CrowdEEG Dataset: Expert Adjudication Discussions for Medical Time Series Data

Abstract

This repository contains a dataset for a medical time series classification task acquired using <strong>CrowdEEG</strong>. 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: <strong>Ambiguity-aware AI Assistants for Medical Data Analysis</strong>. CHI 2020. Mike Schaekermann, Graeme Beaton, Minahz Habib, Andrew Lim, Kate Larson, and Edith Law: <strong>Understanding Expert Disagreement in Medical Data Analysis through Structured Adjudication</strong>. 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: <pre><code>@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} }</code></pre>

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