LANGaware: biomarker workflows for classifying neurodegenerative and affective disorders across languages and severity levels

Abstract

AbstractBackgroundA wide range of neurodegenerative disorders impair communication faculties, manifested as subtle changes in speech and language. Following trends in Machine Learning (ML), Text and Multimedia Analysis, recent works pursue classification of such disorders by developing biomarkers useful for cognitive status description. However, investigations that gauge biomarker applicability on cases of radically different impairment / symptom severity have been under‐investigated. In this work, we build upon previous research and evaluate LANGaware’s biomarker suite in the drastically different tasks of Dementia and Depression classification. For both cases, we use appropriate Healthy Controls and evaluate the proposed biomarker workflow under a multi‐language experimental investigation.MethodWe utilize audio recordings and transcripts from patient responses to verbal cognitive assessment tasks. These are analyzed with LANGaware’s multimodal biomarker pool, mapping raw data to biomarkers scores that quantify vocal, linguistic and grammatical usage proficiency, structure and patterns, by applying both statistical analysis and explainable template matching. Biomarkers activations are fed to ML workflows composed of gradient boosting learners that employ feature selection, filtering and ensemble‐based learning to arrive at configurations best suited for discriminating the disorder of interest. The pipeline is evaluated using a standard train‐test and cross‐validation setup.ResultExperimental results indicate that our method achieves weighted F1 test scores of 82.49% for Greek Dementia classification, using a train / test dataset of 1271 / 624 instances and 80.28% on 570 / 243 English data. We use the same pipeline and biomarker pool for Depression classification, reaching 74.03% and 71.36% performance scores, obtained from 73 / 31 and 652 / 42 available train‐test data, for Greek and English respectively.ConclusionThe above findings show that the proposed biomarker pool generalizes across neurodegenerative and affective disorders of radically different severity. As a result, they constitute valuable decision support tools for accurate, automatic and explainable early diagnosis, facilitating proactive care, improved symptom management and better patient quality of life. Future research efforts include extending our experimental evaluation of LANGaware biomarkers to additional disorders and diseases, as well as expanding the biomarker set to enable analysis of additional modalities.