1. Reducing Distressing Ambulance Transports of Older Adults with Predictive Modeling (Jorn Op den Buijs)

Germany – a super-aged “society” with a growing number of chronically ill elderly – is among the top spenders on healthcare. We use machine learning based on home health data to timely intervene with high risk patients and avoid distressing, costly emergency department visits. Initial results show significant reduction in ambulance dispatch rate. This talk will cover how to deploy prediction models with targeted prevention to facilitate independent living by older adults.

2. Leveraging Graphical Models to Assist Healthcare System (Vidhi Chugh)

The use of machine learning is needed to aid the healthcare system today more than ever. Pandemic has put a lot of pressure on the medical system and doctors are working round the clock to diagnose the symptoms and save the mankind. We are leveraging the power of the graphical networks to learn the joint probability distribution of the multivariate system and assist the end to end analytical solution including predictive and prescriptive analytics.

3. Implementing a Bayesian Adaptive Design for a Clinical Trial Sample Size. A Case Study From LivaNova (Giovanni Ranuzzi)

It is presented a use case of Bayesian adaptive design for a clinical trial to select the ‘optimum’ sample size based on accumulated data. During the study, frequent sample size selection analyses are produced using ‘R statistics’, and predictive probabilities are calculated with a simulation approach and used to drive decisions to stop/continue the trial. The methodology is known as Goldilocks, as it is constantly asking the question, “Is the sample size too big, too small, or just right?”