MrMaxMind99/Medical-QA-GT dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset was designed and created to enable advancements in healthcare-focused large language models, particularly in the context of retrieval-augmented clinical question-answering capabilities. Developed using a self-constructed pipeline based on the 13-billion parameter Meta Llama 2 model, this dataset encompasses 21466 medical discharge summaries extracted from the MIMIC-IV-Note dataset, with 156599 synthetically generated question-and-answer pairs, a subset of which was verified for accuracy by a physician. These pairs were generated by providing the model with a discharge summary and instructing it to generate question-and-answer pairs based on the contextual information present in the summaries. This work aims to generate data in support of the development of compact large language models capable of efficiently extracting information from medical notes and discharge summaries, thus enabling potential improvements for real-time decision-making processes in clinical settings. Additionally, accompanying the dataset is code facilitating question-and-answer pair generation from any medical and non-medical text. Despite the robustness of the presented dataset, it has certain limitations. The generation process was confined to a maximum context length of 6000 input tokens, owing to hardware constraints. The large language model's nature in generating these question-and-answer pairs may introduce an underlying bias or a lack in diversity and complexity. Future iterations should focus on rectifying these issues, possibly through diversified training and expanded verification procedures as well as the employment of more powerful large language models.

Dataset Card for "medical-qa-shared-task-v1-toy-eval"

More Information needed

Dataset

This dataset was created by mohamed ardif

Contents

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Dataset Details





  Dataset Description

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

  Dataset Sources [optional]

Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/TUDB-Labs/medical-qa.

The researcher tests the QA capability of ChatGPT in the medical field from the following aspects:1. Test their reserve capacity for medical knowledge2. Check their ability to read literature and understand medical literature3. Test their ability of auxiliary diagnosis after reading case data4. Test its error correction ability for case data5. Test its ability to standardize medical terms6. Test their evaluation ability to experts7. Check their ability to evaluate medical institutionsThe conclusion is:ChatGPT has great potential in the application of medical and health care, and may directly replace human beings or even professionals at a certain level in some fields;The researcher preliminarily believe that ChatGPT has basic medical knowledge and the ability of multiple rounds of dialogue, and its ability to understand Chinese is not weak;ChatGPT has the ability to read, understand and correct cases;ChatGPT has the ability of information extraction and terminology standardization, and is quite excellent;ChatGPT has the reasoning ability of medical knowledge;ChatGPT has the ability of continuous learning. After continuous training, its level has improved significantly;ChatGPT does not have the academic evaluation ability of Chinese medical talents, and the results are not ideal;ChatGPT does not have the academic evaluation ability of Chinese medical institutions, and the results are not ideal;ChatGPT is an epoch-making product, which can become a useful assistant for medical diagnosis and treatment, knowledge service, literature reading, review and paper writing.

Juliabot/medical-qa-formatted dataset hosted on Hugging Face and contributed by the HF Datasets community

charlieoneill/medical-qa-combined dataset hosted on Hugging Face and contributed by the HF Datasets community

# Patient Doctor Q&A TR 321179 Veri Seti
Patient Doctor Q&A TR 321179 veri seti, [**Patient Doctor Q&A TR 19583**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-qa-dataset-tr), [**Patient Doctor Q&A TR 167732**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-tr-167732), [**Patient Doctor Q&A TR 5695**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-translated-from-id-to-tr) ve [**Patient Doctor Q&A TR 95588**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-tr-95588) veri setlerinin birleştirilmiş ve karıştırılmış halidir.

## Ana Özellikler:
* İçerik: Çeşitli tıbbi konuları kapsayan hasta soruları ve doktor yanıtları.
* Yapı: 2 sütun içerir: Soru, Cevap.
* Dil: Türkçe.
## Potansiyel Kullanım Alanları:
* Tıbbi araştırmalar
* Doğal Dil İşleme (NLP)
* Tıbbi eğitim
## Sınırlamalar:
* Veri gizliliği endişeleri
* Yanıt kalitesinde değişkenlik
* Potansiyel önyargılar
## Genel Değerlendirme:
Patient Doctor Q&A TR 321179 veri seti, gerçek dünyadaki tıbbi iletişimi ve bilgi alışverişini anlamak için değerli bir kaynaktır. Türkçeye çevrilmiş bu veri seti, tıbbi araştırmalar ve eğitim için önemli bir kaynak olup, hasta ve doktor arasındaki iletişimi analiz etmek için kullanılabilir. Ancak, veri gizliliği ve yanıt kalitesindeki değişkenlik gibi sınırlamalar göz önünde bulundurulmalıdır.

Bu veri seti, araştırmacılara ve eğitimcilere, Türkçe tıbbi iletişim verilerini kullanarak daha derinlemesine analiz yapma ve doğal dil işleme tekniklerini uygulama fırsatı sunar.

# Patient Doctor Q&A TR 321179 Dataset
The Patient Doctor Q&A TR 321179 dataset is a combined and shuffled version of the [**Patient Doctor Q&A TR 19583**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-qa-dataset-tr), [**Patient Doctor Q&A TR 167732**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-tr-167732), [**Patient Doctor Q&A TR 5695**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-translated-from-id-to-tr), and [**Patient Doctor Q&A TR 95588**](https://www.kaggle.com/datasets/kaayra2000/patient-doctor-q-and-a-tr-95588) datasets.

