Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov
Year:2014
Research Areas
  • Biomedicine,
  • Information technology and adata processing,
  • Medical computing
Information
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.
International
Si
JCR
Si
Title
Plos One
ISBN
1932-6203
Impact factor JCR
3,73
Impact info
Datos JCR del año 2012
Volume
9
10.1371/journal.pone.0110331
Journal number
10
From page
e110331
To page
e110331
Month
OCTUBRE
Ranking
Cuartil Q1"Multidisciplinary Sciences", ranking 8/55.
Participants
  • Autor: Diana De la Iglesia Jimenez (UPM)
  • Autor: Miguel Garcia Remesal (UPM)
  • Autor: Alberto Anguita Sanchez (UPM)
  • Autor: Miguel Muñoz Mármol (UPM)
  • Autor: Casimir Kulikowski (Rutgers University)
  • Autor: Victor Manuel Maojo Garcia (UPM)
Research Group, Departaments and Institutes related
  • Creador: Departamento: Inteligencia Artificial
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)