Year: 2018
Type: article
Abstract: Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that causes the immune system to attack the body’s own connective tissues and organs. Humans have difficulty predicting SLE symptom severity levels because of the complex interactions of disease trigger exposure levels over time. To address this issue, we constructed a novel machine learning solution that generates a model capable of predicting SLE symptom severity levels with 8.3-19.9% average error. It does so by inputting trigger exposure levels into a recursive neural network and training them with a unique method that continually turns training on and off based on the maximum error each day. This allows the RNN to learn SLE flare activity without overtraining remission activity, thus maintaining a greater degree of plasticity. Models trained in this fashion performed 3.5-5% better on average than those trained via the standard method. Future areas of work include replicating these results with a large patient training data set and evolving the model to predict disease trajectory.
Author: Katherine G. Skocelas
Language: English
Subfield: Rheumatology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2924505552