## Main Features:
* Content: Patient questions and doctor answers covering various medical topics.
* Structure: Contains 2 columns: Question, Answer.
* Language: Turkish.
## Potential Uses:
* Medical research
* Natural Language Processing (NLP)
* Medical education
## Limitations:
* Data privacy concerns
* Variability in answer quality
* Potential biases
## General Assessment:
The Patient Doctor Q&A TR 321179 dataset is a valuable resource for understanding real-world medical communication and information exchange. This dataset, translated into Turkish, is an important resource for medical research and education, and can be used to analyze communication between patients and doctors. However, limitations such as data privacy and variability in answer quality should be considered.

This dataset offers researchers and educators the opportunity to conduct more in-depth analyses and apply natural language processing techniques using Turkish medical communication data.

This dataset has a total of 364,420 pieces of medical QA data, some of which have multiple questions in different ways. We extract medical QA pairs from plain texts (e.g., medical encyclopedias and medical articles). We collected 8,699 encyclopedia entries for diseases and 2,736 encyclopedia entries for medicines on Chinese Wikipedia. Moreover, we crawled 226,432 high-quality medical articles from the Qianwen Health website.

Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III, version 1.4, discharge summaries. These discharge summaries overlap with the n2c2 challenge, so they are filled in with surrogate PHI. We analyze this dataset to characterize the types of information sought by medical experts. We also train baseline models for trigger detection and question generation (QG), paired with unsupervised answer retrieval over EHRs. Our baseline model is able to generate high quality questions in over 62% of cases when prompted with human selected triggers. We release this dataset (and a link to all code to reproduce baseline model results) to facilitate further research into realistic clinical QA and QG.

The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts.

PubMedQA has 1k expert labeled, 61.2k unlabeled and 211.3k artificially generated QA instances.

MQuAD

The Medical Question and Answering dataset(MQuAD) has been refined, including the following datasets. You can download it through the Hugging Face dataset. Use the DATASETS method as follows.

  Quick Guide

from datasets import load_dataset dataset = load_dataset("danielpark/MQuAD-v1")

Medical Q/A datasets gathered from the following websites.

eHealth Forum iCliniq Question Doctors WebMD Data was gathered at the 5th of May 2017.

The MQuAD provides embedded question… See the full description on the dataset page: https://huggingface.co/datasets/danielpark/mquad-v1.

marin-community/datashop-medical-qa dataset hosted on Hugging Face and contributed by the HF Datasets community

bala1524/Medical-QA-Mistral7B-Finetuning dataset hosted on Hugging Face and contributed by the HF Datasets community

Starlord1010/Medical-QA-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset presents the number of medical staff employed in the government sector in the State of Qatar. The data is disaggregated by occupation, including physicians, nurses, pharmacists, dentists, and allied health professionals. It provides insights into workforce trends in the healthcare sector, supporting policy planning and resource allocation for national health services.

Market Overview According to market research, the global Daily QA Check Device market is projected to reach a significant X million value by 2033, expanding at a robust CAGR of X%. This expansion is attributed to various driving factors, including the increasing demand for quality assurance in healthcare, food production, and education. The growing adoption of artificial intelligence (AI) and IoT technologies in these sectors has also contributed to the market's growth. Market Segmentation The Daily QA Check Device market can be segmented based on application, type, and region. By application, the market caters to educational institutions, food production industry, medical institutions, and others. Medical institutions dominate the market due to the stringent regulations for maintaining the accuracy and reliability of medical equipment. By type, the market is classified into basic, intelligent, and professional types. Professional-type devices offer advanced features and automation, leading to their popularity in hospitals and research labs. Regionally, North America holds the largest market share, followed by Europe and Asia Pacific. The Asia Pacific region is expected to witness substantial growth due to the rising demand for quality assurance in emerging economies. Key players in the industry include Guangzhou Raydose Software Technology LLC, Sichuan Jingwei Food Testing Technology, and Shenzhen Ruikang'an Technology Development.

This dataset presents the number of visitors to public health centers in Qatar. It is categorized by the name of the health center and helps evaluate patient load, service demand, and regional distribution of healthcare access across the country.

The X-Ray QA Instruments market is experiencing robust growth, driven by the increasing adoption of advanced imaging technologies in healthcare and stringent regulatory requirements for ensuring radiation safety. The market's expansion is fueled by a rising global population, an aging demographic requiring more frequent medical imaging, and the escalating demand for improved diagnostic accuracy. Technological advancements, including the development of sophisticated and user-friendly QA instruments, are further contributing to market expansion. We estimate the market size to be around $500 million in 2025, demonstrating a significant increase from previous years. This growth is projected to continue at a healthy CAGR (let's assume a conservative 6%), propelled by factors like the increasing prevalence of chronic diseases requiring frequent imaging and the growing adoption of digital X-ray systems. However, market growth is not without its challenges. High initial investment costs for advanced QA instruments can be a barrier for smaller healthcare facilities, particularly in developing economies. Furthermore, the market faces potential restraints from the complexity of instrument operation and maintenance, requiring specialized personnel and potentially increasing operating costs for healthcare providers. Despite these challenges, the long-term outlook for the X-Ray QA Instruments market remains positive, largely due to continuous technological advancements, rising awareness of radiation safety, and the expanding global healthcare infrastructure. The leading players, such as Fluke Biomedical, RaySafe, and others, are actively investing in research and development to introduce innovative products that enhance accuracy, efficiency, and user-friendliness, thus driving further market expansion.