Locating Loneliness Through Social Intelligence Analysis.

Loneliness is a global public health issue, but the dynamics of loneliness are not understood. Through a global loneliness map, we plan to understand the dynamics of loneliness better by analyzing social media data on loneliness through social intelligence analysis. In this paper, we present the first proof of concept of the global loneliness map. Data on loneliness using keywords associated with loneliness was collected from the USA and analyzed to find meaningful associations of themes with loneliness. The NLP tool used for sentiment analysis of the tweets is a valence aware dictionary for sentiment reasoning (VADER). The tweets with negative sentiment were further analyzed for psychosocial linguistic features to find meaningful correlation between loneliness and socioeconomic and emotional themes and factors. Loneliness is subjective, hence social intelligence analysis through social media and machine learning tools can help us better understand loneliness.

Mapping loneliness through comparative analysis of USA and India using social intelligence analysis.

Loneliness, a widespread global public health concern, has far-reaching implications for mental and physical well-being, as well as economic productivity. It also increases the risk of life-threatening conditions. This study conducts a comparative analysis of loneliness in the USA and India using Twitter data, aiming to contribute to a global public health map on loneliness. Collecting 4.1 million tweets globally in October 2022 containing keywords like "lonely", "loneliness", and "alone", the analysis focuses on sentiment and psychosocial linguistic features. Utilizing the Valence Aware Dictionary for Sentiment Reasoning (VADER) for sentiment analysis, the study explores variations in loneliness dynamics across cities, revealing geographical distinctions in correlated topics. The tweets with negative sentiment were further analyzed for psychosocial linguistic features to find a meaningful correlation between loneliness and socioeconomic and emotional themes and factors. Results give detailed top correlated topics with loneliness for each city. The results showed that the dynamics of loneliness through the topics correlated vary across geographical locations. Social media data can be used to capture the dynamics of loneliness which can vary from one place to another depending on the socioeconomic and cultural norms and sociopolitical policies. Social media data to understand loneliness can also provide useful information and insight for public health and policymaking.

Understanding Loneliness in Younger People: Review of the Opportunities and Challenges for Loneliness Interventions.

Loneliness affects the quality of life of people all around the world. Loneliness is also shown to be directly associated with mental health issues and is often the cause of mental health problems. It is also shown to increase the risk of heart diseases and other physical illnesses. Loneliness is studied both from the social and medical sciences perspectives. There are also interventions on the basis of health informatics, information and communication technologies (ICTs), social media, and other technological solutions. In the literature, loneliness is studied from various angles and perspectives ranging from biological to socioeconomical and through anthropological understandings of technology. From the ICT and technological sides, there are multiple reviews studying the effectiveness of intervention strategies and solutions. However, there is a lack of a comprehensive review on loneliness that engulfs the psychological, social, and technological studies of loneliness. From the perspective of loneliness informatics (ie, the application of health informatics practices and tools), it is important to understand the psychological and biological basis of loneliness. When it comes to technological interventions to fight off loneliness, the majority of interventions focus on older people. While loneliness is highest among older people, theoretical and demographical studies of loneliness give a U-shaped distribution age-wise to loneliness; that is, younger people and older people are the demographics most affected by loneliness. But the strategies and interventions designed for older people cannot be directly applied to younger people. We present the dynamics of loneliness in younger people and also provide an overview of the technological interventions for loneliness in younger people. This paper presents an approach wherein the studies carried out from the perspectives of digital health and informatics are discussed in detail. A comprehensive overview of the understanding of loneliness and the study of the overall field of tools and strategies of loneliness informatics was carried out. The need to study loneliness in younger people is addressed and particular digital solutions and interventions developed for younger people are presented. This paper can be used to overcome the challenges of technological gaps in the studies and strategies developed for loneliness. The findings of this study show that the majority of interventions and reviews are focused on older people, with ICT-based and social media-based interventions showing promise for countering the effects of loneliness. There are new technologies, such as conversational agents and robots, which are tailored to the particular needs of younger people. This literature review suggests that the digital solutions developed to overcome loneliness can benefit people, and younger people in particular, more if they are made interactive in order to retain users.

