Machine Learning

This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. In the ‘5D_Data_Extractor.py’ file this can be set up to 6-Dimension, by the ‘fields_included’ variable. Two folders are included, one ready for preprocessing (‘subjects raw’) and the other already preprocessed ‘subjects preprocessed’.

We develop a general group-based continuous-time Markov epidemic model (GgroupEM) framework for any compartmental epidemic model (e.g., susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes of a network. This model can be used to understand the critical dynamic characteristics of a stochastic epidemic spreading over large complex networks while being informative about the state of groups.

Machine learning offers a wide range of techniques to predict medicine expenditures using historical expenditures data as well as other healthcare variables. For example, researchers have developed multi-layer perceptron (MLP), long-short term memory (LSTM), and convolutional neural networks (CNN) for predicting healthcare outcomes. However, recently proposed generative approaches (e.g., generative adversarial networks; GANs) are yet to be explored for time-series prediction of medicine-related expenditures.

Evidence-Based Medicine (EBM) aims to apply the best available evidence gained from scientific methods to clinical decision making. A generally accepted criterion to formulate evidence is to use the PICO framework, where PICO stands for Problem/Population, Intervention, Comparison, and Outcome. Automatic extraction of PICO-related sentences from medical literature is crucial to the success of many EBM applications. In this work, we present our Aceso system, which automatically generates PICO-based evidence summaries from medical literature.