skip to main content
10.1145/3543873.3587361acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
demonstration

MediSage: An AI Assistant for Healthcare via Composition of Neural-Symbolic Reasoning Operators

Published: 30 April 2023 Publication History

Abstract

We introduce MediSage, an AI decision support assistant for medical professionals and caregivers that simplifies the way in which they interact with different modalities of electronic health records (EHRs) through a conversational interface. It provides step-by-step reasoning support to an end-user to summarize patient health, predict patient outcomes and provide comprehensive and personalized healthcare recommendations. MediSage provides these reasoning capabilities by using a knowledge graph that combines general purpose clinical knowledge resources with recent-most information from the EHR data. By combining the structured representation of knowledge with the predictive power of neural models trained over both EHR and knowledge graph data, MediSage brings explainability by construction and represents a stepping stone into the future through further integration with biomedical language models.

Supplemental Material

MP4 File
A conversational medium based AI medical assistant.

References

[1]
Khushbu et al. Agarwal. 2019. Snomed2Vec: Random Walk and Poincar\ ’e Embeddings of a Clinical Knowledge Base for Healthcare Analytics. arXiv preprint arXiv:1907.08650 (2019).
[2]
Khushbu et al. Agarwal. 2022. Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction. Scientific Reports 12, 1 (2022), 10748.
[3]
Payal Chandak, Kexin Huang, and Marinka Zitnik. 2022. Building a knowledge graph to enable precision medicine. BioRxiv (2022), 2022–05.
[4]
Sutanay Choudhury, Sumit Purohit, Peng Lin, Yinghui Wu, Lawrence Holder, and Khushbu Agarwal. 2018. Percolator: Scalable pattern discovery in dynamic graphs. In WSDM.
[5]
Ian C Covert, Scott Lundberg, and Su-In Lee. 2021. Explaining by removing: A unified framework for model explanation. The Journal of Machine Learning Research 22, 1 (2021), 9477–9566.
[6]
Somalee Datta et al.2020. A new paradigm for accelerating clinical data science at Stanford Medicine. (2020). https://arxiv.org/abs/2003.10534
[7]
Jiří et al. Gallo. 2022. Odds-ratio network for postoperative factors revealing differences in the 2-year longitudinal pattern of satisfaction between women and men after total knee arthroplasty. Scientific Reports 12, 1 (2022), 17470.
[8]
Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems 30 (2017).
[9]
Renqian et al. Luo. 2022. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Briefings in Bioinformatics 23, 6 (2022).
[10]
Long et al. Ouyang. 2022. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155 (2022).
[11]
Karan et al. Singhal. 2022. Large Language Models Encode Clinical Knowledge. arXiv preprint arXiv:2212.13138 (2022).
[12]
Ping Wang, Khushbu Agarwal, Colby Ham, Sutanay Choudhury, and Chandan K Reddy. 2021. Self-supervised learning of contextual embeddings for link prediction in heterogeneous networks. In Proceedings of the Web Conference 2021.
[13]
Michihiro Yasunaga, Jure Leskovec, and Percy Liang. 2022. Linkbert: Pretraining language models with document links. arXiv preprint arXiv:2203.15827 (2022).

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
April 2023
1567 pages
ISBN:9781450394192
DOI:10.1145/3543873
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

Check for updates

Qualifiers

  • Demonstration
  • Research
  • Refereed limited

Data Availability

A conversational medium based AI medical assistant. https://dl.acm.org/doi/10.1145/3543873.3587361#MediSage.mp4

Conference

WWW '23
Sponsor:
WWW '23: The ACM Web Conference 2023
April 30 - May 4, 2023
TX, Austin, USA

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 180
    Total Downloads
  • Downloads (Last 12 months)60
  • Downloads (Last 6 weeks)4
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media