Mapping loneliness through social intelligence analysis: a step towards creating global loneliness map.

OBJECTIVES: Loneliness is a prevalent global public health concern with complex dynamics requiring further exploration. This study aims to enhance understanding of loneliness dynamics through building towards a global loneliness map using social intelligence analysis. SETTINGS AND DESIGN: This paper presents a proof of concept for the global loneliness map, using data collected in October 2022. Twitter posts containing keywords such as 'lonely', 'loneliness', 'alone', 'solitude' and 'isolation' were gathered, resulting in 841 796 tweets from the USA. City-specific data were extracted from these tweets to construct a loneliness map for the country. Sentiment analysis using the valence aware dictionary for sentiment reasoning tool was employed to differentiate metaphorical expressions from meaningful correlations between loneliness and socioeconomic and emotional factors. MEASURES AND RESULTS: The sentiment analysis encompassed the USA dataset and city-wise subsets, identifying negative sentiment tweets. Psychosocial linguistic features of these negative tweets were analysed to reveal significant connections between loneliness, socioeconomic aspects and emotional themes. Word clouds depicted topic variations between positively and negatively toned tweets. A frequency list of correlated topics within broader socioeconomic and emotional categories was generated from negative sentiment tweets. Additionally, a comprehensive table displayed top correlated topics for each city. CONCLUSIONS: Leveraging social media data provide insights into the multifaceted nature of loneliness. Given its subjectivity, loneliness experiences exhibit variability. This study serves as a proof of concept for an extensive global loneliness map, holding implications for global public health strategies and policy development. Understanding loneliness dynamics on a larger scale can facilitate targeted interventions and support.

Deep Learning in Colorectal Cancer Classification: A Scoping Review.

Colorectal cancer (CRC) is one of the most common cancers worldwide, and its diagnosis and classification remain challenging for pathologists and imaging specialists. The use of artificial intelligence (AI) technology, specifically deep learning, has emerged as a potential solution to improve the accuracy and speed of classification while maintaining the quality of care. In this scoping review, we aimed to explore the utilization of deep learning for the classification of different types of colorectal cancer. We searched five databases and selected 45 studies that met our inclusion criteria. Our results show that deep learning models have been used to classify colorectal cancer using various types of data, with histopathology and endoscopy images being the most common. The majority of studies used CNN as their classification model. Our findings provide an overview of the current state of research on deep learning in the classification of colorectal cancer.

Using AI for Detection, Prediction and Classification of Retinal Detachment.

The current state of machine learning (ML) and deep learning (DL) algorithms used to detect, classify and predict the onset of retinal detachment (RD) were examined in this scoping review. This severe eye condition can cause vision loss if left untreated. By analyzing the medical imaging modalities such as fundus photography, AI could help to detect peripheral detachment at an earlier stage. We have searched five databases: PubMed, Google Scholar, ScienceDirect, Scopus, and IEEE. Two reviewers independently carried out the selection of the studies and their data extractions. 32 studies fulfilled our eligibility criteria from the 666 references collected. In particular, based on the performance metrics employed in these studies, this scoping review provides a general overview of emerging trends and practices concerning using ML and DL algorithms for detecting, classifying, and predicting RD.

The Pros and Cons of Using ChatGPT in Medical Education: A Scoping Review.

This scoping review explores the advantages and disadvantages of using ChatGPT in medical education. We searched PubMed, Google Scholar, Medline, Scopus, and Science Direct to identify relevant studies. Two reviewers independently conducted study selection and data extraction, followed by a narrative synthesis. Out of 197 references, 25 studies met the eligibility criteria. The primary applications of ChatGPT in medical education include automated scoring, teaching assistance, personalized learning, research assistance, quick access to information, generating case scenarios and exam questions, content creation for learning facilitation, and language translation. We also discuss the challenges and limitations of using ChatGPT in medical education, such as its inability to reason beyond existing knowledge, generation of incorrect information, bias, potential undermining of students' critical thinking skills, and ethical concerns. These concerns include using ChatGPT for exam and assignment cheating by students and researchers, as well as issues related to patients' privacy.

