MediSage: An AI Assistant for Healthcare via Composition of Neural-Symbolic Reasoning Operators
Pages 258 - 261
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.
- Download
- 32.73 MB
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
Quality and Cost Improvement of Healthcare via Complementary Measurement and Diagnosis of Patient General Health Outcome Using Electronic Health Record Data: Research Rationale and Design
In this evolving `third era of health', one of the US Health Care Reform Act's goals is to effectively facilitate the primary care physician's ability to better diagnose and manage the health outcome of the outpatient. That goal must include research on ...
Comments
Information & Contributors
Information
Published In
![cover image ACM Conferences](/cms/asset/da893430-ead5-47e0-b258-3f024ac45f36/3543873.cover.jpg)
April 2023
1567 pages
ISBN:9781450394192
DOI:10.1145/3543873
Copyright © 2023 Owner/Author.
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:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 180Total 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
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format