clinical_trial_risk/CITATION.cff at main · fastdatascience/clinical_trial_risk · GitHub

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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Clinical Trial Risk Tool: software application using
  natural language processing to identify the risk of trial
  uninformativeness
message: 'If you use this software, please cite it as below.'
type: software
authors:
  - family-names: Wood
    given-names: Thomas Andrew
    orcid: 'https://orcid.org/0000-0001-8962-8571'
  - family-names: McNair
    given-names: Douglas
    orcid: 'https://orcid.org/0000-0003-0965-883X'
identifiers:
  - type: doi
    value: 10.12688/gatesopenres.14416.1
repository-code: 'https://github.com/fastdatascience/clinical_trial_risk'
url: 'https://fastdatascience.com/clinical-trial-risk-tool/'
repository: 'https://gatesopenresearch.org/articles/7-56/v1'
repository-artifact: 'https://clinicaltrialrisk.org/'
abstract: >-
  Background: A large proportion of clinical trials end
  without delivering results that are useful for clinical,
  policy, or research decisions. This problem is called
  “uninformativeness”. Some high-risk indicators of
  uninformativeness can be identified at the stage of
  drafting the protocol, however the necessary information
  can be hard to find in unstructured text documents.

  Methods: We have developed a browser-based tool which uses
  natural language processing to identify and quantify the
  risk of uninformativeness. The tool reads and parses the
  text of trial protocols and identifies key features of the
  trial design, which are fed into a risk model. The
  application runs in a browser and features a graphical
  user interface that allows a user to drag and drop the PDF
  of the trial protocol and visualize the risk indicators
  and their locations in the text. The user can correct
  inaccuracies in the tool’s parsing of the text. The tool
  outputs a PDF report listing the key features extracted.
  The tool is focused HIV and tuberculosis trials but could
  be extended to more pathologies in future.

  Results: On a manually tagged dataset of 300 protocols,
  the tool was able to identify the condition of a trial
  with 100% area under curve (AUC), presence or absence of
  statistical analysis plan with 87% AUC, presence or
  absence of effect estimate with 95% AUC, number of
  subjects with 69% accuracy, and simulation with 98% AUC.
  On a dataset of 11,925 protocols downloaded from
  ClinicalTrials.gov, the tool was able to identify trial
  phase with 75% accuracy, number of arms with 58% accuracy,
  and the countries of investigation with 87% AUC.

  Conclusion: We have developed and validated a natural
  language processing tool for identifying and quantifying
  risks of uninformativeness in clinical trial protocols.
  The software is open-source and can be accessed at the
  following link: https://app.clinicaltrialrisk.org
license: MIT
version: 1
date-released: '2023-09-15'
doi: 10.12688/gatesopenres.14416.1