ArticlePancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]2024
Identification of pancreatic cancer risk factors from clinical notes using natural language processing.
Article in Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.], 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed, 8 citations in OpenAlex.
- Leveraging AI to Enhance Electronic Health Records.EJIFCC · 2025Review
- Clinical Information Extraction From Notes of Veterans With Lymphoid Malignancies: Natural Language Processing Study.JMIR medical informatics · 2025Article
- The exposome as a target for primary prevention and a tool for early detection of pancreatic cancer.Best practice & research. Clinical gastroenterology · 2025Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesScreening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR.
methodsWe first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall.
resultsIn the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F
conclusionsRule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.
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What Socratic 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 Socratic graph.