ArticleScientific reports2019
An Early Intestinal Cancer Prediction Algorithm Based on Deep Belief Network.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Identification of Tumor- and Immunosuppression-Driven Glioblastoma Subtypes Characterized by Clinical Prognosis and Therapeutic Targets.Current issues in molecular biology · 2026Article
- Article
- AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions.Cancers · 2023Review
- Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- Deep Neural Network Models for Colon Cancer Screening.Cancers · 2022Review
- Identification and Validation of Aging-Related Genes in Idiopathic Pulmonary Fibrosis.Frontiers in genetics · 2022Article
- Identification of Feature Genes of a Novel Neural Network Model for Bladder Cancer.Frontiers in genetics · 2022Article
- Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study.JMIR medical informatics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The incidence of colorectal cancer (colorectal cancer, CRC) in China has increased in recent years, and its mortality rate has become one of the highest among all cancers. CRC also increasingly affects people's health and quality of life, and the workloads of medical doctors have further increased due to the lack of sufficient medical resources in China. The goal of this study was to construct an automated expert system using a deep learning technique to predict the probability of early stage CRC based on the patient's case report and the patient's attributes. Compared with previous prediction methods, which are either based on sophisticated examinations or have high computational complexity, this method is shown to provide valuable information such as suggesting potentially important early signs to assist in early diagnosis, early treatment and prevention of CRC, hence helping medical doctors reduce the workloads of endoscopies and other treatments.
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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.