Intelligent Early Diagnosis System against Strep Throat Infection Using Deep Neural Networks

The most frequent bacterial pathogen causing acute pharyngitis is Group-A hemolytic Streptococcus (GAS), and sore throat is the second most frequent acute infection. The immunological reaction to group A Streptococcus-induced pharyngitis results in Acute Rheumatic Fever (ARF). A genetically vulnerable host for ARF is a streptococcal infection. ARF, which can affect various organs and cause irreparable valve damage and heart failure, is the antecedent to Rheumatic Heart Disease (RHD). RHD, in many countries is Cardiovascular Disease (CVD) refers to a range of conditions that affect the heart and blood vessels, including coronary artery disease, heart attack, heart failure, and stroke. It is important to note that while this approach has demonstrated promising results, further studies and validation are necessary to establish its clinical feasibility and reliability. Further research can also be done to evaluate the generalization of the model to larger and diverse patient populations. The results showed that using Image Synthesis-based augmentation improved the ROC-AUC scores compared to basic data augmentation. The proposed method could be a valuable tool for healthcare professionals to quickly and accurately diagnose strep throat, leading to timely treatment and improved patient outcomes. The experimental findings indicate that the suggested detection approach for strep throat has a high level of accuracy and effectiveness. The approach has an average sensitivity of 93.1%, average specificity of 96.7%, and an overall accuracy of 96.3%. The ROC-AUC of 0.989 suggests that the approach is effective at distinguishing between positive and negative cases of strep throat. These results indicate that the suggested detection approach is a promising tool for accurately identifying cases of strep throat

A novel hybrid deep learning model for human activity recognition based on transitional activities

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision

Innovations in Domain Knowledge Augmentation of Contextual Models

The digital transformation of our society is creating a tremendous amount of data at an unprecedented rate. A large part of this data is in unstructured text format. While enjoying the benefit of instantaneous data access, we are also burdened by information overload. In healthcare, clinicians have to spend a significant portion of their time reading, writing and synthesizing data in electronic patient record systems. Information overload is reported as one of the main factors contributing to physician burnout; however, information overload is not unique to healthcare. We need better practical tools to help us access the right information at the right time. This has led to a heightened interest in high-performing Natural Language Processing research and solutions. Natural Language Processing (NLP), or Computational Linguistics, is a sub-field of computer science that focuses on analyzing and representing human language. The most recent advancements in NLP are large pre-trained contextual language models (e.g., transformer based models), which are pre-trained on massive corpora, and their context-sensitive embeddings (i.e., learned representation of words) are used in downstream tasks. The introduction of these models has led to significant performance gains in various downstream tasks, including sentiment analysis, entity recognition, and question answering. Such models have the ability to change the embedding of a word based on its imputed meaning, which is derived from the surrounding context. Contextual models can only encode the knowledge available in raw text corpora. Injecting structured domain-specific knowledge into these contextual models could further improve their performance and efficiency. However, this is not a trivial task. It requires a deep understanding of the model’s architecture and the nature and structure of the domain knowledge incorporated into the model. Another challenge facing NLP is the “low-resource” problem, arising from a shortage of publicly available (domain-specific) large datasets for training purposes. The low-resource challenge is especially acute in the biomedical domain, where strict regulation for privacy protection prohibits many datasets from being publicly available to the NLP community. The severe shortage of clinical experts further exacerbates the lack of labeled training datasets for clinical NLP research. We approach these challenges from the knowledge augmentation angle. This thesis explores how knowledge found in structured knowledge bases, either in general-purpose lexical databases (e.g., WordNet) or domain-specific knowledge bases (e.g., the Unified Medical Language Systems or the International Classification of Diseases), can be used to address the low-resource problem. We show that by incorporating domain-specific prior knowledge into a deep learning NLP architecture, we can force an NLP model to learn the associations between distinctive terminologies that it otherwise may not have the opportunity to learn due to the scarcity of domain-specific datasets. Four distinct yet complementary strategies have been pursued. First, we investigate how contextual models can use structured knowledge contained in the lexical database WordNet to distinguish between semantically similar words. We update the input policy of a contextual model by introducing a new mix-up embedding strategy for the input embedding of the target word. We also introduce additional information, such as the degree of similarity between the definitions of the target and the candidate words. We demonstrate that this supplemental information has enabled the model to select candidate words that are semantically similar to the target word rather than those that are only appropriate for the sentence’s context. Having successfully proven that lexical knowledge can aid a contextual model in distinguishing between semantically similar words, we extend this approach to highly specialized vocabularies such as those found in medical text. We explore whether using domain-specific (medical) knowledge from a clinical Metathesaurus (UMLS Metathesaurus) in the architecture of a transformer-based encoder model can aid the model in building ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and the domain knowledge. We also investigate whether incorporating structured medical knowledge into the pre-training phase of a transformer-based model can incentivize the model to learn more accurately the association between distinctive terminologies. This strategy is proven to be effective through a series of benchmark comparisons with other related models. After demonstrating the effect of structured domain (medical) knowledge on the performance of a transformer-based encoder model, we extend the medical features and illustrate that structured medical knowledge can also boost the performance of a (medical) summarization transformer-based sequence-to-sequence model. We introduce a guidance signal consisting of the medical terminologies in the input sequence. Moreover, the input policy is modified by utilizing the semantic types from UMLS, and we also propose a novel weighted loss function. Our study demonstrates the benefit of these strategies in providing a stronger incentive for the model to include relevant medical facts in the summarized output. We further examine whether an NLP model can take advantage of both the relational information between different labels and contextual embedding information by introducing a novel attention mechanism (instead of augmenting the architecture of contextual models with structured information as described in the previous paragraphs). We tackle the challenge of automatic ICD coding, which is the task of assigning codes of the International Classification of Diseases (ICD) system to medical notes. Through a novel attention mechanism, we integrate the information from a Graph Convolutional Network (GCN) that considers the relationship between various codes with the contextual sentence embeddings of the medical notes. Our experiments reveal that this enhancement effectively boosts the model’s performance in the automatic ICD coding task. The main contribution of this thesis is two-fold: (1) this thesis contributes to the computer science literature by demonstrating how domain-specific knowledge can be effectively incorporated into contextual models to improve model performance in NLP tasks that lack helpful training resources; and (2) the knowledge augmentation strategies and the contextual models developed in this research are shown to improve NLP performance in the biomedical field, where publicly available training datasets are scarce but domain-specific knowledge bases and data standards have achieved a wide adoption in electronic medical records systems

