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Identifying Entities (Diseases, Treatments) in Healthcare Data

Finding diseases and treatments in medical text—because even AI needs a medical degree to understand doctor’s notes! 🩺🤖

📊 Understanding the Dataset

In the contemporary healthcare ecosystem, substantial amounts of unstructured textual facts are generated day by day thru electronic health facts (EHRs), medical doctor’s notes, prescriptions, and medical literature. The potential to extract meaningful insights from this records is critical for improving patient care, advancing clinical studies, and optimizing healthcare offerings. The dataset in cognizance incorporates text-based totally scientific statistics, in which sicknesses and their corresponding remedies are embedded inside unstructured sentences.

The dataset consists of categorized textual content samples, that are classified into: -**Train Sentences**: These sentences comprise clinical records, including patient diagnoses and the treatments administered. -**Train Labels**: The corresponding annotations for the train sentences, marking diseases and remedies as named entities. -**Test Sentences**: Similar to educate sentences however used to evaluate model overall performance. -**Test Labels**: The ground reality labels for the test sentences.

A sneak from the dataset may look as follows:

🔍 Example from Dataset:

Train Sentences:

_ "The patient was a 62 -year -old man with squamous epithelium, who was previously treated with success with a combination of radiation therapy and chemotherapy."

Train Labels:

• Disease: 🦠 lung cancer
• Treatment: 💉 Radiation therapy, chemotherapy

This dataset requires the use of** designated Unit Recognition (NER)** to remove and map and map diseases for related treatments 💊, causing the composition of unarmed medical data for analytical purposes.

⚙️ Dataset Properties

1. Unnecessary medical text: Data set contains free-powered medical notes, where disease and treatment conditions are clearly mentioned. Removing this information without clear mapping is a challenge.
2. Many unit types: Datasets contain different - -called institutions such as diseases, treatment, symptoms and possibly medication.
3. Relevant addiction: Many treatments apply to many diseases, and proper mapping depends on reference. For example, "radiotherapy" is used for different cancers, which makes relevant understanding significantly.
4. Unbalanced data distribution: Some diseases and treatment can be displayed more often than others, to balance model performance requires techniques such as overfalling, sub -sampling or transmission of learning.
5. Domain-specific language: is rich in lesson medical terminology, which requires special preprochet using domain-specific NLP techniques and medical oncology such as UML or SNOM CT.

🚧 Challenges Working with Dataset

• Complex medical vocabulary: Medical texts often use vocals, which require special NLP models that are trained at the clinical company.

• Implicit Relationships: Unlike based datasets, ailment-treatment relationships are inferred from context in preference to explicitly stated.

• Synonyms and Abbreviations: Diseases and treatments can be cited the use of special names (e.G., ‘myocardial infarction’ vs. ‘coronary heart assault’). Handling such versions is vital.

• Noise in Data: Unstructured records may additionally contain irrelevant records, typographical errors, and inconsistencies that affect extraction accuracy.

🛠️ Approach to Extracting Insights from the Dataset

To extract sicknesses and their respective treatments from this dataset, we follow a based NLP pipeline:

1. Data Preprocessing 🧹

• Text Cleaning: Remove needless characters, numbers, and stopwords whilst preserving clinical terms.
• Tokenization: Split sentences into phrases for higher processing.
• Medical Term Standardization: Use area-precise libraries like SciSpacy to standardize synonyms and abbreviations.

2. Named Entity Recognition (NER) Model Development 🤖

• Annotation: Ensure accurate labeling of sicknesses and treatments in the dataset.
• Model Selection: Train a deep-mastering-based version like BioBERT or a rule-based model the use of spaCy.
• Training: Use annotated data to teach a custom NER model that classifies words as sickness or treatment entities.
• Evaluation: Measure precision, bear in mind, and F1-score to evaluate version overall performance.

3. Mapping Diseases to Treatments 🔄

• Contextual Relationship Extraction: Identify which treatment corresponds to which sickness using dependency parsing and courting extraction.
• Dictionary or Tabular Output: Store extracted mappings in a based layout.

Example Output:

| 🦠 Disease | 💉 Treatments | |----------|--------------------...

