scholarly journals Ischemic Stroke Prediction by Exploring Sleep Related Features

2021 ◽  
Vol 11 (5) ◽  
pp. 2083
Author(s):  
Jia Xie ◽  
Zhu Wang ◽  
Zhiwen Yu ◽  
Bin Guo ◽  
Xingshe Zhou
Keyword(s):  
Ischemic Stroke ◽  
Life Quality ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Neural System ◽  
Human Beings ◽  
Filter Model ◽  
Fine Grained ◽  
Proposed Model ◽  
Precision Recall Curve

Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.

scholarly journals Constructed model for micro-content recognition in lip reading based deep learning

2021 ◽  
Vol 10 (5) ◽  
pp. 2557-2565
Author(s):  
Nada Hussain Ali ◽  
Matheel Emad Abdulmunem ◽  
Akbas Ezaldeen Ali
Keyword(s):  
Deep Learning ◽  
Recognition Rate ◽  
Human Beings ◽  
Fine Grained ◽  
Content Recognition ◽  
Proposed Model ◽  
Lip Reading ◽  
English Alphabet ◽  
Reading System ◽  
Age Range

Communication between human beings has several ways, one of the most known and used is speech, both visual and acoustic perceptions sensory are involved, because of that, the speech is considered as a multi-sensory process. Micro contents are a small pieces of information that can be used to boost the learning process. Deep learning is an approach that dives into deep texture layers to learn fine grained details. The convolution neural network (CNN) is a deep learning technique that can be employed as a complementary model with micro learning to hold micro contents to achieve special process. In This paper a proposed model for lip reading system is presented with proposed video dataset. The proposed model receives micro contents (the English alphabet) in video as input and recognize them, the role of CNN deep learning is clearly appeared to perform two tasks, the first one is feature extraction and the second one is the recognition process. The implementation results show an efficient accuracy recognition rate for various video dataset that contains variety lip reader for many persons with age range from 11 to 63 years old, the proposed model gives high recognition rate reach to 98%.

Evaluation of the clot burden score (cbs) for acute ischemic stroke (ais) in intent-to-treat patients.

2019 ◽  
Vol 1 (1) ◽  
pp. 11-15 ◽  
Author(s):  
Sarah Yaziz ◽  
Ahmad Sobri Muda ◽  
Wan Asyraf Wan Zaidi ◽  
Nik Azuan Nik Ismail
Keyword(s):  
Ischemic Stroke ◽  
Clinical Outcome ◽  
Acute Ischemic Stroke ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Rank Correlation ◽  
Treatment Decision ◽  
Roc Curves ◽  
Significant Negative Correlation ◽  
Clot Burden

Background : The clot burden score (CBS) is a scoring system used in acute ischemic stroke (AIS) to predict patient outcome and guide treatment decision. However, CBS is not routinely practiced in many institutions. This study aimed to investigate the feasibility of CBS as a relevant predictor of good clinical outcome in AIS cases. Methods:  A retrospective data collection and review of AIS patients in a teaching hospital was done from June 2010 until June 2015. Patients were selected following the inclusion and exclusion criteria. These patients were followed up after 90 days of discharge. The Modified Rankin scale (mRS) was used to assess their outcome (functional status). Linear regression Spearman Rank correlation was performed between the CBS and mRS. The quality performance of the correlations was evaluated using Receiver operating characteristic (ROC) curves. Results: A total of 89 patients with AIS were analysed, 67.4% (n=60) male and 32.6% (n=29) female. Twenty-nine (29) patients (33.7%) had a CBS ?6, 6 patients (6.7%) had CBS <6, while 53 patients (59.6%) were deemed clot free. Ninety (90) days post insult, clinical assessment showed that 57 (67.6%) patients were functionally independent, 27 (30.3%) patients functionally dependent, and 5 (5.6%) patients were deceased. Data analysis reported a significant negative correlation (r= -0.611, p<0.001). ROC curves analysis showed an area under the curve of 0.81 at the cut-off point of 6.5. This showed that a CBS of more than 6 predicted a good mRS clinical outcome in AIS patients; with sensitivity of 98.2%, specificity of 53.1%, positive predictive value (PPV) of 76%, and negative predictive value (NPV) of 21%. Conclusion: CBS is a useful additional variable for the management of AIS cases, and should be incorporated into the routine radiological reporting for acute ischemic stroke (AIS) cases.

Bioluminescent proteins prediction with voting strategy.

2020 ◽  
Vol 15 ◽  
Author(s):  
Shulin Zhao ◽  
Ying Ju ◽  
Xiucai Ye ◽  
Jun Zhang ◽  
Shuguang Han
Keyword(s):  
Amino Acid ◽  
Model Building ◽  
Prediction Method ◽  
Pair Composition ◽  
Proposed Model ◽  
Counting Rules ◽  
Significant Phenomenon ◽  
Voting Strategy ◽  
Improved Accuracy ◽  
Selection Of

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology.In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this paper, we propose a new bioluminescent proteins prediction method based on a voting algorithm. We used four methods of feature extraction based on the amino acid sequence. We extracted 314 dimensional features in total from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, then used a voting algorithm to build the model.To create the best performing model, we discuss the selection of base classifiers and vote counting rules. Results: Our proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. We also improved a previous prediction of bioluminescent proteins in three lineages using our model building method, resulting in greatly improved accuracy.

scholarly journals IIMLP: integrated information-entropy-based method for LncRNA prediction

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Junyi Li ◽  
Huinian Li ◽  
Xiao Ye ◽  
Li Zhang ◽  
Qingzhe Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Information Entropy ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Reading Frame ◽  
Non Coding Rna ◽  
The One ◽  
Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

scholarly journals Stress hyperglycemia as a prognostic factor in acute ischemic stroke patients: a prospective observational cohort study

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Hanaa A. El-Gendy ◽  
Mahmoud A. Mohamed ◽  
Amr E. Abd-Elhamid ◽  
Mohammed A. Nosseir
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Prognostic Value ◽  
Area Under The Curve ◽  
Hemorrhagic Transformation ◽  
Diabetic Patients ◽  
Stroke Patients ◽  
Stress Hyperglycemia ◽  
Discrimination Power ◽  
Observational Cohort

Abstract Background Hyperglycemia is a risk factor for infarct expansion and poor outcome for both diabetic and non-diabetic patients. We aimed to study the prognostic value of stress hyperglycemia on the outcome of acute ischemic stroke patients as regards National Institutes of Health Stroke Scale (NIHSS) as a primary outcome. Results Patients with high random blood sugar (RBS) on admission showed significantly higher values of both median NIHSS score and median duration of hospital stay. There were significant associations between stress hyperglycemia and the risk of 30-day mortality (p < 0.001), the need for mechanical ventilation (p < 0.001) and vasopressors (p < 0.001), and the occurrence of hemorrhagic transformation (p = 0.001). The 24-h RBS levels at a cut off > 145 mg/dl showed a significantly good discrimination power for 30-day mortality (area under the curve = 0.809). Conclusions Stress hyperglycemia had a prognostic value and was associated with less-favorable outcomes of acute stroke patients. Therefore, early glycemic control is recommended for those patients.

Semi-Supervised Aspect-Based Sentiment Analysis for Case-Related Microblog Reviews Using Case Knowledge Graph Embedding

2021 ◽  
pp. 2050012
Author(s):  
Peilian Zhao ◽  
Cunli Mao ◽  
Zhengtao Yu
Keyword(s):  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Opinion Mining ◽  
Data Augmentation ◽  
Training Data ◽  
Knowledge Graph ◽  
Fine Grained ◽  
Learning Framework ◽  
Proposed Model ◽  
Real World Applications

Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly.

scholarly journals The interactional achievement of speaker meaning: Toward a formal account of conversational inference

2018 ◽  
Vol 15 (5) ◽  
pp. 593-625 ◽  
Author(s):  
Chi-Hé Elder ◽  
Michael Haugh
Keyword(s):  
Real Time ◽  
Theoretical Assumption ◽  
Fine Grained ◽  
Local Process ◽  
Speaker Meaning ◽  
Pragmatic Analysis ◽  
Proposed Model ◽  
Partial Representation ◽  
Meaning Negotiation ◽  
Theories Of Meaning

Abstract Dominant accounts of “speaker meaning” in post-Gricean contextualist pragmatics tend to focus on single utterances, making the theoretical assumption that the object of pragmatic analysis is restricted to cases where speakers and hearers agree on utterance meanings, leaving instances of misunderstandings out of their scope. However, we know that divergences in understandings between interlocutors do often arise, and that when they do, speakers can engage in a local process of meaning negotiation. In this paper, we take insights from interactional pragmatics to offer an empirically informed view on speaker meaning that incorporates both speakers’ and hearers’ perspectives, alongside a formalization of how to model speaker meanings in such a way that we can account for both understandings – the canonical cases – and misunderstandings, but critically, also the process of interactionally negotiating meanings between interlocutors. We highlight that utterance-level theories of meaning provide only a partial representation of speaker meaning as it is understood in interaction, and show that inferences about a given utterance at any given time are formally connected to prior and future inferences of participants. Our proposed model thus provides a more fine-grained account of how speakers converge on speaker meanings in real time, showing how such meanings are often subject to a joint endeavor of complex inferential work.

scholarly journals The Accidental Checkmate: Understanding the Intent behind sharing Misinformation on Social Media

