Continuous measurement of blood pressure is crucial to the assessment of many medical conditions. However, the current clinical gold standard involving an arterial catheter, occluding cuff, and other invasive procedures are performed in hospital settings while home-based devices can provide only intermittent measurement and are not as reliable. Therefore, there is a significant need for continuous noninvasive blood pressure (cNIBP) monitoring in the daily life. Pulse transit time (PTT)/pulse arrival time (PAT)-based blood pressure measurement has proven its potential to address this need. In this article, we present state-of-the-art devices and recent literature related to measurement technologies used in PTT/PAT-based methods for cNIBP monitoring. Various physiological signals which could be used to enable cNIBP in the home setting are categorized into two groups (i.e., proximal waveforms and distal waveforms) and are thoroughly discussed and compared. Given insightful analysis of these waveforms, we highlight their combinations to derive PTT/PAT values for BP measurement then discuss challenges presented from the cuffless and PTT/PAT-based nature of these devices. Finally, we conclude with future directions needed for home-based cNIBP adaptation and present societal broader impacts.
The emerging technique of deep learning has been widely applied in many different areas. However, when adopted in a certain specific domain, this technique should be combined with domain knowledge to improve efficiency and accuracy. In particular, when analyzing the applications of deep learning in sentiment analysis, we found that the current approaches are suffering from the following drawbacks: (i) the existing works have not paid much attention to the importance of different types of sentiment terms, which is an important concept in this area; and (ii) the loss function currently employed does not well reflect the degree of error of sentiment misclassification. To overcome such problem, we propose to combine domain knowledge with deep learning. Our proposal includes using sentiment scores, learnt by quadratic programming, to augment training data; and introducing penalty matrix for enhancing the loss function of cross entropy. When experimented, we achieved a significant improvement in classification results.
Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.
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