Social media-based COVID-19 sentiment classification model using Bi-LSTM.

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples' concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.

Intelligent Real-Time Face-Mask Detection System with Hardware Acceleration for COVID-19 Mitigation.

This paper proposes and implements a dedicated hardware accelerated real-time face-mask detection system using deep learning (DL). The proposed face-mask detection model (MaskDetect) was benchmarked on three embedded platforms: Raspberry PI 4B with either Google Coral USB TPU or Intel Neural Compute Stick 2 VPU, and NVIDIA Jetson Nano. The MaskDetect was independently quantised and optimised for each hardware accelerated implementation. An ablation study was carried out on the proposed model and its quantised implementations on the embedded hardware configurations above as a comparison to other popular transfer-learning models, such as VGG16, ResNet-50V2, and InceptionV3, which are compatible with these acceleration hardware platforms. The ablation study revealed that MaskDetect achieved excellent average face-mask detection performance with accuracy above 94% across all embedded platforms except for Coral, which achieved an average accuracy of nearly 90%. With respect to detection performance (accuracy), inference speed (frames per second (FPS)), and product cost, the ablation study revealed that implementation on Jetson Nano is the best choice for real-time face-mask detection. It achieved 94.2% detection accuracy and twice greater FPS when compared to its desktop hardware counterpart.

Automated clinical pathway standardization using SNOMED CT- based semantic relatedness.

The increasing number of patients and heavy workload drive health care institutions to search for efficient and cost-effective methods to deliver optimal care. Clinical pathways are promising care plans that proved to be efficient in reducing costs and optimizing resource usage. However, most clinical pathways are circulated in paper-based formats. Clinical pathway computerization is an emerging research field that aims to integrate clinical pathways with health information systems. A key process in clinical pathway computerization is the standardization of clinical pathway terminology to comply with digital terminology systems. Since clinical pathways include sensitive medical terms, clinical pathway standardization is performed manually and is difficult to automate using machines. The objective of this research is to introduce automation to clinical pathway standardization. The proposed approach utilizes a semantic score-based algorithm that automates the search for SNOMED CT terms. The algorithm was implemented in a software system with a graphical user interface component that physicians can use to standardize clinical pathways by searching for and comparing relevant SNOMED CT retrieved automatically by the algorithm. The system has been tested and validated on SNOMED CT ontology. The experimental results show that the system reached a maximum search space reduction of 98.9% within any single iteration of the algorithm and an overall average of 71.3%. The system enables physicians to locate the proper terms precisely, quickly, and more efficiently. This is demonstrated using case studies, and the results show that human-guided automation is a promising methodology in the field of clinical pathway standardization and computerization.

Ontological framework for standardizing and digitizing clinical pathways in healthcare information systems.

BACKGROUND AND OBJECTIVE: Most healthcare institutions are reorganizing their healthcare delivery systems based on Clinical Pathways (CPs). CPs are novel medical management plans to standardize medical activities, reduce cost, optimize resource usage, and improve the quality of service. However, most CPs are still paper-based and not fully integrated with Health Information Systems (HIS). More CP computerization research is therefore needed to fully benefit from CP's practical potentials. A major contribution of this research is the vision that CP systems deserve to be placed at the centre of HIS, because within CPs lies the very heart of medical planning, treatment and impressions, including healthcare quality and cost factors. METHODS: An important contribution to the realization of this vision is to fully standardize and digitize CPs so that they become machine-readable and smoothly linkable across various HIS. To achieve this goal, this research proposes a framework for (i) CP knowledge representation and sharing using ontologies, (ii) CP standardization based on SNOMED CT and HL7, and (iii) CP digitization based on a novel coding system to encode CP data. To show the feasibility of the proposed framework we developed a prototype clinical pathway management system (CPMS) based on CPs currently in use at hospitals. RESULTS: The results show that CPs can be fully standardized and digitized using SNOMED CT terms and codes, and the CPMS can work as an independent system, performing novel CP-related functions, including useful data analytics. CPs can be compared easily for auditing and quality management. Furthermore, the CPMS was smoothly linked to a hospital EMR and CP data were captured in EMR without any loss. CONCLUSION: The proposed framework is promising and contributes toward solving major challenges related to CP standardization, digitization, and inclusion in today's modern computerized hospitals.