A review of deep learning algorithms for computer vision systems in livestock.

In livestock operations, systematically monitoring animal body weight, bio-metric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to labor, costs, and animal stress. Applications of computer vision are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. However, the development of a computer vision system requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves mas-sive datasets. This article aims to provide an overview of how deep learning has been implemented in computer vision systems used in livestock, and how such implementation can be an effective tool to predict animal phe-notypes and to accelerate the development of predictive modeling for precise management decisions. First, we reviewed the most recent milestones achieved with computer vision systems and its respective deep learning algorithms implemented in Animal Science studies. Second, we reviewed the published research studies in Animal Science, which used deep learning algorithms as the primary analytical strategy for image classification, object detection, object segmentation, and feature extraction. The great number of reviewed articles published in the last few years demonstrates the high interest and rapid development of deep learning algorithms in computer vision systems across livestock species. Deep learning algorithms for computer vision systems, such as Mask R-CNN, Faster R-CNN, YOLO (v3 and v4), DeepLab v3, U-Net and others have been used in Animal Science research studies. Additionally, network architectures such as ResNet, Inception, Xception, and VGG16 have been implemented in several studies across livestock species. The great performance of these deep learning algorithms suggests an33improved predictive ability in livestock applications and a faster inference.34However, only a few articles fully described the deep learning algorithms and its implementation. Thus, information regarding hyperparameter tuning, pre-trained weights, deep learning backbone, and hierarchical data structure were missed. We summarized peer-reviewed articles by computer vision tasks38(image classification, object detection, and object segmentation), deep learn-39ing algorithms, species, and phenotypes including animal identification and behavior, feed intake, animal body weight, and many others. Understanding the principles of computer vision and the algorithms used for each application is crucial to develop efficient systems in livestock operations. Such development will potentially have a major impact on the livestock industry by predicting real-time and accurate phenotypes, which could be used in the future to improve farm management decisions, breeding programs through high-throughput phenotyping, and optimized data-driven interventions

Remote health monitoring systems for elderly people: a survey

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals

PetBERT: automated ICD-11 syndromic disease coding for outbreak detection in first opinion veterinary electronic health records