Applications of Artificial Intelligence (AI) in Medical Education: A Scoping Review.

Artificial Intelligence (AI) is increasingly used to support medical students' learning journeys, providing personalized experiences and improved outcomes. We conducted a scoping review to explore the current application and classifications of AI in medical education. Following the PRISMA-P guidelines, we searched four databases, ultimately including 22 studies. Our analysis identified four AI methods used in various medical education domains, with the majority of applications found in training labs. The use of AI in medical education has the potential to improve patient outcomes by equipping healthcare professionals with better skills and knowledge. Post-implementation refers to the outcomes of AI-based training, which showed improved practical skills among medical students. This scoping review highlights the need for further research to explore the effectiveness of AI applications in different aspects of medical education.

Predicting Long-Term Type 2 Diabetes with Artificial Intelligence (AI): A Scoping Review.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that affects a significant portion of the global population. Artificial intelligence (AI) has emerged as a promising tool for predicting T2DM risk. To provide an overview of the AI techniques used for long-term prediction of T2DM and evaluate their performance, we conducted a scoping review using PRISMA-ScR. Of the 40 papers included in this review, 23 studies used Machine Learning (ML) as the most common AI technique, with Deep Learning (DL) models used exclusively in four studies. Of the 13 studies that used both ML and DL, 8 studies employed ensemble learning models, and SVM and RF were the most used individual classifiers. Our findings highlight the importance of accuracy and recall as validation metrics, with accuracy being used in 31 studies, followed by recall in 29 studies. These discoveries emphasize the critical role of high predictive accuracy and sensitivity in detecting positive T2DM cases.

Understanding Correlations of Loneliness in India and USA.

Loneliness is a global public health issues contributing to a variety of mental and physical health issues. It also increases the risk of life-threatening conditions as well as contributes to burden on the economy in terms of the number of days lost to productivity. Loneliness is a highly varied concept though, which is a result of multiple factors. To understand loneliness this paper carries out a comparative analysis of USA and India through Twitter data on the keywords associated with loneliness. The comparative analysis on loneliness is in the vein of comparative public health literature and to contribute to develop a global public health map on loneliness. The results showed that the dynamics of loneliness through the topics correlated vary across geographical locations. Social media data can be used to capture the dynamics of loneliness which can vary from one place to another depending on the socioeconomic and cultural norms and sociopolitical policies.

An optimized and low-cost FPGA-based DNA sequence alignment--a step towards personal genomics.

DNA sequence alignment is a cardinal process in computational biology but also is much expensive computationally when performing through traditional computational platforms like CPU. Of many off the shelf platforms explored for speeding up the computation process, FPGA stands as the best candidate due to its performance per dollar spent and performance per watt. These two advantages make FPGA as the most appropriate choice for realizing the aim of personal genomics. The previous implementation of DNA sequence alignment did not take into consideration the price of the device on which optimization was performed. This paper presents optimization over previous FPGA implementation that increases the overall speed-up achieved as well as the price incurred by the platform that was optimized. The optimizations are (1) The array of processing elements is made to run on change in input value and not on clock, so eliminating the need for tight clock synchronization, (2) the implementation is unrestrained by the size of the sequences to be aligned, (3) the waiting time required for the sequences to load to FPGA is reduced to the minimum possible and (4) an efficient method is devised to store the output matrix that make possible to save the diagonal elements to be used in next pass, in parallel with the computation of output matrix. Implemented on Spartan3 FPGA, this implementation achieved 20 times performance improvement in terms of CUPS over GPP implementation.