This dataset was scrapped from the MedQuAD repository and then converted it to a csv file.

IT DOES NOT INCLUDE DATA OF MPlus_ADAM_Encyclopedia, MPlus_Drugs, and MPlus_Herbs_Supplements as their answers were removed from the repository due to copyright strike.

All the files contain below columns:

topic: The broad medical category mapped from the authoritative source institute (e.g., cancer, Heart_Lung_Blood).

focus: The highly specific disease, syndrome, drug, or clinical condition targeted (e.g., Adult Acute Lymphoblastic Leukemia).

qtype: The precise clinical intent of the question (e.g., symptoms, treatment, exams and tests, stages, outlook).

question: The natural language medical query string.

answer: The authoritative, verified medical text passage answering the query.

url: The official NIH source web URL used for lineage tracking and user interface citations.

Medical Topics Covered

The records span 12 standardized categories:

cancer: National Cancer Institute (CancerGov)

Genetic_and_Rare_Diseases: Genetic and Rare Diseases Information Center (GARD)

Genetics_Home_Reference: Genetics Home Reference (GHR - Core genetic conditions)

others: MedlinePlus General Health Topics

Diabetes_Digestive_Kidney: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Neurological_Disorders_Stroke: National Institute of Neurological Disorders and Stroke (NINDS)

seniorHealth: NIH Senior Health Website

Heart_Lung_Blood: National Heart, Lung, and Blood Institute (NHLBI)

Disease_Control_Prevention: Centers for Disease Control and Prevention (CDC)

MedQuAD: Medical Question Answering Dataset

MedQuAD includes 47,457 medical question-answer pairs created from 12 NIH websites (e.g. cancer.gov, niddk.nih.gov, GARD, MedlinePlus Health Topics). The collection covers 37 question types (e.g. Treatment, Diagnosis, Side Effects) associated with diseases, drugs and other medical entities such as tests.

We included additional annotations in the XML files, that could be used for diverse IR and NLP tasks, such as the question type, the question focus, its syonyms, its UMLS Concept Unique Identifier (CUI) and Semantic Type. We added the category of the question focus (Disease, Drug or Other) in the 4 MedlinePlus collections. All other collections are about diseases.

The paper cited below describes the collection, the construction method as well as its use and evaluation within a medical question answering system.

N.B. We removed the answers from 3 subsets to respect the MedlinePlus copyright (https://medlineplus.gov/copyright.html): (1) A.D.A.M. Medical Encyclopedia, (2) MedlinePlus Drug information, and (3) MedlinePlus Herbal medicine and supplement information. -- We kept all the other information including the URLs in case you want to crawl the answers. Please contact me if you have any questions.

Reference

If you use the MedQuAD dataset and/or the collection of 2,479 judged answers, please cite the following paper: "A Question-Entailment Approach to Question Answering". Asma Ben Abacha and Dina Demner-Fushman. BMC Bioinformatics, 2019.

@ARTICLE{BenAbacha-BMC-2019,
author = {Asma {Ben Abacha} and Dina Demner{-}Fushman}, title = {A Question-Entailment Approach to Question Answering}, journal = {{BMC} Bioinform.}, volume = {20}, number = {1}, pages = {511:1--511:23}, year = {2019}, url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3119-4} }

This dataset describes the Release File structure of SNOMED CT UK Drug Extension, referred to as Release Format 2 (RF2). The UK Edition of SNOMED CT is the official source of SNOMED CT for use in UK healthcare systems. The UK Edition is a standalone release that combines the content of both the US Extension and the International release of SNOMED CT

A Simple reference set does not have any addition fields.

The medical reference app market is booming, projected to reach $7.7 billion by 2033, driven by mobile healthcare adoption and AI integration. Explore market trends, key players (WebMD, UpToDate, etc.), and regional growth in this comprehensive analysis.

Unlock valuable biomedical knowledge with our comprehensive PubMed Dataset, designed for researchers, analysts, and healthcare professionals to track medical advancements, explore drug discoveries, and analyze scientific literature.

