Predicting the usage of healthcare services using neural networks and nationwide healthcare register data
From Ekaterina Churkina
Speaker: Pekka Marttinen, Aalto University.
Abstract: Accurately predicting the need for healthcare services is important, to allocate the limited resources fairly and efficiently. In this presentation I will introduce the problem of predicting future diagnoses and hospital visits using individual level trajectories of diagnoses and medical procedure codes available in electronic medical records. I will then present our recent work on developing neural networks for nationwide healthcare registers for this problem, to predict the usage of healthcare services by the elderly population in Finland. We show that by leveraging individual patient trajectories and modern neural network architectures, the prediction accuracy can be significantly improved compared to multiple strong baselines.
About the speaker: Pekka Marttinen is an associate professor
in machine learning in the department of computer science at Aalto university,
Finland. He received his PhD in Statistics at the University of Helsinki in
2008, and has been employed at Aalto since 2009, interleaved by periods as a
visiting researcher at the Center for Communicable Disease Dynamics at Harvard
and at the Sanger institute in Cambridge. He has received a Research Fellowship
from the Academy of Finland. His research focuses on method development and
modeling in machine learning, with emphasis on biomedical applications, and he
has published 65 articles on these topics. He is best known for his work on
scalable computational methods for detecting structure in massive genomic data
sets and as well as for the development of Bayesian methodology for efficient
and flexible modeling of complex and noisy data, including techniques such as
likelihood-free inference and Bayesian neural networks.