#DeepLearning predicts when patients die with Average Precision 0.69 (thatβs high).
Andrew Ng announced new project in his twitter: ML to help prioritize palliative (end-of-life) care. Model uses an 18-layer Deep Neural Network that inputs the EHR data of a patient, and outputs the probability of death in the next 3-12 months.
The trained model achieves an AUROC score of 0.93 and an Average Precision score of 0.69 on cross validation.
Site: https://stanfordmlgroup.github.io/projects/improving-palliative-care/
Arxiv: https://arxiv.org/abs/1711.06402
#project #DSinthewild #casestudy
Andrew Ng announced new project in his twitter: ML to help prioritize palliative (end-of-life) care. Model uses an 18-layer Deep Neural Network that inputs the EHR data of a patient, and outputs the probability of death in the next 3-12 months.
The trained model achieves an AUROC score of 0.93 and an Average Precision score of 0.69 on cross validation.
Site: https://stanfordmlgroup.github.io/projects/improving-palliative-care/
Arxiv: https://arxiv.org/abs/1711.06402
#project #DSinthewild #casestudy
stanfordmlgroup.github.io
Improving Palliative Care with Deep Learning
Improving Palliative Care with Deep Learning.