thumbnail image

SG HEALTHCARE AI

DATATHON & EXPO

  • Home
  • Organizers & Sponsors 
    • Organizers & Sponsors
    • Speakers
  • Datathon 
    • Schedule & Mentors
    • Rules & Prizes
    • Results 2021
    • Data
  • Workshops & Seminars 
    • ST Engineering
    • SenseTime
    • Lenovo
    • ASUS
    • Huawei
    • Intel AI in Healthcare
    • Intel oneAPI Workshop
    • Neo4j
    • TIBCO
    • Holomedicine Association Summit
    • Red Hat
    • AWS Workshop
    • NVIDIA x NSCC
    • APICS 2021
  • AWS x NUHS Online Challenge
  • Healthcare AI Leadership
  • Expo
  • Registration
  • Gallery 
    • 2020
    • 2018
    • 2017
  • Contact
  • …  
    • Home
    • Organizers & Sponsors 
      • Organizers & Sponsors
      • Speakers
    • Datathon 
      • Schedule & Mentors
      • Rules & Prizes
      • Results 2021
      • Data
    • Workshops & Seminars 
      • ST Engineering
      • SenseTime
      • Lenovo
      • ASUS
      • Huawei
      • Intel AI in Healthcare
      • Intel oneAPI Workshop
      • Neo4j
      • TIBCO
      • Holomedicine Association Summit
      • Red Hat
      • AWS Workshop
      • NVIDIA x NSCC
      • APICS 2021
    • AWS x NUHS Online Challenge
    • Healthcare AI Leadership
    • Expo
    • Registration
    • Gallery 
      • 2020
      • 2018
      • 2017
    • Contact

SG HEALTHCARE AI

DATATHON & EXPO

  • Home
  • Organizers & Sponsors 
    • Organizers & Sponsors
    • Speakers
  • Datathon 
    • Schedule & Mentors
    • Rules & Prizes
    • Results 2021
    • Data
  • Workshops & Seminars 
    • ST Engineering
    • SenseTime
    • Lenovo
    • ASUS
    • Huawei
    • Intel AI in Healthcare
    • Intel oneAPI Workshop
    • Neo4j
    • TIBCO
    • Holomedicine Association Summit
    • Red Hat
    • AWS Workshop
    • NVIDIA x NSCC
    • APICS 2021
  • AWS x NUHS Online Challenge
  • Healthcare AI Leadership
  • Expo
  • Registration
  • Gallery 
    • 2020
    • 2018
    • 2017
  • Contact
  • …  
    • Home
    • Organizers & Sponsors 
      • Organizers & Sponsors
      • Speakers
    • Datathon 
      • Schedule & Mentors
      • Rules & Prizes
      • Results 2021
      • Data
    • Workshops & Seminars 
      • ST Engineering
      • SenseTime
      • Lenovo
      • ASUS
      • Huawei
      • Intel AI in Healthcare
      • Intel oneAPI Workshop
      • Neo4j
      • TIBCO
      • Holomedicine Association Summit
      • Red Hat
      • AWS Workshop
      • NVIDIA x NSCC
      • APICS 2021
    • AWS x NUHS Online Challenge
    • Healthcare AI Leadership
    • Expo
    • Registration
    • Gallery 
      • 2020
      • 2018
      • 2017
    • Contact

SG HEALTHCARE AI

DATATHON & EXPO

  • Home
  • Organizers & Sponsors
    • Organizers & Sponsors
    • Speakers
  • Datathon
    • Schedule & Mentors
    • Rules & Prizes
    • Results 2021
    • Data
  • Workshops & Seminars
    • ST Engineering
    • SenseTime
    • Lenovo
    • ASUS
    • Huawei
    • Intel AI in Healthcare
    • Intel oneAPI Workshop
    • Neo4j
    • TIBCO
    • Holomedicine Association Summit
    • Red Hat
    • AWS Workshop
    • NVIDIA x NSCC
    • APICS 2021
  • AWS x NUHS Online Challenge
  • Healthcare AI Leadership
  • Expo
  • Registration
  • Gallery
    • 2020
    • 2018
    • 2017
  • Contact
  • Search
    • Data for the Datathon

      De-identified Real-world Healthcare Datasets

    • 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/) 3) the VitalDB dataset (https://vitaldb.net/dataset/) . 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.

      VitalDB Dataset

      Introduction & Documentation: https://vitaldb.net/dataset/

       

      Data Summary: https://vitaldb.net/dataset/?query=overview&documentId=13qqajnNZzkN7NZ9aXnaQ-47NWy7kx-a6gbrcEsi-gak&sectionId=h.1fo5zknztqnw

       

      API Documentation: https://vitaldb.net/docs/

       

      Example code: https://github.com/vitaldb/examples/

       

      When using this resource, please cite:

      Lee, Hyung-Chul, and Chul-Woo Jung. "Vital Recorder—a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices." Scientific reports 8.1 (2018): 1-8.

