Longitudinal EHR & Electronic Health Records Dataset
Patient Health Records & Digital Health
Tags and Keywords
£400,000
About
A large-scale longitudinal electronic health records (EHR) dataset containing structured clinical data across patient encounters, diagnoses, medications, lab results, and clinical observations over time. Sourced from real clinical environments and de-identified in compliance with applicable privacy regulations. Purpose-built for healthcare AI, clinical NLP, and predictive modelling research.
Data Product Features
- Longitudinal patient records spanning multiple encounters over time
- Structured data: diagnoses (ICD codes), medications, lab values, and vitals
- Clinical observations and free-text notes included
- De-identified in compliance with applicable privacy regulations
- Covers diverse patient demographics and clinical conditions
- Delivered in Excel and PDF formats with structured metadata
Distribution
Format: Excel / PDF
Size: Large-scale
Records: Large-scale collection
Data Volume
Large-scale longitudinal clinical records corpus. Exact volume arranged per project scope. Sample available for evaluation before purchase.
Usage
- Training clinical NLP and medical language models
- Predictive modelling for disease progression and readmission risk
- Drug interaction and medication adherence research
- Building AI-assisted clinical decision support systems
- Population health analytics and epidemiological research
Coverage
Geographic Coverage: India-origin clinical sources
Time Range: Longitudinal — multi-year patient histories
Language: English
License
CC0 — No Rights Reserved
AI Training Rights
Licensee is granted a non-exclusive, worldwide, and perpetual right to use this data product to train, fine-tune, and evaluate machine learning models. The data product itself may not be redistributed or shared outside licensed usage. Licensee must comply with all applicable laws, including data protection and privacy regulations.
Who Can Use It
- Healthcare AI Companies: For training clinical NLP and predictive models
- Hospital Tech Teams: For building clinical decision support tools
- Pharmaceutical Companies: For drug interaction and outcomes research
- Academic Institutions: For epidemiological and population health research
Data Dictionary
- patient_id (string) — De-identified unique patient identifier
- encounter_id (string) — Unique identifier for each clinical encounter
- encounter_date (date) — Date of clinical encounter in YYYY-MM-DD format
- diagnosis_code (string) — ICD-10 diagnosis code
- diagnosis_description (string) — Human-readable diagnosis label
- medication_name (string) — Prescribed medication name
- medication_dosage (string) — Dosage and frequency of medication
- lab_test_name (string) — Name of laboratory test conducted
- lab_result_value (float) — Numeric result of the lab test
- lab_result_unit (string) — Unit of measurement for lab result
- clinical_notes (text) — De-identified free-text clinical observations
- patient_age_group (string) — De-identified age bracket
- patient_gender (string) — De-identified gender
- de_identification_status (string) — Confirmed de-identified flag
TRUSTLoading...
£400,000
Download Dataset in TEXT Format
Recommended Datasets
Loading recommendations...