ArticleBMC bioinformatics2022
An interpretable DIC risk prediction model based on convolutional neural networks with time series data.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Cross-algorithm machine learning and consensus features for early disseminated intravascular coagulation risk prediction in sepsis-induced coagulopathy.Journal of translational medicine · 2026Article
- Development and Validation of the Clinical Risk Prediction Model for Disseminated Intravascular Coagulation after Severe Burn Injury.Annals of burns and fire disasters · 2025Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Comparative Analysis of Feature Extraction Methods and Machine Learning Models for Predicting Osteoporosis Prevalence.Journal of medical systems · 2025Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Disseminated intravascular coagulation (DIC) is a complex, life-threatening syndrome associated with the end-stage of different coagulation disorders. Early prediction of the risk of DIC development is an urgent clinical need to reduce adverse outcomes. However, effective approaches and models to identify early DIC are still lacking. In this study, a novel interpretable deep learning based time series is used to predict the risk of DIC. The study cohort included ICU patients from a 4300-bed academic hospital between January 1, 2019, and January 1, 2022. Experimental results show that our model achieves excellent performance (AUC: 0.986, Accuracy: 95.7%, and F1:0.935). Gradient-weighted Class Activation Mapping (Grad-CAM) was used to explain how predictive models identified patients with DIC. The decision basis of the model was displayed in the form of a heat map. The model can be used to identify high-risk patients with DIC early, which will help in the early intervention of DIC patients and improve the treatment effect.
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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.