      National Sleep Research Resource Datasets

      Introduction & Documentation: https://sleepdata.org/about

       

      Data Summary: https://sleepdata.org/datasets

       

      API Documentation: https://sleepdata.org/tools

       

      Forum : https://sleepdata.org/forum

       

      !!! Note: These datasets required individual registration and approval from NSRR. Thus, we will not be able to host these datasets on our cloud for every team. Teams that would like to use these datasets please remember to apply for approval and would need to host the datasets locally themselves. Thanks for your understanding in advance.

    • 2. Medical Imaging Datasets

      Wi will provide a large collection of medical image datasets will be provided to all teams.

      MIMIC CXR Dataset

      Introduction & Documentation: https://physionet.org/content/mimic-cxr/2.0.0/

       

      When using this resource, please cite:

      Johnson AE, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng CY, Mark RG, Horng S. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Scientific Data. 2019;6.

      3D Medical Image Dataset from Medical Segmentation Decatholon

      Introduction & Documentation: http://medicaldecathlon.com/

       

      When using this resource, please cite:

      https://arxiv.org/abs/1902.09063

      NIH Chest X-ray dataset

      Introduction & Documentation: https://www.kaggle.com/nih-chest-xrays/data

       

      When using this resource, please cite:

      http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf(link is external)

      AutoImplant2020

      Introduction & Documentation: https://autoimplant.grand-challenge.org/

       

      When using this resource, please cite:

      https://arxiv.org/pdf/2006.00980.pdf

      VerSe2019

      Introduction & Documentation: https://osf.io/nqjyw/

       

      When using this resource, please cite:

      https://arxiv.org/pdf/2001.09193.pdf

      VerSe2020

      Introduction & Documentation: https://osf.io/t98fz/

       

      When using this resource, please cite:

      https://arxiv.org/pdf/2001.09193.pdf

      MICCAI 2020 RibFrac Challenge: Rib Fracture Detection and Classification

      Introduction & Documentation: https://ribfrac.grand-challenge.org/

       

      When using this resource, please cite:

      Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet(In press)

      EMIDEC automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI

      Introduction & Documentation: http://emidec.com/

       

      When using this resource, please cite:

      https://www.mdpi.com/2306-5729/5/4/89

      Multimodal Brain Tumor Segmentation Challenge 2020: Data

      Introduction & Documentation: https://www.med.upenn.edu/cbica/brats2020/data.html

       

      When using this resource, please cite:

      https://pubmed.ncbi.nlm.nih.gov/25494501/

      https://pubmed.ncbi.nlm.nih.gov/28872634/

      https://arxiv.org/abs/1811.02629

      HECKTOR challenge

      Introduction & Documentation: https://www.aicrowd.com/challenges/miccai-2020-hecktor

       

      When using this resource, please cite: https://github.com/voreille/hecktor

       

      Chest X-Ray Images (Pneumonia)

      Introduction & Documentation: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

       

      When using this resource, please cite: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5

       

      Diabetic Retinopathy Detection

      Introduction & Documentation: https://www.kaggle.com/c/diabetic-retinopathy-detection/data

       

      When using this resource, please cite: http://www.eyepacs.com/

       

      Messidor

      Introduction & Documentation: http://www.adcis.net/en/third-party/messidor/

       

      When using this resource, please cite:

      http://www.ias-iss.org/ojs/IAS/article/view/1155
      http://dx.doi.org/10.5566/ias.1155.

       

      SARAS

      Introduction & Documentation: https://saras-mesad.grand-challenge.org/

       

      When using this resource, please cite:

      https://arxiv.org/abs/2104.03178
      https://arxiv.org/abs/2006.07164

       

      Standford CheXpet dataset

      Introduction & Documentation: https://stanfordmlgroup.github.io/competitions/chexpert/

       

      When using this resource, please cite:

      https://arxiv.org/abs/1901.07031?utm_medium=email&utm_source=transaction

      Chula RBC-12-Dataset

      Introduction & Documentation: https://github.com/Chula-PIC-Lab/Chula-RBC-12-Dataset

       

      When using this resource, please cite:

      https://arxiv.org/abs/2012.01321

       

    SINGAPORE HEALTHCARE AI DATATHON AND EXPO 2021

      Cookie Use
      We use cookies to ensure a smooth browsing experience. By continuing we assume you accept the use of cookies.
      Learn More