Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier

Aim Bipolar disorder (BD) is a mood disorder with a high morbidity and death rate. Lithium (Li), a prominent mood stabilizer, is often used as a first‐line treatment. However, clinical studies have shown that Li is fully effective in roughly 30% of BD patients. Our goal in this study was to use feat...

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Bibliographic Details
Published in:Bipolar disorders 2023-03, Vol.25 (2), p.110-127
Main Authors: Tripathi, Utkarsh, Mizrahi, Liron, Alda, Martin, Falkovich, Gregory, Stern, Shani
Format: Article
Language:English
Subjects:
Bipolar disorder
Bipolar Disorder - diagnosis
bipolar disorders
Dentate gyrus
entropy
Granule cells
Humans
Information Theory
iPSC
Lithium
Lithium - therapeutic use
Lithium Compounds - therapeutic use
lithium responders and non‐responders
lithium response prediction
machine learning
Mood
Morbidity
mutual information
Patients
Support Vector Machine
Support vector machines
SVM classifier
valproic acid
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Summary:Aim Bipolar disorder (BD) is a mood disorder with a high morbidity and death rate. Lithium (Li), a prominent mood stabilizer, is often used as a first‐line treatment. However, clinical studies have shown that Li is fully effective in roughly 30% of BD patients. Our goal in this study was to use features derived from information theory to improve the prediction of the patient's response to Li as well as develop a diagnostic algorithm for the disorder. Methods We have performed electrophysiological recordings in patient‐derived dentate gyrus (DG) granule neurons (from a total of 9 subjects) for three groups: 3 control individuals, 3 BD patients who respond to Li treatment (LR), and 3 BD patients who do not respond to Li treatment (NR). The recordings were analyzed by the statistical tools of modern information theory. We used a Support Vector Machine (SVM) and Random forest (RF) classifiers with the basic electrophysiological features with additional information theory features. Results Information theory features provided further knowledge about the distribution of the electrophysiological entities and the interactions between the different features, which improved classification schemes. These newly added features significantly improved our ability to distinguish the BD patients from the control individuals (an improvement from 60% to 74% accuracy) and LR from NR patients (an improvement from 81% to 99% accuracy). Conclusion The addition of Information theory‐derived features provides further knowledge about the distribution of the parameters and their interactions, thus significantly improving the ability to discriminate and predict the LRs from the NRs and the patients from the controls.
ISSN:1398-5647
1399-5618
DOI:10.1111/bdi.13282