A survey on the role of artificial intelligence in managing Long COVID.

In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.

A Semantic-Based Framework for Verbal Autopsy to Identify the Cause of Maternal Death.

OBJECTIVE: Verbal autopsy is a technique used to collect information about a decedent from his/her family members using questionnaires, conducting interviews, making observations, and sampling. In substantial parts of the world, particularly in Africa and Asia, many deaths are unrecorded. In 2017, globally pregnant women were dying daily around 810 and 295,000 in a year because of pregnancy-related problems, pointed out by World Health Organization. Identifying the cause of a death is a complex process which requires in-depth medical knowledge and practical experience. Generally, medical practitioners possess different knowledge levels, set of abilities, and problem-solving skills. Additionally, the medical negligence plays a significant part in further worsening the situation. Accurate identification of the cause of death can help a government to take strategic measures to focus on, particularly increasing the death rate in a specific region. METHODS: This research provides a solution by introducing a semantic-based verbal autopsy framework for maternal death (SVAF-MD) to identify the cause of death. The proposed framework consists of four main components as follows: (1) clinical practice guidelines, (2) knowledge collection, (3) knowledge modeling, and (4) knowledge codification. Maternal ontology for the framework is developed using Protégé knowledge editor. Resource description framework application programming interface (API) for PHP (RAP) is used as a Semantic Web toolkit along with Simple Protocol and RDF Query Language (SPARQL) is used for querying with ontology to retrieve data. RESULTS: The results show that 92% of maternal causes of deaths assigned using SVAF-MD correctly matched manual reports already prepared by gynecologists. CONCLUSION: SVAF-MD, a semantic-based framework for the verbal autopsy of maternal deaths, assigns the cause of death with minimum involvement of medical practitioners. This research helps the government to ease down the verbal autopsy process, overcome the delays in reporting, and facilitate in terms of accurate results to devise the policies to reduce the maternal mortality.

A Health Informatics Reporting System for Technology Illiterate Workforce Using Mobile Phone.

BACKGROUND: The reduction and control over neonatal, infant, and maternal mortality is a collective mission of the World Health Organization under United Nations. METHODS: This article summarizes the automation of verbal autopsy reporting for neonatal, infant, and maternal mortality with primary focus on user-centered design for technologically illiterate workforce with minimum available resources. The diminution in neonatal, infant, and maternal deaths is not possible until grassroot level quality data are available for mortality. The estimated data are less effective for developing countries like Pakistan because it has heterogeneous demographic pockets with respect to mortality causes. The Neonatal, Infant, and Maternal Death E-surveillance System is a project in which a real-time reporting system is innovated that is useful in detecting the causes of mortality and effective in adopting appropriate countermeasure policies. In a pilot study, the system was implemented initially in nine districts of Punjab, Pakistan. The initial system was refined after getting detailed feedback from district management staff including Lady Health Workers and Lady Health Supervisors. The refined surveillance system was finally implemented in all 36 districts of Punjab, Pakistan. RESULTS: The results exhibited 31% improvement in infant data collection and 6% improvement in maternal data collection regarding mortality. CONCLUSION: This research will be helpful in achieving the milestone of gathering real-time mortality data from grassroot level using user-centered design methodology.

An evolutionary approach for image segmentation.

The paper explores the use of evolutionary techniques in dealing with the image segmentation problem. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A genetic algorithm that uses a fitness function based on an extension of the normalized cut criterion is proposed. The algorithm employs the locus-based representation of individuals, which allows for the partitioning of images without setting the number of segments beforehand. A new concept of nearest neighbor that takes into account not only the spatial location of a pixel, but also the affinity with the other pixels contained in the neighborhood, is also defined. Experimental results show that our approach is able to segment images in a number of regions that conform well to human visual perception. The visual perceptiveness is substantiated by objective evaluation methods based on uniformity of pixels inside a region, and comparison with ground-truth segmentations available for part of the used test images.