Dataset Features

Scientific Articles & Abstracts: Access structured data from PubMed, including article titles, abstracts, authors, publication dates, and journal sources. Medical Research & Clinical Studies: Retrieve data on clinical trials, drug research, disease studies, and healthcare innovations. Keywords & MeSH Terms: Extract key medical subject headings (MeSH) and keywords to categorize and analyze research topics. Publication & Citation Data: Track citation counts, journal impact factors, and author affiliations for academic and industry research.

Customizable Subsets for Specific Needs Our PubMed Dataset is fully customizable, allowing you to filter data based on publication date, research category, keywords, or specific journals. Whether you need broad coverage for medical research or focused data for pharmaceutical analysis, we tailor the dataset to your needs.

Popular Use Cases

Pharmaceutical Research & Drug Development: Analyze clinical trial data, drug efficacy studies, and emerging treatments. Medical & Healthcare Intelligence: Track disease outbreaks, healthcare trends, and advancements in medical technology. AI & Machine Learning Applications: Use structured biomedical data to train AI models for predictive analytics, medical diagnosis, and literature summarization. Academic & Scientific Research: Access a vast collection of peer-reviewed studies for literature reviews, meta-analyses, and academic publishing. Regulatory & Compliance Monitoring: Stay updated on medical regulations, FDA approvals, and healthcare policy changes.

Whether you're conducting medical research, analyzing healthcare trends, or developing AI-driven solutions, our PubMed Dataset provides the structured data you need. Get started today and customize your dataset to fit your research objectives.

This dataset consists of electronic medical records collected from healthcare information systems and publicly available clinical data repositories. It includes structured clinical attributes related to patient demographics, medical conditions, hospital encounters, and treatment-related information. The dataset is designed to support research on medical data organization, secure access, and relevance-based information retrieval within healthcare environments.

All records are anonymized to protect patient confidentiality, ensuring compliance with ethical data usage standards. The dataset enables exploration of semantic relevance in medical data access, efficient record retrieval, and secure handling of sensitive healthcare information. It is suitable for developing and evaluating healthcare data search systems that emphasize accurate information access while maintaining data privacy and access accountability.

Patient Demographics: gender: Male, Female, Unknown age ethnicity

Hospital Details: hospitalid: Each hospital was given unique id wardid: Ward Id is given in which patient was treated apacheadmissiondx: Disease diagnosed admissionheight: Height of the patients hospitaladmittime24: Admission time to the hospital hospitaladmitsource: Department Source of the admission hospitaldischargeyear: Discharge year from the hospital hospitaldischargetime24: Discharge time from the hospital hospitaldischargelocation: Patient Discharge to which location (Home, Death, Other hospital. etc) hospitaldischargestatus (Alive, Expired)

Hospital Unit Details: unittype: Unit in which admitted unitadmittime24: Time of admision to the Unit unitadmitsource: Department source for the unit unitvisitnumber: No. of times visited unitstaytype: Admit, readmit, etc admissionweight: Weight during the admission dischargeweight: Weight during the Discharge unitdischargetime24: Discharge time from the Unit unitdischargelocation: Patient Discharge to which location (Home, Death, Other hospital. etc) unitdischargestatus: (Alive, Expired)

Column Descriptions

patient_id – Unique identifier assigned to each patient record.

anonymized_patient_id – Privacy-preserving identifier used to protect patient identity.

age – Age of the patient at the time of record entry.

gender – Biological gender of the patient.

admission_type – Type of hospital admission associated with the record.

diagnosis – Medical condition or diagnosis recorded for the patient.

procedure – Clinical procedure or medical intervention documented.

medication – Medication or treatment information associated with the patient.

hospital_stay – Duration or details of the patient’s hospital visit.

clinical_text – Consolidated clinical information representing patient medical context.

search_query – Medical information request used for record retrieval evaluation.

permission_level – Authorized access role associated with the medical record.

access_hash – Cryptographic reference used to record secure access events.

relevance_score – Degree of relevance between a query and a medical record.

The global mHealth Apps market size was valued USD 31.21 billion in 2023 and is expected to rise to USD 105.57 billion by 2032 at a CAGR of 14.5%.

