A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

Iglesia Jiménez, Diana de la and García Remesal, Miguel and Anguita Sanchez, Alberto and Muñoz Mármol, Miguel and Kulikowski, Casimir and Maojo Garcia, Victor Manuel (2014). A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov. "Plos One", v. 9 (n. 10); pp.. ISSN 1932-6203. https://doi.org/10.1371/journal.pone.0110331.

Description

Title: A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov
Author/s:
  • Iglesia Jiménez, Diana de la
  • García Remesal, Miguel
  • Anguita Sanchez, Alberto
  • Muñoz Mármol, Miguel
  • Kulikowski, Casimir
  • Maojo Garcia, Victor Manuel
Item Type: Article
Título de Revista/Publicación: Plos One
Date: October 2014
ISSN: 1932-6203
Volume: 9
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Funding Projects

TypeCodeAcronymLeaderTitle
FP7270089p-medicineUNIVERSITAT DES SAARLANDESFrom data sharing and integration via VPH models to personalised medicine
FP7288048EURECAPHILIPS ELECTRONICS NEDERLAND B.V.Enabling information re-Use by linking clinical REsearch and CAre
Government of SpainFIS/AES PS09/0006UnspecifiedUnspecifiedUnspecified

More information

Item ID: 32554
DC Identifier: http://oa.upm.es/32554/
OAI Identifier: oai:oa.upm.es:32554
DOI: 10.1371/journal.pone.0110331
Official URL: http://dx.plos.org/10.1371/journal.pone.0110331
Deposited by: Memoria Investigacion
Deposited on: 05 Nov 2014 12:29
Last Modified: 16 May 2019 08:10
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