Publication Library
A Voice-based Triage for Type 2 Diabetes using a Conversational Virtual Assistant in the Home Environment
Description: Incorporating cloud technology with Internet of Medical Things for ubiquitous healthcare has seen many successful applications in the last decade with the advent of machine learning and deep learning techniques. One of these applications, namely voice-based pathology, has yet to receive notable attention from academia and industry. Applying voice analysis to early detection of fatal diseases holds much promise to improve health outcomes and quality of life of patients. In this paper, we propose a novel application of acoustic machine learning based triaging into commoditised conversational virtual assistant systems to pre-screen for onset of diabetes. Specifically, we developed a triaging system which extracts acoustic features from the voices of n=24 older adults when they converse with a virtual assistant and predict the incidence of Diabetes Mellitus (Type 2) or not. Our triaging system achieved hit-rates of 70% and 60% for male and female older adult subjects, respectively. Our proposed triaging uses 7 non-identifiable voice-based features and can operate within resource-constrained embedded systems running voice-based virtual assistants. This application demonstrates the feasibility of applying voice-based pathology analysis to improve health outcomes of older adults within the home environment by early detection of life-changing chronic conditions like diabetes.
Created At: 04 December 2024
Updated At: 04 December 2024
Exploring Long-Term Prediction of Type 2 Diabetes Microvascular Complications
Description: Electronic healthcare records (EHR) contain a huge wealth of data that can support the prediction of clinical outcomes. EHR data is often stored and analysed using clinical codes (ICD10, SNOMED), however these can differ across registries and healthcare providers. Integrating data across systems involves mapping between different clinical ontologies requiring domain expertise, and at times resulting in data loss. To overcome this, code-agnostic models have been proposed. We assess the effectiveness of a code-agnostic representation approach on the task of long-term microvascular complication prediction for individuals living with Type 2 Diabetes. Our method encodes individual EHRs as text using fine-tuned, pretrained clinical language models. Leveraging large-scale EHR data from the UK, we employ a multi-label approach to simultaneously predict the risk of microvascular complications across 1-, 5-, and 10-year windows. We demonstrate that a code-agnostic approach outperforms a code-based model and illustrate that performance is better with longer prediction windows but is biased to the first occurring complication. Overall, we highlight that context length is vitally important for model performance. This study highlights the possibility of including data from across different clinical ontologies and is a starting point for generalisable clinical models.
Created At: 04 December 2024
Updated At: 04 December 2024
Diabetic Retinopathy Classification from Retinal Images using Machine Learning Approaches
Description: Diabetic Retinopathy is one of the most familiar diseases and is a diabetes complication that affects eyes. Initially, diabetic retinopathy may cause no symptoms or only mild vision problems. Eventually, it can cause blindness. So early detection of symptoms could help to avoid blindness. In this paper, we present some experiments on some features of diabetic retinopathy, like properties of exudates, properties of blood vessels and properties of microaneurysm. Using the features, we can classify healthy, mild non-proliferative, moderate non-proliferative, severe non-proliferative and proliferative stages of DR. Support Vector Machine, Random Forest and Naive Bayes classifiers are used to classify the stages. Finally, Random Forest is found to be the best for higher accuracy, sensitivity and specificity of 76.5%, 77.2% and 93.3% respectively.
Created At: 04 December 2024
Updated At: 04 December 2024
Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning
Description: Real-time lower limb movement resistance monitoring is critical for various applications in clinical and sports settings, such as rehabilitation and athletic training. Current methods often face limitations in accuracy, computational efficiency, and generalizability, which hinder their practical implementation. To address these challenges, we propose a novel Mobile Multi-Task Learning Network (MMTL-Net) that integrates MobileNetV3 for efficient feature extraction and employs multi-task learning to simultaneously predict resistance levels and recognize activities. The advantages of MMTL-Net include enhanced accuracy, reduced latency, and improved computational efficiency, making it highly suitable for real-time applications. Experimental results demonstrate that MMTL-Net significantly outperforms existing models on the UCI Human Activity Recognition and Wireless Sensor Data Mining Activity Prediction datasets, achieving a lower Force Error Rate (FER) of 6.8% and a higher Resistance Prediction Accuracy (RPA) of 91.2%. Additionally, the model shows a Real-time Responsiveness (RTR) of 12 milliseconds and a Throughput (TP) of 33 frames per second. These findings underscore the model's robustness and effectiveness in diverse real-world scenarios. The proposed framework not only advances the state-of-the-art in resistance monitoring but also paves the way for more efficient and accurate systems in clinical and sports applications. In real-world settings, the practical implications of MMTL-Net include its potential to enhance patient outcomes in rehabilitation and improve athletic performance through precise, real-time monitoring and feedback.
Created At: 04 December 2024
Updated At: 04 December 2024
Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm
Description: This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that integrates edge computing and deep learning algorithms. Traditional systems often experience delays and reduced accuracy when handling complex motion data, whereas our method, by incorporating a SAC-optimized deep learning model within the IoT architecture, achieves efficient motion recognition and real-time feedback. Experimental results show that this system significantly outperforms traditional methods in response time, data processing accuracy, and energy efficiency, particularly excelling in complex track and field events. This research not only enhances the precision and efficiency of athlete monitoring but also provides new technical support and application prospects for sports science research.
Created At: 04 December 2024
Updated At: 04 December 2024