Machine Learning & High Dimensional Data – Clinical Outcome

Author:Benjamin W Y Lo, MD PHD FRCSC Department of Neurological Surgery Lenox Hill Hospital

Advances in biostatistics and computing have led to the creation of novel types of machine learning algorithms for clinical outcome prediction models. Three clinically and statistically robust models include artificial neural networks, fuzzy logic and bayesian analysis. These techniques complement classical approaches of regression analysis and decision tree analysis. This editorial explains the novel outcome prediction system using Bayesian neural networks with fuzzy logic. This novel approach can be practically applied to both clinical and non-clinical settings.Introduction to Bayesian Analysis, Artificial Neural Networks and Fuzzy LogicBayesian Analysis

Bayesian analysis enables incorporation of new data with existing knowledge. Based on this knowledge, the researcher expresses the degree of belief about a certain parameter in the form of a prior probability distribution. The normal bell-shaped distribution is an example of a probability distribution. This prior probability distribution is then combined with its likelihood of occurrence, forming a posterior probability distribution (posterior probability = prior probability X likelihood).

The end result of Bayesian analysis is the formation of a posterior probability distribution (Figure 1). It represents a revised or updated belief after taking new data into account. If there is lack of existing knowledge on the subject of interest, the researcher can still use Bayesian techniques. Here, the researcher is encouraged to use vague or uninformed prior probabilities.

Artificial Neural Networks

Artificial neural networks mimic biological neural systems. In biological systems, incoming dendrites collect signals which feed to the neuron (Figure 2). An electrical signal propagates along the axon with neurotransmitter discharge at the synapse.Examples of biologicalneural networks include the human brain and retina.

In artificial neural networks,input variablesconverge on a number of nodes. Nodes are grouped into layers. Layers are linked to each other via interconnection links. Between input and output layers, there can exist one or two hidden layers (Figure 3). Latent variables make up the hidden layer(s). In order to advance from one layer to the next, signals are processed via activation functions.

Artificial neural networks assume all or none logic. In the case of clinical outcome prediction, subjects are classified as having good or bad prognosis. Within each layer, nodes in the artificial neural network are connected with each other via connection links. Activation functions and associated weights are applied to these connection links.Artificial neural networks are intelligent systems that can learn and change behaviour by themselves as they gain experience. They also take into account latent variables or unobserved variables. These variables are not directly measured or accounted for during the design of the artificial neural network.

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Samir Redzepagic, MD, Public Health Officer, Monash Public Health