Estimation of subjective quality of life in schizophrenic patients using speech features

Yuko Shibata; John Noel Victorino; Tomoya Natsuyama; Naomichi Okamoto; Reiji Yoshimura; Tomohiro Shibata

doi:10.3389/fresc.2023.1121034

Estimation of subjective quality of life in schizophrenic patients using speech features

Front Rehabil Sci. 2023 Mar 10:4:1121034. doi: 10.3389/fresc.2023.1121034. eCollection 2023.

Authors

Yuko Shibata¹, John Noel Victorino¹, Tomoya Natsuyama², Naomichi Okamoto², Reiji Yoshimura², Tomohiro Shibata¹

Affiliations

¹ Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan.
² Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan.

Abstract

Introduction: Patients with schizophrenia experience the most prolonged hospital stay in Japan. Also, the high re-hospitalization rate affects their quality of life (QoL). Despite being an effective predictor of treatment, QoL has not been widely utilized due to time constraints and lack of interest. As such, this study aimed to estimate the schizophrenic patients' subjective quality of life using speech features. Specifically, this study uses speech from patients with schizophrenia to estimate the subscale scores, which measure the subjective QoL of the patients. The objectives were to (1) estimate the subscale scores from different patients or cross-sectional measurements, and 2) estimate the subscale scores from the same patient in different periods or longitudinal measurements.

Methods: A conversational agent was built to record the responses of 18 schizophrenic patients on the Japanese Schizophrenia Quality of Life Scale (JSQLS) with three subscales: "Psychosocial," "Motivation and Energy," and "Symptoms and Side-effects." These three subscales were used as objective variables. On the other hand, the speech features during measurement (Chromagram, Mel spectrogram, Mel-Frequency Cepstrum Coefficient) were used as explanatory variables. For the first objective, a trained model estimated the subscale scores for the 18 subjects using the Nested Cross-validation (CV) method. For the second objective, six of the 18 subjects were measured twice. Then, another trained model estimated the subscale scores for the second time using the 18 subjects' data as training data. Ten different machine learning algorithms were used in this study, and the errors of the learned models were compared.

Results and discussion: The results showed that the mean RMSE of the cross-sectional measurement was 13.433, with k-Nearest Neighbors as the best model. Meanwhile, the mean RMSE of the longitudinal measurement was 13.301, using Random Forest as the best. RMSE of less than 10 suggests that the estimated subscale scores using speech features were close to the actual JSQLS subscale scores. Ten out of 18 subjects were estimated with an RMSE of less than 10 for cross-sectional measurement. Meanwhile, five out of six had the same observation for longitudinal measurement. Future studies using a larger number of subjects and the development of more personalized models based on longitudinal measurements are needed to apply the results to telemedicine for continuous monitoring of QoL.

Keywords: machine learning; model development; quality of life; schizophrenia; speech analysis.