge from KG. The talk will explain Knowledge-Infused learning through four capabilities needed for healthcare applications: Medical Entity Normalization, Knowledge-Aware Hybrid Patient-Clinician Matchmaking, and Abstractive Summarization.

Short bio:
Manas Gaur has been Data Science and AI for Social Good Fellow with the University of Chicago and Dataminr Inc. His interdisciplinary research funded by NIH and NSF operationalizes the use of Knowledge Graphs, Natural Language Understanding, and Machine Learning to solve social good problems in the domain of Mental Health, Cyber Social Harms, and Crisis Response.
https://manasgaur.github.io
(manas@email.sc.edu | @manasgaur90)

19:50 - Q&A
All questions can be asked using the Youtube live chat.
Questions will be addressed at the end of the keynote.

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What is PyData? PyData is a group for users and developers of data analysis tools to share ideas and learn from each other. We gather to discuss best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. We approach data science using many languages, including (but not limited to) Python, Julia, and R.

FUTURE SPEAKERS

Would you like to speak at this meetup or a future one? Please submit your proposal at (coordinaciondatalab@gmail.com )