HealthE Article Swipe
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· 2023
· Open Access
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· DOI: https://doi.org/10.5281/zenodo.7539392
· OA: W4393642972
# HealthE Dataset HealthE contains 3,400 pieces of health advice gathered 1) from public health websites (i.e. WebMD.com, MedlinePlus.gov, CDC.gov, and MayoClinic.org) 2) from the publicly available [Preclude dataset]([https://userpages.umbc.edu/~nroy/courses/shhasp18/papers/p286-preum.pdf](https://userpages.umbc.edu/~nroy/courses/shhasp18/papers/p286-preum.pdf)). Each sample was hand-labeled for health entity recognition by a team of 14 annotators at the author's institution. Automatic recognition of health entities will enable further research in large-scale modeling of texts from online health communities. The data is provided in two parts. Both are formatted using the popular, free python `pickle` library and require use of the popular, free `pandas` library. `healthe.pkl` is a `pandas.DataFrame` object containing the 3,400 health-advice statement with hand-labeled health entities. `non_advice.pkl` is a `pandas.DataFrame` object containing the 2,256 pieces of non-advice statements. To load the files in python, use the following code block.<br> ```<br> import pickle<br> import pandas as pd<br> healthe_df = pd.read_pickle('healthe.pkl')<br> non_advice_df = pd.read_pickle('non_advice_df.pkl')<br> ``` `healthe_df` has four columns.<br> * `text` contains the health advice statement text<br> * `entities` contains a python list of (entity, class) tuples<br> * `tokenized_text` contains a list of tokens obtained by tokenizing the health advice statement text <br> * `labels` contains a list of the same length as `tokenized_text`, where each token is mapped to a class label. `non_advice_df` has one column, `text`, referring to each non-health-advice-statement.