SPARQL access to the SPHN data

Graph and download economic data for Pharmaceutical and Other Medical Products Expenditures (PHMEPREXPHCSA) from 2000 to 2021 about pharmaceuticals, healthcare, medical, health, expenditures, and USA.

The size of the Clinical reference Laboratories Market was valued at USD 124.69 Billion in 2023 and is projected to reach USD 180.18 Billion by 2032, with an expected CAGR of 5.40% during the forecast period. Recent developments include: December 2020- TriCore Reference Laboratories, an independent clinical reference laboratory sponsored by Presbyterian Healthcare Services and University of New Mexico Health Sciences Center, established a new branch lab at New Mexico State University (NMSU). To date, it has processed more than 15,000 COVID-19 tests.. Key drivers for this market are: Increasing Value-Based Outsourcing from Hospitals will Boost the Market for Clinical Reference Lab Services 18, Application of Advanced Automation Technology in Reference Laboratories 18; Cost and Time Savings for Hospitals and Clinics Due to Increase in Outsourcing by Independent Laboratories 18. Potential restraints include: Healthcare Budgetary Restrictions Will Hamper the Growth of the Market 19, High Costs of Advanced Technologies May Lead to an Increase in the Costs of Specialised Tests 19.

A collection of online books and documents in life science and healthcare whose full text can be searched through the Entrez system. Bookshelf provides free online access to books and documents in life science and healthcare.

Medical Questions: Unveiling the MedQuAD Dataset

Have you ever wondered where medical chatbots or intelligent search engines for health information get their knowledge? The answer lies in large datasets like MedQuAD! This rich resource provides a treasure trove of real-world medical questions and informative answers, paving the way for advancements in Natural Language Processing (NLP) and Information Retrieval (IR) within the healthcare domain.

What is MedQuAD?

MedQuAD, short for Medical Question Answering Dataset, is a collection of question-answer pairs meticulously curated from 12 trusted National Institutes of Health (NIH) websites. These websites cover a wide range of health topics, from cancer.gov to GARD (Genetic and Rare Diseases Information Resource).

What makes MedQuAD unique?

Beyond the sheer volume of data, MedQuAD offers unique features that empower researchers and developers:

1. Diversity of Questions: MedQuAD encompasses a spectrum of 37 question types, ranging from treatment options and diagnosis inquiries to understanding side effects. This variety reflects the diverse needs of individuals seeking medical information.
2. Focus on Specific Entities: MedQuAD goes beyond just questions and answers. It delves deeper by associating each question with the entity it focuses on, such as diseases, drugs, or other medical tests. This targeted approach facilitates more focused research and NLP applications.
3. Rich Annotations: While the answers from MedlinePlus collections are excluded due to copyright restrictions, MedQuAD retains valuable annotations within its XML files. These annotations include question type, synonyms, unique identifiers (CUI) for medical concepts, and semantic types. This additional information opens doors for more sophisticated NLP tasks.

The Power of MedQuAD

MedQuAD serves as a valuable springboard for various applications in the medical NLP and IR field. Here are some potential uses:

1. Training Chatbots and Virtual Assistants: AI-powered medical chatbots can leverage MedQuAD to learn how to respond accurately and informatively to a wide range of health inquiries from users.
2. Developing Intelligent Search Engines: Search engines can be enhanced to provide more relevant and accurate health information by drawing insights from the question types and focuses presented in MedQuAD.
3. Studying User Concerns in Healthcare: Analyzing the types of questions within MedQuAD can reveal valuable insights into what information users are most interested in and what areas require clearer explanations.

In essence, MedQuAD is a powerful tool for unlocking the potential of NLP and IR in the medical domain. By leveraging this rich dataset, researchers and developers are paving the way for a future where individuals can access accurate and comprehensive health information with increasing ease and efficiency.

Reference:

If you use the MedQuAD dataset or the associated QA test collection, please cite the following paper: Ben Abacha, A., & Demner-Fushman, D. (2019). A Question-Entailment Approach to Question Answering. BMC Bioinformatics, 20(1), 511. https://doi.org/10.1186/s12859-019-3119-4

🏥 Synthetic Patient Records — EHR Dataset (2018–2024)

100K patients · 15 conditions · ICD-10 codes · lab results · medications · hospitalisations · 5 files

About

A comprehensive synthetic Electronic Health Records (EHR) dataset modelling US adult patients from 2018 to 2024. Built on CDC/NIH/NHANES population statistics to reflect realistic disease prevalence, lab reference ranges, medication patterns, and clinical outcomes.

