Latent Phrase Matching for Dysarthric Speech

Many consumer speech recognition systems are not tuned for people with speech disabilities, resulting in poor recognition and user experience, especially for severe speech differences. Recent studies have emphasized interest in personalized speech models from people with atypical speech patterns. We propose a query-by-example-based personalized phrase recognition system that is trained using small amounts of speech, is language agnostic, does not assume a traditional pronunciation lexicon, and generalizes well across speech difference severities. On an internal dataset collected from 32 people with dysarthria, this approach works regardless of severity and shows a 60% improvement in recall relative to a commercial speech recognition system. On the public EasyCall dataset of dysarthric speech, our approach improves accuracy by 30.5%. Performance degrades as the number of phrases increases, but consistently outperforms ASR systems when trained with 50 unique phrases

Comparison of the efficacy of thoracolumbosacral and lumbosacral orthosis for adolescent idiopathic scoliosis in patients with major thoracolumbar or lumbar curves: a prospective controlled study

IntroductionThoracolumbosacral orthosis (TLSO) is the most commonly used type of brace for the conservative treatment of adolescent idiopathic scoliosis (AIS). Although lumbosacral orthosis (LSO) is designed to correct single thoracolumbar or lumbar (TL/L) curves, its effectiveness remains underexplored. This novel article aims to compare the effectiveness of LSO with TLSO in treating AIS with main TL/L curves.MethodsThis prospective controlled cohort study enrolled patients with AIS with main TL/L curves and minor thoracic curves who were treated with either TLSO or LSO. Demographic and radiographic data were compared between the two groups. Treatment outcomes were also assessed. Risk factors for minor curve progression were identified, and a cut-off value was determined within the LSO group.ResultsOverall, 82 patients were recruited, including 44 in the TLSO group and 38 in the LSO group. The initial TL/L curves showed no difference between both groups. However, the baseline thoracic curves were significantly larger in the TLSO group compared to the LSO group (25.98° ± 7.47° vs. 18.71° ± 5.95°, P < 0.001). At the last follow-up, LSO demonstrated similar effectiveness to TLSO in treating TL/L curves but was less effective for thoracic curves. The initial magnitude of thoracic curves was identified as a risk factor for minor curve outcomes in the LSO group. The ROC curve analysis determined a cut-off value of 21° for thoracic curves to predict treatment outcomes.DiscussionIn contrast to TLSO, LSO exhibits comparable effectiveness in treating main TL/L curves, making it a viable clinical option; however, it is less effective for thoracic minor curves. The initial magnitude of the minor thoracic curves may guide the selection of the appropriate brace type for patients with AIS with main TL/L curves

Knowledge-Assisted Sequential Pattern Analysis: An Application in Labor Contraction Prediction

Although neuraxial techniques, such as spinal and epidural, are still considered as the gold standard for labor analgesia, there are some parturients who cannot receive neuraxial analgesia because of pre-existing conditions, or who request analgesia other than epidural block. An alternative analgesia is remifentanil, which is a relatively new, very potent and short-acting opioid. It has been shown to be effective in the relief of labor pain, but reports to date have failed to find the optimal dosing regimen. A challenge to a systemic opioid is that it must match the unique time course of labor pain. A continuous infusion is not ideal, as the parturient experiences no pain between contractions. Moreover, a continuous infusion during times in which the patient does not experience pain, may increase the risks of respiratory depression, sedation and nausea. The continuous infusion also increases the amount of the drug to which the fetus is exposed. Designing an optimal dosing regimen necessitates the prediction of the pace of contractions, so that the drug can be given shortly before the pain of the contraction begins. The prediction and thus drug administration should be made early enough to allow for the administration of intravenous analgesia that will have maximal efficacy during contractions, little effect between contractions, and minimal impact on the fetus. Towards such a need, we propose a knowledge-assisted sequential pattern analysis framework to predict the changes in intrauterine pressure, which indicate the occurrence of labor contractions. The proposed framework predicts in real time and provides a prediction multiple seconds before a contraction occurs, so as to assist in designing optimal administration strategies of remifentanil in labor. The proposed framework first selects a group of patients, from the stored record, who share similar demographic and obstetrical information with the current patient of interest. Second, it develops a sequential association rule mining approach to learn the patterns of the contractions from the historical patient tracings of the selected patients. Third, a sequential association rule-based collaborative filtering strategy is designed to dynamically select a training dataset from the historical patient tracings, as well as from the most recent training time series of the patient of interest. The training set is used for training a set of prediction models. A k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) approach with heuristic parameter tuning is proposed to conduct the long-term time series prediction. A post-prediction process is also incorporated to further enhance the prediction results. Because to the best of our knowledge, there has been no previous study to predict future contractions, this work can be considered as a pioneer in the field. We evaluate the performance of the proposed framework using actual data from anonymous patients with varied contraction patterns. The data include patient demographic and obstetrical information, and measured intrauterine pressure time series. Overall, the proposed framework outperforms several well-known prediction methods, and it accomplishes that in real time. Meanwhile, experiments that compare each component with some other famous algorithms are conducted. The promising experimental results show that all proposed components improve the prediction precision, and the proposed framework achieves the effectiveness, robustness and efficiency that are needed for designing the optimal dosing regimen of remifentanil

