Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to classify four common voice disorders (i.e. functional dysphonia, neoplasm, phonotrauma, and vocal palsy). Methods: In the proposed framework, acoustic signals are transformed into mel-frequency cepstral coefficients, and a bi-directional long-short term memory network (BiLSTM) is adopted to model the sequential features. The experiments were conducted on a large-scale database, wherein 1,045 continuous speech were collected by the speech clinic of a hospital from 2012 to 2019. Results: Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall improvements of 78.12–89.27% and 50.92–80.68%, respectively, compared with systems that use a single vowel. Conclusions: The results are consistent with other machine learning algorithms, including gated recurrent units, random forest, deep neural networks, and LSTM.The sensitivities for each disorder were also analyzed, and the model capabilities were visualized via principal component analysis. An alternative experiment based on a balanced dataset again confirms the advantages of using continuous speech for learning voice disorders.

Type 2 diabetes mellitus (T2DM) is a prevalent health challenge faced by countries worldwide. In this study, we propose a novel large language multimodal models (LLMMs) framework incorporating multimodal data from clinical notes and laboratory results for diabetes risk prediction. We collected five years of electronic health records (EHRs) dating from 2017 to 2021 from a Taiwan hospital database. This dataset included 1,420,596 clinical notes, 387,392 laboratory results, and more than 1505 laboratory test items. Our method combined a text embedding encoder and multi-head attention layer to learn laboratory values, and utilized a deep neural network (DNN) module to merge blood features with chronic disease semantics into a latent space. In our experiments, we observed that integrating clinical notes with predictions based on textual laboratory values significantly enhanced the predictive capability of the unimodal model in the early detection of T2DM. Moreover, we achieved an area greater than 0.70 under the receiver operating characteristic curve (AUC) for new-onset T2DM prediction, demonstrating the effectiveness of leveraging textual laboratory data for training and inference in LLMs and improving the accuracy of new-onset diabetes prediction.

Benign vocal fold lesions account for the majority of clinical voice disorders, for which the first-line treatment generally involves conservative management such as voice rest and voice therapy through various approaches (eg, hygienic, symptomatic, and physiologic).¹ In refractory cases, these treatments are followed by microsurgery.² With advances in modern endoscopic technology, office-based laryngeal procedures have become an effective treatment modality for various benign laryngeal lesions.³ These procedures have a good patient tolerability,⁴ high success rates,⁵ stable perioperative hemodynamic profiles,⁶ and are cost-effective.⁷