ReviewTherapeutic innovation & regulatory science2021
Text Classification for Clinical Trial Operations: Evaluation and Comparison of Natural Language Processing Techniques.
Review in Therapeutic innovation & regulatory science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- CNNeoPP: a large language model-enhanced deep learning pipeline for personalized neoantigen prediction and liquid biopsy applications.Frontiers in immunology · 2026Article
- Using Large Language Models for Advanced and Flexible Labelling of Protocol Deviations in Clinical Development.Therapeutic innovation & regulatory science · 2025Article
- Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification.Scientific reports · 2025Article
- Challenges and opportunities in cancer immunotherapy: a Society for Immunotherapy of Cancer (SITC) strategic vision.Journal for immunotherapy of cancer · 2024Review
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- An Improved Deep Learning Model: S-TextBLCNN for Traditional Chinese Medicine Formula Classification.Frontiers in genetics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ability to detect patterns and trends across protocol deviations (PDs) is key to ensure high data quality and sufficient oversight of patient safety. In clinical trial operations, some business processes and work instructions limit efficient protocol deviation trending because a majority of protocol deviations are left unclassified. When this occurs, it restricts clinical teams from determining systemic issues or signals in the data. The unstructured text in protocol deviation descriptions is an important component of trial operation knowledge. Natural language processing (NLP) can make protocol deviation descriptions more accessible and can support information extraction and trending analysis. This paper reviews how the natural language processing techniques of Term-Frequency Inverse-Document-Frequency (TF-IDF) combined with the supervised machine learning model of Support Vector Machines (SVM) and word embedding approaches such as word2vec can be used to categorize/label protocol deviations across multiple therapeutic areas. NLP is a key tool that will lead to more data driven decisions in clinical trial operations.
Indexed as
Identifiers
33125616What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.