Harnessing LSTMs for Enhanced Prediction of Psychotic Episodes in Schizophrenia Spectrum

Paraskevi V. Tsakmaki, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos

Abstract: In the quest to enhance predictive models for schizophrenia spectrum disorders, Long Short-Term Memory networks (LSTM) have been pivotal due to their adept han-dling of temporal data sequences. This ability to process time-dependent data, enhances the monitoring of longitudinal patterns that are indicative of relapse. This study leverages LSTMs to analyze Heart Rate Variability (HRV), a key marker in neuropsychiatric evaluations, focusing on individual patient data. Emphasizing individualization, each LSTM model is carefully tai-lored to reflect the unique behavioral and physiological patterns of the patient. By customizing the analysis to accommodate the distinct fluctuations in HRV, LSTMs have shown a remarkable capacity to decode the complex, patient-specific patterns, as evidenced by a notable predictive average score of $0.972\pm 0.068$. This figure not only validates the LSTM model's effectiveness in grappling with the diverse aspects of psychosis but also highlights its exceptional ability to adapt to the unique temporal dynamics inherent in each patient's mental health progression.