ArticleBioinformatics (Oxford, England)2021
Cox-nnet v2.0: improved neural-network-based survival prediction extended to large-scale EMR data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.bioRxiv : the preprint server for biology · 2026Article
- Breast cancer pathogenesis, diagnosis and treatment: a comprehensive review.Frontiers in oncology · 2026Review
- A Machine Learning Framework for Cancer Prognostics: Integrating Temporal and Immune Gene Dynamics via ARIMA-CNN.Biomedicines · 2025Article
- Predicting interval from diagnosis to delivery in preeclampsia using electronic health records.Nature communications · 2025Article
- Characterization of Non-Monotonic Relationships between Tumor Mutational Burden and Clinical Outcomes.Cancer research communications · 2024Article
- Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data.Communications medicine · 2023Article
- Deep Learning in Prediction of Late Major Bleeding After Transcatheter Aortic Valve Replacement.Clinical epidemiology · 2022Article
- Clinical time-to-event prediction enhanced by incorporating compatible related outcomes.PLOS digital health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
summaryCox-nnet is a neural-network-based prognosis prediction method, originally applied to genomics data. Here, we propose the version 2 of Cox-nnet, with significant improvement on efficiency and interpretability, making it suitable to predict prognosis based on large-scale population data, including those electronic medical records (EMR) datasets. We also add permutation-based feature importance scores and the direction of feature coefficients. When applied on a kidney transplantation dataset, Cox-nnet v2.0 reduces the training time of Cox-nnet up to 32-folds (n =10 000) and achieves better prediction accuracy than Cox-PH (P<0.05). It also achieves similarly superior performance on a publicly available SUPPORT data (n=8000). The high efficiency and accuracy make Cox-nnet v2.0 a desirable method for survival prediction in large-scale EMR data. AVAILABILITY AND IMPLEMENTATION: Cox-nnet v2.0 is freely available to the public at https://github.com/lanagarmire/Cox-nnet-v2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.