  Background. Real-world data show that approximately 50% of psoriasis patients
treated with a biologic agent will discontinue the drug because of loss of
efficacy. History of previous therapy with another biologic, female sex and
obesity were identified as predictors of drug discontinuations, but their
individual predictive value is low. Objectives. To determine whether machine
learning algorithms can produce models that can accurately predict outcomes of
biologic therapy in psoriasis on individual patient level. Results. All tested
machine learning algorithms could accurately predict the risk of drug
discontinuation and its cause (e.g. lack of efficacy vs adverse event). The
learned generalized linear model achieved diagnostic accuracy of 82%, requiring
under 2 seconds per patient using the psoriasis patients dataset. Input
optimization analysis established a profile of a patient who has best chances
of long-term treatment success: biologic-naive patient under 49 years,
early-onset plaque psoriasis without psoriatic arthritis, weight < 100 kg, and
moderate-to-severe psoriasis activity (DLQI $\geq$ 16; PASI $\geq$ 10).
Moreover, a different generalized linear model is used to predict the length of
treatment for each patient with mean absolute error (MAE) of 4.5 months.
However Pearson Correlation Coefficient indicates 0.935 linear dependencies
between the actual treatment lengths and predicted ones. Conclusions. Machine
learning algorithms predict the risk of drug discontinuation and treatment
duration with accuracy exceeding 80%, based on a small set of predictive
variables. This approach can be used as a decision-making tool, communicating
expected outcomes to the patient, and development of evidence-based guidelines.