2021 ◽  
Author(s):  
Ajay Agarwal
Keyword(s):  
Social Media ◽  
Neural System ◽  
Human Beings ◽  
World Community ◽  
Independent Analysis ◽  
False Sense ◽  
Media Platform ◽  
The Common ◽  
Fact Checking ◽  
Independent Parameters

The bloom of COVID19 has resulted in the explosion of ripple pollens which have severely affected the world community in the terms of their multi-axial impact. These pollens, despite being indistinguishable, have a varied set of characteristics in terms of their origin and contribution towards the overall declining homeostasis of human beings. The most prominent of these pollens are misinformation. Various studies have been conducted, performed, and stochastically replicated to build ML-based models to accurately detect misinformation and its variates on the common modalities of spread. However, the recent independent analysis conducted on the prior studies reveals how the current fact-checking systems fail and fall flat in fulfilling any practical demands that the misinfodemic of COVID19 brought for us. While the scientific community broadly accepts the pandemic-like resemblance of the rampant misinformation spread, we must also make sure that our response to the same is multi-faceted, interdisciplinary, and doesn't stand restricted. As crucial it is to chart the features of misinformation spread, it is also important to understand why it spreads in the first place? Our paper deals with the latter question through a game-theory-based approach. We implement a game with two social media users or players who aim at increasing their outreach on their social media handles whilst spreading misinformation knowingly. We take five independent parameters from 100 Twitter handles that have shared misinformation during the period of COVID19. Twitter was chosen as it is a prominent social media platform accredited to the major modality for misinformation spread. The outreach increment on the user’s Twitter handles was measured using various features provided by Twitter- number of comments, number of retweets, and number of likes. Later, using a computational neuroscientific approach, we map each of these features with the type of neural system they trigger in a person’s brain. This helps in understanding how misinformation whilst being used as an intentional decoy to increase outreach on social media, also, affects the human social cognition system eliciting pseudo-responses that weren’t intended otherwise leading to realizing possible neuroscientific correlation as to how spreading misinformation on social media intentionally/unintentionally becomes a strategic maneuver to increased reach and possibly a false sense of accomplishment.

scholarly journals Circulating Exosomal miRNAs as Novel Biomarkers Perform Superior Diagnostic Efficiency Compared With Plasma miRNAs for Large-Artery Atherosclerosis Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Mengying Niu ◽  
Hong Li ◽  
Xu Li ◽  
Xiaoqian Yan ◽  
Aijun Ma ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
High Throughput Sequencing ◽  
Area Under The Curve ◽  
Large Artery ◽  
Diagnostic Efficiency ◽  
Validation Phase ◽  
Discovery Phase ◽  
Exosomal Mirnas ◽  
Small Artery Occlusion ◽  
Plasma Mirnas

Recently, exosomal miRNAs have been reported to be associated with some diseases, and these miRNAs can be used for diagnosis and treatment. However, diagnostic biomarkers of exosomal miRNAs for ischemic stroke have rarely been studied. In the present study, we aimed to identify exosomal miRNAs that are associated with large-artery atherosclerosis (LAA) stroke, the most common subtype of ischemic stroke; to further verify their diagnostic efficiency; and to obtain promising biomarkers. High-throughput sequencing was performed on samples from 10 subjects. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed on exosomes and plasma in the discovery phase (66 subjects in total) and the validation phase (520 subjects in total). We identified 5 candidate differentially expressed miRNAs (miR-369-3p, miR-493-3p, miR-379-5p, miR-1296-5p, and miR-1277-5p) in the discovery phase according to their biological functions, 4 of which (miR-369-3p, miR-493-3p, miR-379-5p, and miR-1296-5p) were confirmed in the validation phase. These four exosomal miRNAs could be used to distinguish LAA samples from small artery occlusion (SAO) samples, LAA samples from atherosclerosis (AS) samples, and LAA samples from control samples and were superior to plasma miRNAs. In addition, composite biomarkers achieved higher area under the curve (AUC) values than single biomarkers. According to our analysis, the expression levels of exosomal miR-493-3p and miR-1296-5p were negatively correlated with the National Institutes of Health Stroke Scale (NIHSS) score. The four identified exosomal miRNAs are promising biomarkers for the diagnosis of LAA stroke, and their diagnostic efficiency is superior to that of their counterparts in plasma.

scholarly journals Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

2020 ◽  
Vol 34 (05) ◽  
pp. 7391-7398
Author(s):  
Muhammad Asif Ali ◽  
Yifang Sun ◽  
Bing Li ◽  
Wei Wang
Keyword(s):  
Language Processing ◽  
Training Data ◽  
Specific Context ◽  
Fine Grained ◽  
Named Entity ◽  
Distant Supervision ◽  
Proposed Model ◽  
Wide Range ◽  
Relative Score ◽  
Noisy Labels

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention's sentence-specific context. This is inadequate for highly overlapping and/or noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro-f1 and micro-f1 respectively.

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