55,515 research outputs found
Interactive exploration of population scale pharmacoepidemiology datasets
Population-scale drug prescription data linked with adverse drug reaction
(ADR) data supports the fitting of models large enough to detect drug use and
ADR patterns that are not detectable using traditional methods on smaller
datasets. However, detecting ADR patterns in large datasets requires tools for
scalable data processing, machine learning for data analysis, and interactive
visualization. To our knowledge no existing pharmacoepidemiology tool supports
all three requirements. We have therefore created a tool for interactive
exploration of patterns in prescription datasets with millions of samples. We
use Spark to preprocess the data for machine learning and for analyses using
SQL queries. We have implemented models in Keras and the scikit-learn
framework. The model results are visualized and interpreted using live Python
coding in Jupyter. We apply our tool to explore a 384 million prescription data
set from the Norwegian Prescription Database combined with a 62 million
prescriptions for elders that were hospitalized. We preprocess the data in two
minutes, train models in seconds, and plot the results in milliseconds. Our
results show the power of combining computational power, short computation
times, and ease of use for analysis of population scale pharmacoepidemiology
datasets. The code is open source and available at:
https://github.com/uit-hdl/norpd_prescription_analyse
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse
outcomes in different clinical settings. These models are often developed and
evaluated on datasets with heterogeneous patient populations. However, good
predictive performance on the aggregate population does not imply good
performance for specific groups.
In this work, we present a two-step framework to 1) learn relevant patient
subgroups, and 2) predict an outcome for separate patient populations in a
multi-task framework, where each population is a separate task. We demonstrate
how to discover relevant groups in an unsupervised way with a
sequence-to-sequence autoencoder. We show that using these groups in a
multi-task framework leads to better predictive performance of in-hospital
mortality both across groups and overall. We also highlight the need for more
granular evaluation of performance when dealing with heterogeneous populations.Comment: KDD 201
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