Why synthetic EHR? Real patient data cannot be shared publicly due to HIPAA. This dataset fills the gap for students and researchers who want to practise clinical ML, survival analysis, readmission prediction, and medical NLP — without access to a hospital system.

Calibrated to: - CDC National Health and Nutrition Examination Survey (NHANES) — vitals, lab ranges - CDC National Center for Health Statistics — disease prevalence rates - CMS Medicare/Medicaid — hospitalisation and readmission benchmarks - WHO ICD-10 classification system — diagnosis codes

Files

Conditions Modelled (15 total)

Key Columns — patients.csv

Key Columns — lab_results.csv

Key Columns — outcomes.csv

Suggested Research Tasks

Clinical prediction models: ```python

30-day readmission prediction (classic clinical ML task)

Features: age, charlson_index, LOS, diagnosis, vitals

Target: readmitted_30d

Challenge: imbalanced (~15% positive rate)

**Lab value prediction / imputation:**
Predict missing lab values from demographics and comorbidities. Common in real EHR settings where not all tests are ordered.

**Survival analysis:**
Use `outcomes.csv` + `patients.csv` for time-to-event analysis. Cox proportional hazards, Kaplan-Meier by condition subgroup.

**Medication adherence:**
Predict `adherence_pct` from patient character...

Structured, evidence-based clinical reference content organized by clinical department (vascular access, ICU, ED, oncology, infection prevention) and content type (guidelines, guides, policies, patient education). Authored and reviewed by practicing clinicians.

Source: Study data.

Graph and download economic data for Employment for Health Care and Social Assistance: Medical and Diagnostic Laboratories (NAICS 6215) in the United States (IPURN6215W010000000) from 1987 to 2025 about diagnostic labs, healthcare, social assistance, medical, health, NAICS, IP, employment, and USA.

Dataset Card for "medical-domain"

  Dataset Summary

Medical transcription data scraped from mtsamples.com Medical data is extremely hard to find due to HIPAA privacy regulations. This dataset offers a solution by providing medical transcription samples. This dataset contains sample medical transcriptions for various medical specialties.

  Languages

english

  Citation Information

Acknowledgements Medical transcription data scraped from mtsamples.com… See the full description on the dataset page: https://huggingface.co/datasets/argilla/medical-domain.

BioMedSearch is a biomedical search engine that contains NIH/PubMed documents, plus a large collection of theses, dissertations, and other publications not found anywhere else for free, making it the most comprehensive free search on the web. :Besides free-form search, users can search based on Author, Journal Title, Publication Date, the Language in which the article was published (many non-English articles have English language abstracts), MeSH (Medical Subject Headings) and more. : The goal of BioMedSearch.com is to provide free access to a massive collection of authoritative documents relating to the biomedical field. Our mission is to make these important works available to the community in a way that is fast and easy, while still offering the advanced features demanded by power users such as portfolios, collaboration features, bibliographical citation export, alerts, and more. Whether you are doctor, scientist, or someone interested in researching a medical topic out of personal interest, BioMedSearch aggregates a vast number of authoritative documents in one place to make finding medical information easy, fast and free.

This dataset is the public medical text record (progress notes) written in Japanese.

Any researchers can use this dataset without privacy issues.

CC BY-NC 4.0

crowd.zip: 9,756 pseudo progress notes written by crowd workers

crowd_evaluated.zip: 83 pseudo progress notes with authentic quality written by crowd workers

MD.zip: 19 pseudo progress notes written by medical doctors

Reference:

Kagawa, R., Baba, Y., & Tsurushima, H. (2021, December). A practical and universal framework for generating publicly available medical notes of authentic quality via the power of crowds. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 3534-3543). IEEE.

http://hdl.handle.net/2241/0002002333

The supplemental files of the paper are here: https://github.com/rinabouk/HMData2021