LOCATION ADDRESS (Hybrid in person and zoom)
In-person at Hacker Dojo, 855 Maude Ave, Mountain View, CA 94043
***For faster entry*** at the Dojo, please [read the Dojo policies](https://tinyurl.com/9cn8sevt) and when you sign up, state “I accept the [Hacker Dojo policies](https://tinyurl.com/9cn8sevt)”
Online on Zoom
[https://acm-org.zoom.us/j/93871996231?pwd=akwxc2RrcWUzYlFMNnpaY1JjZC9zQT09](https://acm-org.zoom.us/j/93871996231?pwd=akwxc2RrcWUzYlFMNnpaY1JjZC9zQT09)
On YouTube:
[https://youtube.com/live/UMI7q8yS3ZQ?feature=share](https://youtube.com/live/UMI7q8yS3ZQ?feature=share)
AGENDA
6:30 Door opens, food and networking (we invite honor system contributions)
7:00 SFBayACM upcoming events, introduce the speaker
7:10 speaker presentation starts
8:15 – 8:30 finish, depending on Q&A
TALK DESCRIPTION
We review the literature on medical discourse and attempt to build a computational model of it. Medical discourse sheds a light on communication structure of patient-doctor and other communication scenarios in healthcare and should be leveraged to facilitate and automate this communication when it is possible and practical. We propose a unified framework to represent communication discourse at the meta-level, where the subject of the communication is expressed in a language object.
So far, the broad range of work on medical discourse is detached from computational discourse analysis, and we explore the possibilities of filling this gap and computationally treat the peculiarities of how information is passed between the agents in a hospital setting. We encode such discourse-level features as social interaction, critical discourse, metaphoric language, and representation of pain.
We select the domain of question answering (QA) against a corpus of medical documents of diverse nature to evaluate our computational model of medical discourse. We compare the performance of our discourse-enriched prompt-base models with the ones without manual discourse feature engineering. It turns out that applying specific structures obtained in medical discourse studies improves the relevance and efficiency of question answering. We pro also propose a RAG architecture leveraging discourse analysis.
[https://github.com/bgalitsky/medical_discourse](https://github.com/bgalitsky/medical_discourse)
KEYWORDS: Question answering system in health, computational medical discourse, large language models, natural language processing
SPEAKER BIO:
Boris Galitsky contributed linguistic and machine learning technologies to Silicon Valley startups as well as companies like eBay and Oracle for over 25 years. Boris’ information extraction and sentiment analysis techniques assisted a number of acquisitions, such as Xoopit by Yahoo, Uptake by Groupon, Loglogic by Tibco and Zvents by eBay. His security-related technologies of document analysis contributed to acquisition of Elastica by Semantec. [https://github.com/bgalitsky/relevance-based-on-parse-trees](https://github.com/bgalitsky/relevance-based-on-parse-trees)
As an architect of the Intelligent Bots project at Oracle, Boris developed a discourse analysis technique user for dialogue management and published in the book “Developing Enterprise Chatbots”. He also published a two-volume monograph “AI for CRM”, based on his experience developing Oracle Digital Assistant. Boris is Apache committer to OpenNLP where he created OpenNLP. Similarity component which is a basis for a semantically-enriched search engine and chatbot development.
Galitsky’s exploration and formalization of human seasoning culminated in the book “Computational Autism” broadly used by parents of children with autistic reasoning and rehabilitation personnel. Boris’s focus on medical domain led to another research monograph, “AI for Health Applications and Management”.
[https://www.amazon.com/Books-Boris-Galitsky/s?rh=n%3A283155%2Cp_27%3ABoris+Galitsky](https://www.amazon.com/Books-Boris-Galitsky/s?rh=n%3A283155%2Cp_27%3ABoris+Galitsky)
An Author of 150+ publications, 50+ patents and 6 books, Boris’s focus now is on improving content generation quality.
[https://www.linkedin.com/in/boris-galitsky-342109204/](https://www.linkedin.com/in/boris-galitsky-342109204/)
