Literature DB >> 28690394

A Software Application for Mining and Presenting Relevant Cancer Clinical Trials per Cancer Mutation.

Lisa M Gandy1, Jordan Gumm1, Amanda L Blackford2, Elana J Fertig2, Luis A Diaz2.   

Abstract

ClinicalTrials.org is a popular portal which physicians use to find clinical trials for their patients. However, the current setup of ClinicalTrials.org makes it difficult for oncologists to locate clinical trials for patients based on mutational status. We present CTMine, a system that mines ClinicalTrials.org for clinical trials per cancer mutation and displays the trials in a user-friendly Web application. The system currently lists clinical trials for 6 common genes (ALK, BRAF, ERBB2, EGFR, KIT, and KRAS). The current machine learning model used to identify relevant clinical trials focusing on the above gene mutations had an average 88% precision/recall. As part of this analysis, we compared human versus machine and found that oncologists were unable to reach a consensus on whether a clinical trial mined by CTMine was "relevant" per gene mutation, a finding that highlights an important topic which deems future exploration.

Entities:  

Keywords:  clinicaltrials.gov; gene-specific therapies; information retrieval; machine learning; natural language processing

Year:  2017        PMID: 28690394      PMCID: PMC5485907          DOI: 10.1177/1176935117711940

Source DB:  PubMed          Journal:  Cancer Inform        ISSN: 1176-9351


  4 in total

1.  Towards automatic recognition of scientifically rigorous clinical research evidence.

Authors:  Halil Kilicoglu; Dina Demner-Fushman; Thomas C Rindflesch; Nancy L Wilczynski; R Brian Haynes
Journal:  J Am Med Inform Assoc       Date:  2008-10-24       Impact factor: 4.497

2.  Learning regular expressions for clinical text classification.

Authors:  Duy Duc An Bui; Qing Zeng-Treitler
Journal:  J Am Med Inform Assoc       Date:  2014-02-27       Impact factor: 4.497

3.  Feasibility of Large-Scale Genomic Testing to Facilitate Enrollment Onto Genomically Matched Clinical Trials.

Authors:  Funda Meric-Bernstam; Lauren Brusco; Kenna Shaw; Chacha Horombe; Scott Kopetz; Michael A Davies; Mark Routbort; Sarina A Piha-Paul; Filip Janku; Naoto Ueno; David Hong; John De Groot; Vinod Ravi; Yisheng Li; Raja Luthra; Keyur Patel; Russell Broaddus; John Mendelsohn; Gordon B Mills
Journal:  J Clin Oncol       Date:  2015-05-26       Impact factor: 44.544

4.  Deafness mutation mining using regular expression based pattern matching.

Authors:  Christopher M Frenz
Journal:  BMC Med Inform Decis Mak       Date:  2007-10-25       Impact factor: 2.796

  4 in total
  1 in total

1.  Unique insights from ClinicalTrials.gov by mining protein mutations and RSids in addition to applying the Human Phenotype Ontology.

Authors:  Shray Alag
Journal:  PLoS One       Date:  2020-05-27       Impact factor: 3.240

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.