progression
Cox Survival model Youtube
- combination of Kaplan miler and exponential model
- assume that hazard changes over time, but hazard ratio is proportional
- can only estimate hazard ratio of factors, cannot predict survival itself
Optimizing Survival Analysis of XGBoost for Ties to Predict Disease Progression of Breast Cancer
- method: EXSA XGBoost + Cox, based on selected features
- data: 12119 breast cancer patients
- task: risk score, grouping
T-LSTM
[Recurrent neural networks for multivariate time series with missing values]. Scientific reports 2018.
- Imputation-based: GRU-Simple and GRU-D
ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling IJCAI-19
- 结合了之前分开有用过的attention和time-aware, added time interval to the attention
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
ODE2VAE [ODE2VAE: Deep generative second order ODEs with Bayesian neural networks]
GRU-ODE [GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series]
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression NeurIPS 2021 | code
- combine (explainable but limited) expert knowledge with ML latent representative power
Longitudinal self-supervised learning
- dataset: neuroimaging of Alzheimer (2641 of 811 patients) and alcohol dependence (1499 of 274+329)
- Factor disentanglement: one dimension can be explained by a specific factor (brain age), invariant to other factors (e.g. gender, ethnicity)`
* added a cosine loss while training auto-encoder
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
Self-Supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets Conference on Health, Inference, and Learning (CHIL) 2023 | code data on request
- dataset: Homekit Flu Monitoring Study: 591k user-days of Fitbit data, 5196 participants, 6 months
- pretext task: same user, reconstruct data, predict Domain Inspired Features (best!)
- transfer task: infer positive / symptoms