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SG HEALTHCARE AI

DATATHON & EXPO

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SG HEALTHCARE AI

DATATHON & EXPO

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SG HEALTHCARE AI

DATATHON & EXPO

  • Results for the Datathon

  • Datasets

     

    Reminder: Teams need to apply and obtain access to the datasets they intend to use before the datathon.

     

    1. Electrical Medical Records Datasets

    During the datathon, teams will have access to 3 de-identified EMR datasets. Teams may choose to use one or all of these datasets to answer their clinical questions. In particular, these three datasets are: 1) the Medical Information Mart for Intensive Care (MIMIC)-IV Database from Physionet 2) the Philips eICU Collaborative Research Database (https://eicu-crd.mit.edu/). These three databases share similar data schemas. They contain hourly physiologic readings from bedside monitors, validated by ICU nurses. They also contain records of demographics, labs, nursing progress notes, discharge summaries, IV medications, fluid balance, and other clinical variables.

    MIMIC-IV Dataset

    Introduction & Access Application: https://mimic-iv.mit.edu/

     

    Github repository: https://github.com/MIT-LCP/mimic-iv

     

    Documentation: https://mimic-iv.mit.edu/docs/

     

    When using this resource, please cite:
    Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2020). MIMIC-IV (version 0.4). PhysioNet. https://doi.org/10.13026/a3wn-hq05.


    Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

    eICU-CRD Dataset

    Introduction & Documentation: https://eicu-crd.mit.edu/about/eicu/

     

    Github repository: https://github.com/mit-eicu/eicu-code

     

    Example code: https://github.com/mit-eicu/eicu-code/blob/master/concepts/icustay_detail.sql

     

    When using this resource, please cite:
    Pollard, T., Johnson, A., Raffa, J., Celi, L. A., Badawi, O., & Mark, R. (2019). eICU Collaborative Research Database (version 2.0). PhysioNet. https://doi.org/10.13026/C2WM1R.

     

    The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG and Badawi O. Scientific Data (2018). DOI: http://dx.doi.org/10.1038/sdata.2018.178.

     

    Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

  • We had 50 teams with close to 500 physicians and data scientists from more than 10 regions that joined us in our 2020 event!

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  • And the winner goes to...

    Champion

    Team 36: Developing an algorithm to predict real-time hourly variation of ICP

    First Runner-up

    Team 11: Drug-drug Interactions via Graph Neural network and Causal Inference

    First Runner-up

    Team 15: Diagnosis and Prediction of Diabetes via Overnight SpO2 Signal Based on Deep Learning

    Second Runner-up

    Team 09: AI model for Intraoperative Hypoxemia Prediction

    Second Runner-up

    Team 50: Early Prediction of Sepsis Mortality using Multimodal Machine Learning

    Second Runner-up

    Team 55: Using Explainable AI for Image Analysis in Diabetic Foot Ulcers

    Best Presentation Award

    Team 21: Application of Three-stage Prediction of Mortality in Critically Ill Patients

    Best Presentation Award

    Team 28: Heterogeneity in Treatment Effects of Hydrocrotisone for Sepsis

    Best Potential Award

    Team 54: Paediatric Pneumonia Chest C-ray Classification

    Best Potential Award

    Team 59: Blindness Terminator

SINGAPORE HEALTHCARE AI DATATHON AND EXPO 2021

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