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Latent Dirichlet Allocation in predicting clinical trial terminations.

Simon Geletta1, Lendie Follett2, Marcia Laugerman2

  • 1Department of Public Health, Des Moines University, 169 Ryan Hall, 3200 Grand Ave, Des Moines, IA, USA. sgeletta@dmu.edu.

BMC Medical Informatics and Decision Making
|November 29, 2019
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Natural language processing and machine learning accurately predict clinical trial success. Identifying patterns in study narratives helps distinguish completed trials from those that terminate, improving research viability.

Keywords:
Clinical trialsLatent Dirichlet allocationPredictionStructured dataUnstructured data

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Area of Science:

  • Computational linguistics
  • Biomedical informatics
  • Machine learning in healthcare

Background:

  • A significant percentage of funded research studies terminate without successful outcomes.
  • The reasons behind study terminations are not well understood, representing a knowledge gap.
  • High termination rates are concerning given the rigorous planning and peer-review of funded studies.

Purpose of the Study:

  • To identify factors contributing to clinical study failures.
  • To distinguish between studies that complete successfully and those that terminate.
  • To leverage natural language processing and machine learning for predicting study outcomes.

Main Methods:

  • Utilized data from ClinicalTrials.gov, including structured and unstructured narrative data.
  • Applied Latent Dirichlet Allocation (LDA) to derive 25 topics from narrative data.
  • Developed random forest models using structured data alone and combined with LDA-derived topics to predict study termination.

Main Results:

  • Latent Dirichlet Allocation (LDA) demonstrated significant interpretive and predictive value for clinical trial failure.
  • A combined modeling approach using structured data and text topics yielded robust predictive probabilities.
  • The model incorporating text topics showed improved sensitivity and specificity compared to using structured data alone.

Conclusions:

  • Topic modeling with LDA enhances the utility of unstructured data for predicting study completion versus termination.
  • This research provides a foundation for future studies evaluating the viability of health study designs.
  • The findings suggest a novel approach to proactively identify and potentially mitigate risks of clinical trial failure.