k-NN based LS-SVM framework for long-term time series prediction

Long-term time series prediction is to predict the future values multi-step ahead. It has received more and more attention due to its applications in predicting stock prices, traffic status, power consumption, etc. In this paper, a k-nearest neighbors (k-NN) based least squares support vector machine (LS-SVM) framework is proposed to perform long-term time series prediction. A new distance function, which integrates the Euclidean distance and the dissimilarity of the trend of a time series, is defined for the k-NN approach. By selecting similar instances (i.e., nearest neighbors) in the training dataset for each testing instance based on the k-NN approach, the complexity of training an LS-SVM regressor is reduced significantly. Experiments on two types of datasets were conducted to compare the prediction performance of the proposed framework with the traditional LS-SVM approach and the LL-MIMO (Multi-Input Multi-Output Local Learning) approach at the prediction horizon 20. The experimental results demonstrate that the proposed framework outperforms both traditional LS-SVM approach and LL-MIMO approach in prediction. Furthermore, experimental results also show the promising long-term prediction ability of the proposed framework even when the prediction horizon is large (up to 180)

Long-Term Time Series Prediction Using k-NN Based LS-SVM Framework with Multi-Value Integration

Time series modeling and prediction are very attractive topics, which play an important role in many fields such as transportation prediction [4], power prediction [13, 18], and health care study [7]. The purpose of time series prediction is to forecast the values of data points ahead of time, where long-term time series prediction is to make the predictions multi-step ahead. The prediction process is commonly performed by observing and modeling the past values, and assuming that the future values will follow the same trend. When the prediction horizon increases, the uncertainty of the future trend also increases, rendering a more challenging prediction problem. Researchers have dedicated their effort to study how to extract as much knowledge as possible from the past values, and how to better utilize such knowledge for long-term time series prediction. There has been previous research work in order to tackle this challenge based on some classical time series prediction approaches, such as exponential smoothing [12], linear regression [14], autoregressive integrated moving average (ARIMA) [33], support vector machines (SVM) [25], artificial neural networks (ANN) [10, 33], and fuzzy logic [10]

Hemorrhagic bronchitis caused by carbapenem-resistant Acinetobacter baumannii infection: A case report

Carbapenem-resistant Acinetobacter baumannii (CR-AB) is rarely found in community respiratory infections, and there are currently no reports of hemorrhagic bronchitis caused by its infection. This work presents a case of bronchial bleeding in a diabetic patient who acquired a community-acquired infection of CR-AB. Treatment with levofloxacin was unsuccessful, as the patient's hemoptysis symptoms recur. The patient was treated with minocycline based on the drug sensitivity test, resulting in the disappearance of hemoptysis symptoms. The patient was subjected to follow-up by phone for three months and did not experience any further hemoptysis symptoms. This case highlights that CR-AB infection causes hemorrhagic bronchitis, and the antimicrobial treatment should be based on drug sensitivity results

Stability of the Stochastic Reaction-Diffusion Neural Network with Time-Varying Delays and p-Laplacian

The main aim of this paper is to discuss moment exponential stability for a stochastic reaction-diffusion neural network with time-varying delays and p-Laplacian. Using the Itô formula, a delay differential inequality and the characteristics of the neural network, the algebraic conditions for the moment exponential stability of the nonconstant equilibrium solution are derived. An example is also given for illustration

Multi-model integration for long-term time series prediction

Long-term (multi-step-ahead) time series prediction is a much more challenging task comparing to the short-term (one-step-ahead) time series prediction. This is due to the increasing uncertainty and the lack of knowledge about the future trend. In this paper, we propose a multi-model integration strategy to 1) generate predicted values using multiple predictive models; and then 2) integrate the predicted values to generate a final predicted value as the output. In the first step, a k-nearest-neighbor (k-NN) based least squares support vector machine (LS-SVM) approach is used for long-term time series prediction. An autoregressive model is then employed in the second step to combine the predicted values from the multiple k-NN based LS-SVM models. The proposed multi-model integration strategy is evaluated using six datasets, and the experimental results demonstrate that the proposed strategy consistently outperforms some existing predictors

Knowledge-Assisted Sequential Pattern Analysis With Heuristic Parameter Tuning for Labor Contraction Prediction

The optimal dosing regimen of remifentanil for relieving labor pain should achieve maximal efficacy during contractions and little effect between contractions. Toward such a need, we propose a knowledge-assisted sequential pattern analysis with heuristic parameter tuning to predict the changes in intrauterine pressure, which indicates the occurrence of labor contractions. This enables giving the drug shortly before each contraction starts. A sequential association rule mining based patient selection strategy is designed to dynamically select data for training regression models. A novel heuristic parameter tuning method is proposed to decide the appropriate value ranges and searching strategies for both the regularization factor and the Gaussian kernel parameter of least-squares support vector machine with radial basis function (RBF) kernel, which is used as the regression model for time series prediction. The parameter tuning method utilizes information extracted from the training dataset, and it is adaptive to the characteristics of time series. The promising experimental results show that the proposed framework is able to achieve the lowest prediction errors as compared to some existing methods