A Text-Mining Algorithm for Identifying Drug Pairs Potentially Involved in Prescribing Cascades: Development and Internal Validation Within the Prescribing Inappropriateness Assessment (PINA) Digital Tool - Report - MDSpire
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An Algorithm for Text Mining to Detect Potential Drug Pair Interactions in Prescribing Cascades: Development and Internal Validation Using the Prescribing Inappropriateness Assessment (PINA) Tool

  • By

  • Massimo Carollo

  • Anna Forti

  • Salvatore Crisafulli

  • Luca L’Abbate

  • Irene Cristini

  • Federica Soardo

  • Marilisa Giustina Stano

  • David Bellantuono

  • Davide Benetti

  • Riccardo Lora

  • Gianluca Trifirò

  • September 19, 2026

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Clinical Report: An Algorithm for Text Mining to Detect Potential Drug Pair Interactions

Overview

An algorithm was developed and internally validated to identify drug pairs that may indicate prescribing cascades during medication reviews.

Background

Polypharmacy, defined as the concurrent use of five or more medications, is increasingly prevalent, particularly among older patients. This complexity raises the risk of adverse drug reactions (ADRs) and prescribing cascades, where an ADR from one medication leads to additional prescriptions.

Data Highlights

No numerical data or trial data presented in the source material.

Key Findings

  • The algorithm is based on regulatory product information.
  • Internal validation showed few spurious matches in drug pair identification.
  • Broader terminology-based matching improved detection of relevant drug pairs.
  • The tool does not confirm prescribing cascades or initiate automatic deprescribing.
  • The PINA software is intended to assist healthcare professionals in structured medication reviews.

Clinical Implications

The algorithm prioritizes candidate drug pairs for clinical assessment. It does not replace clinical judgment or confirm prescribing cascades.

Conclusion

The development of this algorithm is aimed at identifying potential prescribing cascades.

Related Resources & Content

  1. Drug Safety, 2020 -- Assessing the Viability of Detecting Drug–Drug Interaction Signals within Standard Pharmacovigilance Practices
  2. npj Digital Medicine, 2026 -- Predicting Drug-Drug Interactions: From Computational Machine Learning Approaches to Clinical Implementation
  3. Drug Safety, 2014 -- Identification of Dose-Related Adverse Drug Reactions Using Temporal Data Mining in Electronic Health Records of Inpatient Psychiatric Patients
  4. Drug Safety, 2025 -- Utilizing Network Analysis and Machine Learning for Signal Identification and Prioritization Through Electronic Health Records and Administrative Databases: A Conceptual Exploration in Drug-Induced Acute Myocardial Infarction
  5. Scottish Polypharmacy Guidance, 2026-2029 -- Polypharmacy Guidance: appropriate prescribing, making medicines safe, effective and sustainable
  6. Drugs & Aging, 2026 -- Prescribing Cascades: An Umbrella Review and Updated Systematic Review
  7. 4. General Principles - Polypharmacy Guidance: appropriate prescribing, making medicines safe, effective and sustainable 2026 - 2029 - gov.scot
  8. Prescribing Cascades: An Umbrella Review and Updated Systematic Review | Drugs & Aging | Springer Nature Link
  9. Effective deprescribing strategies for reducing potentially inappropriate medications and improving economic outcomes in community-based settings: a systematic review and meta-analysis - PMC

Original Source(s)

A Text-Mining Algorithm for Identifying Drug Pairs Potentially Involved in Prescribing Cascades: Development and Internal Validation Within the Prescribing Inappropriateness Assessment (PINA) Digital Tool

  • by Massimo Carollo, Anna Forti, Salvatore Crisafulli, Luca L’Abbate, Irene Cristini, Federica Soardo, Marilisa Giustina Stano, David Bellantuono, Davide Benetti, Riccardo Lora, Gianluca Trifirò

  • September 19, 2026

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