CrowdEEG Dataset: Expert Adjudication Discussions for Medical Time Series Data

This repository contains a dataset for a medical time series classification task acquired using CrowdEEG. Beyond classification labels, the dataset includes structured arguments from adjudication discussions of 3 medical experts per contentious classification decision.

The corpus has been referenced in the following papers:

• Mike Schaekermann, Graeme Beaton, Elaheh Sanoubari, Andrew Lim, Kate Larson, and Edith Law: Ambiguity-aware AI Assistants for Medical Data Analysis. CHI 2020.
• Mike Schaekermann, Graeme Beaton, Minahz Habib, Andrew Lim, Kate Larson, and Edith Law: Understanding Expert Disagreement in Medical Data Analysis through Structured Adjudication. CSCW 2019.

This repository only contains classification labels and adjudication arguments, not the raw medical time series records. Please reach out to Mike Schaekermann (mikeschaekermann@gmail.com) to request access to the underlying raw time series data. A statement of purpose and proof of your institutional ethics clearance may be required.

If you find this data useful in your research, please consider citing:

@inproceedings{Schaekermann2020AmbiguityAwareAI,
  Author = {Schaekermann, Mike and Beaton, Graeme and Sanoubari, Elaheh and Lim, Andrew and Larson, Kate and Law, Edith},
  Title = {Ambiguity-Aware AI Assistants for Medical Data Analysis},
  Year = {2020},
  ISBN = {9781450367080},
  Publisher = {Association for Computing Machinery},
  Address = {New York, NY, USA},
  DOI = {10.1145/3313831.3376506},
  Pages = {1–14},
  Numpages = {14},
  Location = {Honolulu, HI, USA},
  Series = {CHI '20}
}