ArticleJournal of biomedical informatics2022
Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer.
Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Diagnostic Risk Prediction Models for Upper Gastrointestinal Cancers: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Diagnosis methods for pancreatic cancer with the technique of deep learning: a review and a meta-analysis.Frontiers in oncology · 2025Pooled it
- Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.The American journal of gastroenterology · 2024Pooled it
- Ensemble deep learning model based on CT scans: differentiating and subtype-classifying pancreatic inflammations and tumors, and predicting pancreatic lesion invasiveness.Quantitative imaging in medicine and surgery · 2026Article
- Article
- Review
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Article
- Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.BMC medical research methodology · 2025Article
- Improved accuracy and efficiency of primary care fall risk screening of older adults using a machine learning approach.Journal of the American Geriatrics Society · 2024Article
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
- Diagnostic ability of deep learning in detection of pancreatic tumour.Scientific reports · 2023Article
- A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain.Healthcare (Basel, Switzerland) · 2023Review
- Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer.Patterns (New York, N.Y.) · 2023Article
- Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model.Frontiers in neurorobotics · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The multi-modal and unstructured nature of observational data in Electronic Health Records (EHR) is currently a significant obstacle for the application of machine learning towards risk stratification. In this study, we develop a deep learning framework for incorporating longitudinal clinical data from EHR to infer risk for pancreatic cancer (PC). This framework includes a novel training protocol, which enforces an emphasis on early detection by applying an independent Poisson-random mask on proximal-time measurements for each variable. Data fusion for irregular multivariate time-series features is enabled by a "grouped" neural network (GrpNN) architecture, which uses representation learning to generate a dimensionally reduced vector for each measurement set before making a final prediction. These models were evaluated using EHR data from Columbia University Irving Medical Center-New York Presbyterian Hospital. Our framework demonstrated better performance on early detection (AUROC 0.671, CI 95% 0.667 - 0.675, p < 0.001) at 12 months prior to diagnosis compared to a logistic regression, xgboost, and a feedforward neural network baseline. We demonstrate that our masking strategy results greater improvements at distal times prior to diagnosis, and that our GrpNN model improves generalizability by reducing overfitting relative to the feedforward baseline. The results were consistent across reported race. Our proposed algorithm is potentially generalizable to other diseases including but not limited to cancer where early detection can improve survival.
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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.