Effective public health surveillance requires consistent monitoring of disease signals such that researchers and decision-makers can react dynamically to changes in disease occurrence. However, whilst surveillance initiatives exist in production animal veterinary medicine, comparable frameworks for companion animals are lacking. First-opinion veterinary electronic health records (EHRs) have the potential to reveal disease signals and often represent the initial reporting of clinical syndromes in animals presenting for medical attention, highlighting their possible significance in early disease detection. Yet despite their availability, there are limitations surrounding their free text-based nature, inhibiting the ability for national-level mortality and morbidity statistics to occur. This paper presents PetBERT, a large language model trained on over 500 million words from 5.1 million EHRs across the UK. PetBERT-ICD is the additional training of PetBERT as a multi-label classifier for the automated coding of veterinary clinical EHRs with the International Classification of Disease 11 framework, achieving F1 scores exceeding 83% across 20 disease codings with minimal annotations. PetBERT-ICD effectively identifies disease outbreaks, outperforming current clinician-assigned point-of-care labelling strategies up to 3 weeks earlier. The potential for PetBERT-ICD to enhance disease surveillance in veterinary medicine represents a promising avenue for advancing animal health and improving public health outcomes

Cognitive computing and wireless communications on the edge for healthcare service robots

In recent years, we have witnessed dramatic developments of mobile healthcare robots, which enjoy many advantages over their human counterparts. Previous communication networks for healthcare robots always suffer from high response latency and/or time-consuming computing demands. Robust and high-speed communications and swift processing are critical, sometimes vital in particular in the case of healthcare robots, to the healthcare receivers. As a promising solution, offloading delay-sensitive and communicating-intensive tasks to the robot is expected to improve the services and benefit users. In this paper, we review several state-of-the-art technologies, such as the human–robot interface, environment and user status perceiving, navigation, robust communication and artificial intelligence, of a mobile healthcare robot and discuss in details the customized demands over offloading the computation and communication tasks. According to the intrinsic demands of tasks over the network usage, we categorize abilities of a typical healthcare robot into alternative classes: the edge functionalities and the core functionalities. Many latency-sensitive tasks, such as user interaction, or time-consuming tasks including health receiver status recognition and autonomous moving, can be processed by the robot without frequent communications with data centers. On the other hand, several fundamental abilities, such as radio resource management, mobility management, service provisioning management, need to update the main body with the cutting-edge artificial intelligence. Robustness and safety, in this case, are the primary goals in wireless communications that AI may provide ground-breaking solutions. Based on this partition, this article refers to several state-of-the-art technologies of a mobile healthcare robot and reviews some challenges to be met for its wireless communications

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review

© 2020 Elsevier Ltd. All rights reserved.Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.Peer reviewe

Internet of Things data contextualisation for scalable information processing, security, and privacy

The Internet of Things (IoT) interconnects billions of sensors and other devices (i.e., things) via the internet, enabling novel services and products that are becoming increasingly important for industry, government, education and society in general. It is estimated that by 2025, the number of IoT devices will exceed 50 billion, which is seven times the estimated human population at that time. With such a tremendous increase in the number of IoT devices, the data they generate is also increasing exponentially and needs to be analysed and secured more efficiently. This gives rise to what is appearing to be the most significant challenge for the IoT: Novel, scalable solutions are required to analyse and secure the extraordinary amount of data generated by tens of billions of IoT devices. Currently, no solutions exist in the literature that provide scalable and secure IoT scale data processing. In this thesis, a novel scalable approach is proposed for processing and securing IoT scale data, which we refer to as contextualisation. The contextualisation solution aims to exclude irrelevant IoT data from processing and address data analysis and security considerations via the use of contextual information. More specifically, contextualisation can effectively reduce the volume, velocity and variety of data that needs to be processed and secured in IoT applications. This contextualisation-based data reduction can subsequently provide IoT applications with the scalability needed for IoT scale knowledge extraction and information security. IoT scale applications, such as smart parking or smart healthcare systems, can benefit from the proposed method, which  improves the scalability of data processing as well as the security and privacy of data.   The main contributions of this thesis are: 1) An introduction to context and contextualisation for IoT applications; 2) a contextualisation methodology for IoT-based applications that is modelled around observation, orientation, decision and action loops; 3) a collection of contextualisation techniques and a corresponding software platform for IoT data processing (referred to as contextualisation-as-a-service or ConTaaS) that enables highly scalable data analysis, security and privacy solutions; and 4) an evaluation of ConTaaS in several IoT applications to demonstrate that our contextualisation techniques permit data analysis, security and privacy solutions to remain linear, even in situations where the number of IoT data points increases exponentially