Shibata Yuko, Victorino John Noel, Natsuyama Tomoya, Okamoto Naomichi, Yoshimura Reiji, Shibata Tomohiro
Department of Life Science and System Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan.
Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan.
Front Rehabil Sci. 2023 Mar 10;4:1121034. doi: 10.3389/fresc.2023.1121034. eCollection 2023.
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.
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.
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.
在日本，精神分裂症患者的住院时间最长。此外，高再住院率也会影响他们的生活质量（QoL）。尽管生活质量是治疗效果的有效预测指标，但由于时间限制和缺乏关注，它尚未得到广泛应用。因此，本研究旨在利用语音特征评估精神分裂症患者的主观生活质量。具体而言，本研究使用精神分裂症患者的语音来估计子量表得分，该得分用于衡量患者的主观生活质量。目标是：（1）从不同患者或横断面测量中估计子量表得分；（2）从同一患者在不同时期或纵向测量中估计子量表得分。
构建了一个对话代理，以记录18名精神分裂症患者对日本精神分裂症生活质量量表（JSQLS）的回答，该量表有三个子量表：“心理社会”、“动机与活力”和“症状与副作用”。这三个子量表用作客观变量。另一方面，测量期间的语音特征（色度图、梅尔频谱图、梅尔频率倒谱系数）用作解释变量。对于第一个目标，一个经过训练的模型使用嵌套交叉验证（CV）方法估计18名受试者的子量表得分。对于第二个目标，1次测量了18名受试者中的6名。然后，另一个经过训练的模型使用18名受试者的数据作为训练数据再次估计子量表得分。本研究使用了10种不同的机器学习算法，并比较了学习模型的误差。
结果表明，横断面测量的平均均方根误差（RMSE）为13.433，k近邻算法为最佳模型。同时，纵向测量的平均RMSE为13.301，随机森林算法为最佳。RMSE小于10表明，使用语音特征估计的子量表得分接近实际的JSQLS子量表得分。18名受试者中有10名在横断面测量中的RMSE估计小于10。同时，6名受试者中有5名在纵向测量中也有相同的情况。未来需要使用更多受试者进行研究，并基于纵向测量开发更个性化的模型，以便将结果应用于远程医疗，对生活质量进行持续监测。
