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PSI Conference 2018: Introduction to machine learning for longitudinal medical data

06 June 2018

In the era of big data, there has been a surge in collected biomedical data, which has provided ample challenges for distributed computing but also posed novel inference questions. Classical machine learning techniques, such as logistic regression, neural networks, support vector machine and Gaussian processes performed very well in non-temporal prediction tasks but typically relied on the independence assumption. However, many recent application have longitudinal context in the form of short- and long-term dependencies. Hidden Markov Models proved popular to model longitudinal data but increasingly become less computationally feasible for a large number of hidden states. Recently, advances in parallel computing led to widespread use of deep learning approaches, such as recurrent neural networks and convolutional networks, and attracted attention due to their impressive results on sequence data. Finally, we will look in more detail at a case study from healthcare analytics which infers disease type from multiple irregularly sampled longitudinal observations, such as blood pressure, heart rate and blood oxygen saturation.

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