progression

ODE

Neural ODE Neural Ordinary Differential Equations NeurIPS 2018 | YouTube

• consider NN not as discrete layers but as a continuous function, a whole big block
• not specifying depth of the network, let it learn by itself
• better than RNN because: 1. more efficient 2. better at irregular time series
• add ODE solver in the forward propagation

Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression NeurIPS 2021 | code

• combine (explainable but limited) expert knowledge with ML latent representative power

COX

LSTM

ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling

• 结合了之前分开有用过的attention和time-aware, added time interval to the attention

SSL-based

Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning 19年一个workshop

• dataset: 3308 scans from 221 patients
• pretext task: predict the time interval between two scan images of the same patient
• transfer task: intermediate state -> advanced stage ? by adding a classifier and fine-tune