Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

Healthcare organizations rely on patients’feedback and experiences to evaluate their performance and services,thereby allowing such organizations to improve inadequate services and address any shortcomings.According to the literature,social networks and particularly Twitter are effective platforms for gathering public opinions.Moreover,recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors,including healthcare.The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus.The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January 2022.The tweets were labeled according to sentiment(positive or negative)and sector(public or private),and thereby the Hospital Patient Experiences in Saudi Arabia(HoPE-SA)dataset was produced.A simple statistical analysis was conducted to examine differences in patient views of healthcare sectors.The authors trained five models to distinguish sentiments in tweets automatically with the following schemes:a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture,using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers(BERT),Multi-dialect Arabic BERT(MAR-BERT),and multilingual BERT(mBERT),as well as a pretrained word2vec model with a support vector machine classifier.This is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of MARBERT.The deep-learning fine-tuned MARBERT-based model—the authors’best-performing model—achieved accuracy,micro-F1,and macro-F1 scores of 98.71%,98.73%,and 98.63%,respectively.

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times,cities are getting smart and can be managed effectively through diverse architectures and services.Smart cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic diseases.With latest developments in digital data collection,AI techniques can be employed for clinical decision making process.On the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the globe.Generally,wearables can be employed in healthcare systems that instigate the development of CVD detection and classification.With this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC technique.The proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data collection.Then,the collected data is saved in cloud server for examination.Followed by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health condition.To accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)technique.In addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of CVD.The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

IWD-Miner: A Novel Metaheuristic Algorithm for Medical Data Classification

Medical data classification(MDC)refers to the application of classification methods on medical datasets.This work focuses on applying a classification task to medical datasets related to specific diseases in order to predict the associated diagnosis or prognosis.To gain experts’trust,the prediction and the reasoning behind it are equally important.Accordingly,we confine our research to learn rule-based models because they are transparent and comprehensible.One approach to MDC involves the use of metaheuristic(MH)algorithms.Here we report on the development and testing of a novel MH algorithm:IWD-Miner.This algorithm can be viewed as a fusion of Intelligent Water Drops(IWDs)and AntMiner+.It was subjected to a four-stage sensitivity analysis to optimize its performance.For this purpose,21 publicly available medical datasets were used from the Machine Learning Repository at the University of California Irvine.Interestingly,there were only limited differences in performance between IWDMiner variants which is suggestive of its robustness.Finally,using the same 21 datasets,we compared the performance of the optimized IWD-Miner against two extant algorithms,AntMiner+and J48.The experiments showed that both rival algorithms are considered comparable in the effectiveness to IWD-Miner,as confirmed by the Wilcoxon nonparametric statistical test.Results suggest that IWD-Miner is more efficient than AntMiner+as measured by the average number of fitness evaluations to a solution(1,386,621.30 vs.2,827,283.88 fitness evaluations,respectively).J48 exhibited higher accuracy on average than IWD-Miner(79.58 vs.73.65,respectively)but produced larger models(32.82 leaves vs.8.38 terms,respectively).

Impact of preprocessing on medical data classification

The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics of medical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of prepro- cessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations con- sidered include the discretization of numeric attributes, the selection of attribute subset（s）, and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained.