  Continuous diagnosis and prognosis are essential for intensive care patients.
It can provide more opportunities for timely treatment and rational resource
allocation, especially for sepsis, a main cause of death in ICU, and COVID-19,
a new worldwide epidemic. Although deep learning methods have shown their great
superiority in many medical tasks, they tend to catastrophically forget, over
fit, and get results too late when performing diagnosis and prognosis in the
continuous mode. In this work, we summarized the three requirements of this
task, proposed a new concept, continuous classification of time series (CCTS),
and designed a novel model training method, restricted update strategy of
neural networks (RU). In the context of continuous prognosis, our method
outperformed all baselines and achieved the average accuracy of 90%, 97%, and
85% on sepsis prognosis, COVID-19 mortality prediction, and eight diseases
classification. Superiorly, our method can also endow deep learning with
interpretability, having the potential to explore disease mechanisms and
provide a new horizon for medical research. We have achieved disease staging
for sepsis and COVID-19, discovering four stages and three stages with their
typical biomarkers respectively. Further, our method is a data-agnostic and
model-agnostic plug-in, it can be used to continuously prognose other diseases
with staging and even implement CCTS in other fields.
