Dataset Card for MedLens Drug-Drug Interaction (DDI) Evidence

This dataset is a curated, multi-regional drug-drug interaction (DDI) and adverse drug event (ADE) signal database compiled from US, EU/EEA, and India regulatory and pharmacovigilance sources.

It is structured as a SQLite database intended for deterministic offline medication-safety lookups.

This also has a mobile version to put in mobile applications.

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Dataset Details

Dataset Description

The MedLens DDI Evidence dataset aggregates prioritized DDI/ADE screening signals from five regional source files covering the US, EU/EEA, and India.

Each record links a drug pair to one or more adverse effects, a severity level, interaction category, mechanism/rationale, evidence basis, source URLs, patient risk flags, and population relevance notes.

The database contains:

• 21,810 known interaction pairs
• 105,460 pair-level adverse effect rows
• 162,600 raw DDI signal rows (original source-level records, pre-deduplication)

Severity Breakdown Across Interaction Pairs

Severity	Pairs
Major	16,174
Moderate	5,523
Minor	113

• Curated by: Ashutosh
• Language(s): English (drug names are generic/INN)
• License: CC BY 4.0

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Dataset Sources

• Repository: MedLens on GitHub (update with actual URL)

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Uses

Direct Use

• Offline/on-device medication safety screening
• Drug-drug interaction lookup for clinical decision support prototypes
• Pharmacovigilance research and signal analysis
• Training or evaluation data for NLP models that classify or explain DDI severity
• Regional comparison of interaction signals (US vs EU/EEA vs India)

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Out-of-Scope Use

• Not a substitute for clinical judgment or a licensed drug interaction database. Signals are screening-level, not patient-specific diagnoses or causality determinations.

• Should not be used to generate or imply patient-specific medical advice.

• Not suitable for production clinical systems without expert validation and regulatory clearance.

• Does not cover all known drug interactions; coverage is limited to the source files below.

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Dataset Structure

The SQLite database contains five tables.

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known_interaction

Deduplicated drug-pair level interaction records.

Column	Type	Description
drug_a, drug_b	TEXT	Normalized generic drug names (alphabetical order)
severity	TEXT	Consensus severity: Major, Moderate, or Minor
severity_rank	INTEGER	Numeric rank (higher = more severe)
row_count	INTEGER	Number of raw signals contributing to this pair
source_regions_json	TEXT	JSON array of regions (e.g. ["us","eu/eea"])
evidence_bases_json	TEXT	JSON array of evidence basis strings
source_bases_json	TEXT	JSON array of source systems
source_urls_json	TEXT	JSON array of reference URLs
mechanisms_json	TEXT	JSON array of mechanism/rationale strings
risk_flags_json	TEXT	JSON array of patient risk flag strings
dataset_types_json	TEXT	JSON array of dataset type labels
use_case_notes_json	TEXT	JSON array of use-case notes

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known_interaction_effect

One row per (pair, adverse effect, severity) combination.

Column	Type	Description
known_interaction_id	INTEGER	FK → known_interaction.id
adverse_effect	TEXT	Adverse effect name (e.g. qt prolongation, myelosuppression)
severity	TEXT	Effect-level severity
row_count	INTEGER	Number of raw signals with this effect
source_regions_json	TEXT	JSON array of regions

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ddi_raw_signal

Original source-level records before deduplication (162,600 rows).

Key Columns

• source_file
• region
• drug1_raw
• drug2_raw
• normalized_drug1
• normalized_drug2
• adverse_effect
• severity
• mechanism_or_rationale
• interaction_category
• interaction_direction
• evidence_basis
• source_basis
• source_urls
• population_relevance
• patient_risk_flags
• dataset_type
• use_case_note

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evidence_import_file

Provenance table — one row per source CSV.

Source File	Region	Rows Seen	Rows Imported	Unique Pairs
usa_prioritized_ddi_ade_signals.csv	us	33,306	31,666	5,770
eu_eea_prioritized_ddi_ade_signals.csv	eu/eea	58,567	50,911	8,724
india_prioritized_ddi_ade_signals.csv	india	10,430	10,139	1,915
india_expanded_prioritized_ddi_ade_signals.csv	india_expanded	55,297	49,188	14,565
india_common_generic_ddi_5000.csv	india_common_generic	5,000	5,000	3,471

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ddi_import_issue

Contains 15,696 rows flagging drug name pairs that could not be resolved to canonical entries during import.

This table is useful for improving normalization coverage.

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Dataset Creation

Curation Rationale

Existing open DDI datasets tend to be US-centric or require a network connection.

This dataset was assembled to enable fully offline, multi-regional medication safety checks — particularly for India, where brand-name and generic drug usage patterns differ significantly from the US/EU baseline.

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Source Data

Data Collection and Processing

Raw DDI/ADE signals were compiled from the following public regulatory and pharmacovigilance sources.

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US Signals

• DailyMed — FDA-approved drug labeling
• TWOSIDES — Post-market drug interaction database
• CMS Part D Drug Spending Dashboard
• ClinCalc Top-300 Drugs 2023

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EU/EEA Signals

• EMA SmPC (Summary of Product Characteristics)
• EMA DDI Guideline
• EudraVigilance — EU adverse reaction reports
• EU ADR Reports / EU Union Register
• WHO ATC/DDD Index

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India Signals

• PVPI — Pharmacovigilance Programme of India
• NLEM 2022 (National List of Essential Medicines)
• TWOSIDES

Drug names were normalized to generic/INN form.

Pairs were deduplicated by canonical (drug_a, drug_b) sorted order.

Severity was aggregated by taking the highest severity signal per pair.

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Who Are the Source Data Producers?

Data originates from:

• US FDA (labeling, FAERS)
• European Medicines Agency (EMA)
• Indian Pharmacopoeia Commission / PVPI
• Academic pharmacoepidemiology projects (TWOSIDES)
• CMS (Medicare Part D utilization data)

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Personal and Sensitive Information

This dataset contains no patient-level data.

All records are drug-pair-level aggregate signals.

No personally identifiable information (PII) is present.

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Bias, Risks, and Limitations

• Signal, not causality:
  All records are screening-level DDI/ADE signals. They indicate a potential interaction of concern, not a confirmed causal relationship for any individual patient.

• Coverage gaps:
  15,696 raw signals (≈9%) could not be resolved to canonical drug names and are excluded from the deduplicated interaction table.

• Severity aggregation:
  Where multiple sources disagree on severity, the highest severity is taken. This may overstate risk for some pairs.

• Regional bias:
  India coverage is strong for common generics but may underrepresent rare drugs. US coverage skews toward high-prescription-volume drugs (Top-300 proxy).

• Temporal limitations:
  Signal databases are periodically updated; this snapshot reflects the state at import time.

• Not exhaustive:
  Absence of a pair from this dataset does not mean the combination is safe.

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Recommendations

• Always cross-reference with current product labeling and consult a qualified healthcare professional before making clinical decisions.

• Use the ddi_import_issue table to identify drug names that failed normalization and expand coverage over time.

• Treat Major severity pairs as requiring clinical review.

• Treat Minor severity pairs as informational.

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Glossary

Term	Meaning
DDI	Drug-Drug Interaction
ADE	Adverse Drug Event
FAERS	FDA Adverse Event Reporting System
PVPI	Pharmacovigilance Programme of India
NLEM	National List of Essential Medicines (India)
INN	International Nonproprietary Name (generic drug name)
TWOSIDES	A database of drug pair adverse effects derived from post-market surveillance data
SmPC	Summary of Product Characteristics (EU equivalent of US drug labeling)

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Dataset Card Contact

prog.mishra@gmail.com

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Citation

If you use this dataset, please cite:

@dataset{mishra2026medlens,
  author       = {Ashutosh Mishra},
  title        = {MedLens: Multi-Regional Drug-Drug Interaction (DDI) Evidence Dataset},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/ASHu2/medlens},
  version      = {1.0},
  license      = {CC-BY-4.0}
}