OBJECTIVE: Physical distancing and stay-at-home measures implemented to slow transmission of novel coronavirus disease (COVID-19) may intensify feelings of loneliness in older adults, especially those living alone. Our aim was to characterise the extent of loneliness during the first wave in a sample of older adults living in the community and assess characteristics associated with loneliness. DESIGN: Online cross-sectional survey between 6 May and 19 May 2020. SETTING: Ontario, Canada. PARTICIPANTS: Convenience sample of members of a national retired educators' organisation. PRIMARY OUTCOME MEASURES: Self-reported loneliness, including differences between women and men. RESULTS: 4879 respondents (71.0% women; 67.4% 65-79 years) reported that in the preceding week, 43.1% felt lonely at least some of the time, including 8.3% who felt lonely always or often. Women had increased odds of loneliness compared with men, whether living alone (adjusted OR (aOR) 1.52, 95% CI 1.13 to 2.04) or with others (2.44, 95% CI 2.04 to 2.92). Increasing age group decreased the odds of loneliness (aOR 0.69 (95% CI 0.59 to 0.81) 65-79 years and 0.50 (95% CI 0.39 to 0.65) 80+ years compared with <65 years). Living alone was associated with loneliness, with a greater association in men (aOR 4.26, 95% CI 3.15 to 5.76) than women (aOR 2.65, 95% CI 2.26 to 3.11). Other factors associated with loneliness included: fair or poor health (aOR 1.93, 95% CI 1.54 to 2.41), being a caregiver (aOR 1.18, 95% CI 1.02 to 1.37), receiving care (aOR 1.47, 95% CI 1.19 to 1.81), high concern for the pandemic (aOR 1.55, 95% CI 1.31 to 1.84), not experiencing positive effects of pandemic distancing measures (aOR 1.94, 95% CI 1.62 to 2.32) and changes to daily routine (aOR 2.81, 95% CI 1.96 to 4.03). CONCLUSIONS: While many older adults reported feeling lonely during COVID-19, several characteristics-such as being female and living alone-increased the odds of loneliness. These characteristics may help identify priorities for targeting interventions to reduce loneliness.

https://bmjopen.bmj.com/content/bmjopen/11/4/e044517.full.pdf

Loneliness among older adults in the community during COVID-19: a cross-sectional survey in Canada.

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

/pdf/a-systematic-review-of-artificial-intelligence-techniques-in-1wo9rqflz9.pdf

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Background and Aim Loneliness is a common problem in older adults and contributes to poor health This scoping review aimed to synthesize and report evidence on the effectiveness of interventions using social robots or computer agents to reduce loneliness in older adults and to explore intervention strategies Methods The review adhered to the Arksey and O'Malley process for conducting scoping reviews The SCOPUS, PUBMED, Web of Science, EMBASE, CINAHL, PsycINFO, ACM Digital Library and IEEE Xplore databases were searched in November, 2020 A two-step selection process identified eligible research Information was extracted from papers and entered into an Excel coding sheet and summarised Quality assessments were conducted using the Mixed Methods Appraisal Tool Results Twenty-nine studies were included, of which most were of moderate to high quality Eighteen were observational and 11 were experimental Twenty-four used robots, four used computer agents and one study used both The majority of results showed that robots or computer agents positively impacted at least one loneliness outcome measure Some unintended negative consequences on social outcomes were reported, such as sadness when the robot was removed Overall, the interventions helped to combat loneliness by acting as a direct companion (69%), a catalyst for social interaction (41%), facilitating remote communication with others (10%) and reminding users of upcoming social engagements (3%) Conclusion Evidence to date suggests that robots can help combat loneliness in older adults, but there is insufficient research on computer agents Common strategies for reducing loneliness include direct companionship and enabling social interactions Future research could investigate other strategies used in human interventions (eg, addressing maladaptive social cognition and improving social skills), and the effects of design features on efficacy It is recommended that more robust experimental and mixed methods research be conducted, using a combination of validated self-report, observational, and interview measures of loneliness

/pdf/friends-from-the-future-a-scoping-review-of-research-into-3cvqhndmo1.pdf

Friends from the Future: A Scoping Review of Research into Robots and Computer Agents to Combat Loneliness in Older People

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.

/pdf/medical-internet-of-things-based-breast-cancer-diagnosis-7q7tv9al.pdf

Medical Internet-of-Things Based Breast Cancer Diagnosis Using Hyperparameter-Optimized Neural Networks

The outbreak of Coronavirus (COVID-19) has spread between people around the world at a rapid rate so that the number of infected people and deaths is increasing quickly every day. Accordingly, it is a vital process to detect positive cases at an early stage for treatment and controlling the disease from spreading. Several medical tests had been applied for COVID-19 detection in certain injuries, but with limited efficiency. In this study, a new COVID-19 diagnosis strategy called Feature Correlated Naive Bayes (FCNB) has been introduced. The FCNB consists of four phases, which are; Feature Selection Phase (FSP), Feature Clustering Phase (FCP), Master Feature Weighting Phase (MFWP), and Feature Correlated Naive Bayes Phase (FCNBP). The FSP selects only the most effective features among the extracted features from laboratory tests for both COVID-19 patients and non-COVID-19 people by using the Genetic Algorithm as a wrapper method. The FCP constructs many clusters of features based on the selected features from FSP by using a novel clustering technique. These clusters of features are called Master Features (MFs) in which each MF contains a set of dependent features. The MFWP assigns a weight value to each MF by using a new weight calculation method. The FCNBP is used to classify patients based on the weighted Naive Bayes algorithm with many modifications as the correlation between features. The proposed FCNB strategy has been compared to recent competitive techniques. Experimental results have proven the effectiveness of the FCNB strategy in which it outperforms recent competitive techniques because it achieves the maximum (99%) detection accuracy.

/pdf/accurate-detection-of-covid-19-patients-based-on-feature-2xwfuip349.pdf

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy.

The use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.

/pdf/text-messaging-based-medical-diagnosis-using-natural-fz12drxysc.pdf

Israel O. Ndaman

Papers

Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic