Medical Question Pair by Kaggle | Healthcare

About this Dataset

Medical Question Pair

Identifying Similarities and Differences in Doctor’s Questions

By Huggingface Hub [source]

About this dataset

This dataset challenges machines to recognize subtle yet essential similarities and differences in medical questions asked by 11 different doctors. By providing question pairs labeled with either 1 for a positive, similar pair, or 0 for a negative, different pair, algorithms test their ability to decipher the unseen nuances between these texts. This collection of over 10K examples gives insight into how machines can think like doctors, helping create more accurate diagnosis and treatment plans with fewer errors than human counterparts. With its comprehensive labels and unique take on language processing and classification, this dataset tests the mettle of today's machine learning solutions while setting the stage for tomorrow’s medical advancements

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How to use the dataset

This dataset contains question pairs for machine comprehension and classification drawn from 11 different doctors. All question pairs have been labeled with a value of 1 (similar) or 0 (different), allowing algorithms to distinguish between similar and different questions. This can be used to effectively recognize positive and negative differences in medical questions asked by different doctors.

To use this dataset, you should first understand the format of the data provided. The columns include question_1, which is the first question in the pair; question_2, which is the second question in the pair; and label, which is a value of 1 or 0 indicating whether the pair is similar (1) or different (0).

Once you have familiarized yourself with the data format, you can begin using it for medical text classification tasks. Some possible applications could involve creating classifiers that accurately predict if two questions are similar or different based on contextual clues; or building an algorithm that determines similarities by looking at semantic relations between words present in each sentence. Additionally, clustering algorithms can be used to detect common patterns among documents or individual sentences based on specific criteria such as topic, context, length of sentence etc., allowing one to group them accordingly. Finally recommendation systems can also be built using this data set deep learning methods whereby predictions about unknown pairs are made through embedding learning techniques with supervised methods for tuning accuracy results achieve optimal performance levels when analyzing findings from sets test examples assessing models’ overall effectiveness testing whether hold true applying scale task recognition given problems modern world techniques leaping ahead last few years instance neural networks popular baseline approaches machine comprehension syntactic semantic information leverage better decision making industry research projects knowing natural language understanding handling noisy incomplete data sources increase accuracy solutions handcrafted features perpetual goal pushing field computer science aid user experience

Research Ideas

Automatically identifying symptoms and diseases based on medical questions asked

Detecting possible causes or treatments for illnesses by comparing similar question pairs from different doctors

Generating new diagnostic guidelines and protocols using machine learning algorithms to analyze similarities between question pairs from multiple experts

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv

Column name	Description
question_1	The first question in the pair. (String)
question_2	The second question in the pair. (String)
label	The label indicating whether the questions are similar (1) or different (0). (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.

Tables

Train

@kaggle.thedevastator_medical_question_pair_classification.train

301.1 KB
3048 rows
4 columns


CREATE TABLE train (
  "dr_id" BIGINT,
  "question_1" VARCHAR,
  "question_2" VARCHAR,
  "label" BIGINT
);