ArticlePatterns (New York, N.Y.)2023
Continuous diagnosis and prognosis by controlling the update process of deep neural networks.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
- A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data.Health data science · 2026Review
- DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information.Patterns (New York, N.Y.) · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Adaptive model training strategy for continuous classification of time series.Applied intelligence (Dordrecht, Netherlands) · 2023Article
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Authors and funding
6 authors.
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Abstract
Continuous diagnosis and prognosis are essential for critical patients. They can provide more opportunities for timely treatment and rational allocation. Although deep-learning techniques have demonstrated superiority in many medical tasks, they frequently forget, overfit, and produce results too late when performing continuous diagnosis and prognosis. In this work, we summarize the four requirements; propose a concept, continuous classification of time series (CCTS); and design a training method for deep learning, restricted update strategy (RU). The RU outperforms all baselines and achieves average accuracies of 90%, 97%, and 85% on continuous sepsis prognosis, COVID-19 mortality prediction, and eight disease classifications, respectively. The RU can also endow deep learning with interpretability, exploring disease mechanisms through staging and biomarker discovery. We find four sepsis stages, three COVID-19 stages, and their respective biomarkers. Further, our approach is data and model agnostic. It can be applied to other diseases and even in other fields.
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Registered trials
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