A neural network approach to the outcome definition on first treatment with sertraline in a psychiatric population

Authors:
L. Franchini;C. Spagnolo;D. Rossini;E. Smeraldi;L. Bellodi;E. Politi
Affiliations:
Department of Neuropsychiatric Sciences, School of Medicine, Istituto Scientifico H. San Raffaele, University of Milan, via Stamira d'Ancona 20, 20127 Milan, Italy;Department of Neuropsychiatric Sciences, School of Medicine, Istituto Scientifico H. San Raffaele, University of Milan, via Stamira d'Ancona 20, 20127 Milan, Italy;Department of Neuropsychiatric Sciences, School of Medicine, Istituto Scientifico H. San Raffaele, University of Milan, via Stamira d'Ancona 20, 20127 Milan, Italy;Department of Neuropsychiatric Sciences, School of Medicine, Istituto Scientifico H. San Raffaele, University of Milan, via Stamira d'Ancona 20, 20127 Milan, Italy;Department of Neuropsychiatric Sciences, School of Medicine, Istituto Scientifico H. San Raffaele, University of Milan, via Stamira d'Ancona 20, 20127 Milan, Italy;Department of Neuropsychiatric Sciences, School of Medicine, Istituto Scientifico H. San Raffaele, University of Milan, via Stamira d'Ancona 20, 20127 Milan, Italy
Venue:
Artificial Intelligence in Medicine
Year:
2001

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Abstract

Therapy decision is one of the most important tasks clinicians have to perform in their clinical practice. The decision process requires taking into account many different factors. The Authors have proposed a neural computing approach for supporting clinical decision analysis. The mathematical model of artificial neural network (ANN) has been applied on a pool of clinical information gathered through case description freely filled by senior psychiatrists into 416 clinical charts. Sertraline, as drug for treatment, has been chosen since its clinical uses range from treatment of depression to that of many other psychiatric clinical conditions so that it has been thought to be a good candidate to this type of study. The ANN performance in forecasting successful and unsuccessful treatment cases showed an overall accuracy of classification of 97.35%. This result suggests a possible future application of this method to obtain a reliable prediction of a given psychiatric patient outcome during a specific psychopharmacological therapy, optimising the decisional making process.