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Multilingual Healthcare Text Dataset - Hindi-English-Punjabi

This dataset contains patient records with hip health conditions, including avascular necrosis, with detailed information in English, Hindi and Punjabi language.

About Dataset

This dataset comprises patient records with hip health conditions, specifically avascular necrosis and bone marrow edema syndrome, along with detailed information in English, Punjabi and Hindi languages. The dataset includes patient demographics, diagnosis, treatment plans, and patient history. The primary purpose of this dataset is to provide a comprehensive understanding of hip health conditions, enabling healthcare professionals to develop effective treatment strategies. This dataset can be utilized in various applications, including academic research, healthcare policy development, and medical education. Academic Research: Conducting research studies using this dataset to better understand hip health conditions and their impact on patients. 4. Public Health: Identifying patterns and trends in patient outcomes and treatment effectiveness to inform healthcare policy and resource allocation decisions. 5. Business Intelligence: Analyzing patient demographics and treatment plans to identify opportunities for cost savings and improved patient satisfaction. The citation of the dataset - Kajol Bgga. (2025). Multilingual Healthcare Text Dataset (Hi, En, Pu) [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/10744788. This dataset was identified and facilitated for onboarding as part of the Dataset Onboarding Support Team (DOST) initiative led by by CivicDataLab (CDL), partnering with the Gates Foundation in collaboration with BHASHINI. CivicDataLab provided technical support for dataset discovery, validation, metadata preparation and onboarding facilitation. All dataset ownership and intellectual property rights remain with the original author(s).

Purpose of Dataset

The Purpose Of This Dataset Is To Provide Structured Healthcare Data For Understanding Patient Health Conditions, Treatment Patterns, And Healthcare Outcomes. The Dataset Supports Academic Research, Medical Education, Healthcare Analytics, And Public Health Studies By Enabling The Analysis Of Patient Demographics, Medical Conditions, Hospital Records, Treatment Information, And Healthcare Utilization Trends. It Can Also Assist In Identifying Patterns In Patient Outcomes, Treatment Effectiveness, And Healthcare Costs To Support Data-driven Decision-making, Healthcare Policy Development, And Operational Improvements In Healthcare Systems.

Dataset Metadata

License

CC0 1.0 Public Domain

Geographical coverage

Global

Sector

Healthcare, Wellness and Family Welfare

Author

Kajol Bagga

Source organisation

Digital India BHASHINI Division

Uploaded by

Nikil Augustine

Data Quality Score (Beta)

4.75

Dataset type

Structured

Frequency

Time Granularity

Static

Year range

11/08/2024 - 11/10/2024

Date & Time

07/05/26 07:22:59

Visibility

Open

Hosted / Redirected

Hosted

Data Type

Hybrid

Data Collection Method

The Dataset Has Been Curated From Publicly Available Medical Resources, Research Papers, Patient Forums, And Clinical Notes (If Applicable, Mention Specific Sources). Some Data Was Manually Collected And Translated To Ensure Linguistic Diversity And Accuracy. Collection & Processing Text Was Sourced From Medical Websites, Patient Feedback, Health Blogs, And Doctor Consultations (Adjust If Needed). Preprocessing Steps Included: Text Normalization (Removing Unnecessary Symbols, Correcting Spelling Errors). Language Tagging (To Identify And Separate Hindi, English, And Punjabi Text). Anonymization (Removing Any Personal Or Sensitive Information). The Dataset Was Formatted To Be Machine Learning-ready, Suitable For Training Multilingual Nlp Models, Including Transformer-based Architectures Like Bert And Indic-bert. Intended Use Healthcare Text Classification Named Entity Recognition (Ner) Sentiment Analysis In Medical Contexts Multilingual Medical Chatbot Development Domain-specific Language Modeling In Healthcare Limitations & Ethical Considerations The Dataset Does Not Contain Real Patient Records To Ensure Compliance With Privacy Regulations. While Efforts Were Made To Ensure Accuracy, Some Translations And Medical Terms May Require Domain-specific Validation.