13 December, 2016
Big Data In Healthcare: Paris Hospitals Predict Admission Rates Using Machine Learning
Hospitals in Paris are trialling Big Data and machine learning systems designed to forecast admission rates – leading to more efficient deployment of resources and better patient outcomes.
It’s just one more way in which cutting-edge data science is being applied to real-world problems in healthcare, along with creating personalized medicines, fighting cancer and streamlining pharmaceutical trials.
At four of the hospitals which make up the Assistance Publique-Hôpitaux de Paris (AP-HP), data from internal and external sources – including 10 years’ worth of hospital admissions records has been crunched to come up with day and hour-level predictions of the number of patients expected through the doors.
The core of the analytics work involves using time series analysis techniques – looking for ways in which patterns in the data can be used to predict the admission rates at different times. Machine learning is employed to determine which algorithms provide the best indicator of future trends, when they are fed data from the past.
The system is built on the open source Trusted Analytics Platform (TAP) – which was chosen for the task due to its capacity for ingesting and crunching large amounts of data, as well as its gearing towards open, collaborative development environments.
Kyle Ambert, a data scientist with Intel who contributes to the TAP project and worked with AP-HP on their implementation, told me “What was interesting with this work is that although there are many analytical solutions for these types of problems, none of them have been implemented in a distributed fashion.