|
|
--- |
|
|
dataset_info: |
|
|
features: |
|
|
- name: text |
|
|
dtype: string |
|
|
- name: label |
|
|
dtype: |
|
|
class_label: |
|
|
names: |
|
|
'0': Bacterial Infections and Mycoses |
|
|
'1': Virus Diseases |
|
|
'2': Parasitic Diseases |
|
|
'3': Neoplasms |
|
|
'4': Musculoskeletal Diseases |
|
|
'5': Digestive System Diseases |
|
|
'6': Stomatognathic Diseases |
|
|
'7': Respiratory Tract Diseases |
|
|
'8': Otorhinolaryngologic Diseases |
|
|
'9': Nervous System Diseases |
|
|
'10': Eye Diseases |
|
|
'11': Urologic and Male Genital Diseases |
|
|
'12': Female Genital Diseases and Pregnancy Complications |
|
|
'13': Cardiovascular Diseases |
|
|
'14': Hemic and Lymphatic Diseases |
|
|
'15': Neonatal Diseases and Abnormalities |
|
|
'16': Skin and Connective Tissue Diseases |
|
|
'17': Nutritional and Metabolic Diseases |
|
|
'18': Endocrine Diseases |
|
|
'19': Immunologic Diseases |
|
|
'20': Disorders of Environmental Origin |
|
|
'21': Animal Diseases |
|
|
'22': Pathological Conditions, Signs and Symptoms |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 4302749 |
|
|
num_examples: 3357 |
|
|
- name: test |
|
|
num_bytes: 5207699 |
|
|
num_examples: 4043 |
|
|
download_size: 5084973 |
|
|
dataset_size: 9510448 |
|
|
configs: |
|
|
- config_name: default |
|
|
data_files: |
|
|
- split: train |
|
|
path: data/train-* |
|
|
- split: test |
|
|
path: data/test-* |
|
|
--- |
|
|
|
|
|
This dataset is an adaptation of the Ohsumed dataset made available in https://github.com/yao8839836/text_gcn, wich removes records from the original Ohsumed corpus belonging to more than one desease category. The dataset is divided into the same train and test splits defined in the original repository. |
|
|
|
|
|
Numerical labels were converted to `ClassLabel`s using string descriptors as names, based on the following relation (adapted from [this repo](https://github.com/Evgeneus/screening-classification-datasets/blob/master/ohsumed-based-screening-datasets/README.md)): |
|
|
|
|
|
| Label | Original Category | Name | |
|
|
|-------|----------------|------------| |
|
|
| 0 | C01 | Bacterial Infections and Mycoses | |
|
|
| 1 | C02 | Virus Diseases | |
|
|
| 2 | C03 | Parasitic Diseases | |
|
|
| 3 | C04 | Neoplasms | |
|
|
| 4 | C05 | Musculoskeletal Diseases | |
|
|
| 5 | C06 | Digestive System Diseases | |
|
|
| 6 | C07 | Stomatognathic Diseases | |
|
|
| 7 | C08 | Respiratory Tract Diseases | |
|
|
| 8 | C09 | Otorhinolaryngologic Diseases | |
|
|
| 9 | C10 | Nervous System Diseases | |
|
|
| 10 | C11 | Eye Diseases | |
|
|
| 11 | C12 | Urologic and Male Genital Diseases | |
|
|
| 12 | C13 | Female Genital Diseases and Pregnancy Complications | |
|
|
| 13 | C14 | Cardiovascular Diseases | |
|
|
| 14 | C15 | Hemic and Lymphatic Diseases | |
|
|
| 15 | C16 | Neonatal Diseases and Abnormalities | |
|
|
| 16 | C17 | Skin and Connective Tissue Diseases | |
|
|
| 17 | C18 | Nutritional and Metabolic Diseases | |
|
|
| 18 | C19 | Endocrine Diseases | |
|
|
| 19 | C20 | Immunologic Diseases | |
|
|
| 20 | C21 | Disorders of Environmental Origin | |
|
|
| 21 | C22 | Animal Diseases | |
|
|
| 22 | C23 | Pathological Conditions, Signs and Symptoms | |
|
|
|
|
|
|
|
|
To cite the original work: |
|
|
|
|
|
```bib |
|
|
@InProceedings{10.1007/BFb0026683, |
|
|
author="Joachims, Thorsten", |
|
|
editor="N{\'e}dellec, Claire |
|
|
and Rouveirol, C{\'e}line", |
|
|
title="Text categorization with Support Vector Machines: Learning with many relevant features", |
|
|
booktitle="Machine Learning: ECML-98", |
|
|
year="1998", |
|
|
publisher="Springer Berlin Heidelberg", |
|
|
address="Berlin, Heidelberg", |
|
|
pages="137--142", |
|
|
abstract="This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.", |
|
|
isbn="978-3-540-69781-7" |
|
|
} |
|
|
``` |
