AI Use Case

Predict patient mortality to provide appropriate support

Predict likely outcome risk for patient to support healthcare providers and affected individuals (and family) with planning, correct treatment pathways and recommended responses, including palliative care support.



Risk reduction - Mortality rate,Risk reduction - Patient outcomes

Case Studies

Google~Google Health improves predictions of hospital patient medical outcomes by using deep neural networks trained on 46 billion data points,Stanford University~Stanford University Medical uses machine learning to improve palliative care by predicting end-of-life within the next year with 90% accuracy,KenSci~KenSci improves on prediction models of mortality risk six months to one year out through deep machine learning leading to better palliative care,Stanford University~Stanford University researchers plan to improve palliative care by predicting mortality of patient with deep learning,The Francis Crick Institute~Scientists at the Francis Crick Institute outperform medical models at predicting risk of death in patients with heart disease using machine learning,PLA General Hospital~PLA General Hospital in Beijing is using an algorithm for predicting if patients will wake up from a coma,Florida State University~Researchers at Florida State University predict one-year mortality rate in ICU patients suffering from a heart disease,Imperial College London~Imperial College Researchers diagnose ovarian cancer patients with survival rates under 2 years



Healthcare Providers And Services

Data Sets

Structured / Semi-structured

AI Technologies

Model Architecture - RNN - Long Short-Term Memory (LSTM),Model Architecture - Deep Neural Networks,Machine Learning (ML),ML Task - Prediction - Regression,Model Architecture - Recurrent Neural Network (RNN)

Potential Vendors