Search results for: thoughtful intelligence

Authors: Sitira Williams, Georgina Cherry, Andrea Wright, Kevin Wells, Taran Rai, Richard Brown, Travis Street, Alasdair Cook

Abstract:

Social media sources (50) were identified, keywords defined by veterinarians and organised into 6 topics known to be indicative of feline pruritus: body areas, behaviors, symptoms, diagnosis, and treatments. These were augmented using academic literature, a cat owner survey, synonyms, and Google Trends. The content was collected using a social intelligence solution, with keywords tagged and filtered. Data were aggregated and de-duplicated. SL content matching body areas, behaviors and symptoms were reviewed manually, and posts were marked relevant if: posted by a pet owner, identifying an itchy cat and not duplicated. A sub-set of 493 posts published from 2009-2022 was used for reflexive thematic analysis in NVIVO (Burlington, MA) to identify themes. Five themes were identified: allergy, pruritus, additional behaviors, unusual or undesirable behaviors, diagnosis, and treatment. Most (258) posts reported the cat was excessively licking, itching, and scratching. The majority were indoor cats and were less playful and friendly when itchy. Half of these posts did not indicate a known cause of pruritus. Bald spots and scabs (123) were reported, often causing swelling and fur loss, and 56 reported bumps, lumps, and dry patches. Other impacts on the cat’s quality of life were ear mites, cat self-trauma and stress. Seven posts reported their cats’ symptoms caused them ongoing anxiety and depression. Cats with food allergies to poultry (often chicken and beef) causing bald spots featured in 23 posts. Veterinarians advised switching to a raw food diet and/or changing their bowls. Some cats got worse after switching, leaving owners’ needs unmet. Allergic reactions to flea bites causing excessive itching, red spots, scabs, and fur loss were reported in 13 posts. Some (3) posts indicated allergic reactions to medication. Cats with seasonal and skin allergies, causing sneezing, scratching, headshaking, watery eyes, and nasal discharge, were reported 17 times. Eighty-five posts identified additional behaviors. Of these, 13 reported their cat’s burst pimple or insect bite. Common behaviors were headshaking, rubbing, pawing at their ears, and aggressively chewing. In some cases, bites or pimples triggered previously unseen itchiness, making the cat irritable. Twenty-four reported their cat had anxiety: overgrooming, itching, losing fur, hiding, freaking out, breathing quickly, sleeplessness, hissing and vocalising. Most reported these cats as having itchy skin, fleas, and bumps. Cats were commonly diagnosed with an ear infection, ringworm, acne, or kidney disease. Acne was diagnosed in cats with an allergy flare-up or overgrooming. Ear infections were diagnosed in itchy cats with mites or other parasites. Of the treatments mentioned, steroids were most frequently used, then anti-parasitics, including flea treatments and oral medication (steroids, antibiotics). Forty-six posts reported distress following poor outcomes after medication or additional vet consultations. SL provides veterinarians with unique insights. Verbatim comments highlight the detrimental effects of pruritus on pets and owner quality of life. This study demonstrates the need for veterinarians to communicate management and treatment options more effectively to relieve owner frustrations. Data analysis could be scaled up using machine learning for topic modeling.

Keywords: content analysis, feline, itch, pruritus, social media, thematic analysis, veterinary dermatology

Procedia PDF Downloads 186

Authors: Ramazan Bakir, Gizem Kayar

Abstract:

It is known that Alzheimer and Dementia are the two most common types of Neurodegenerative diseases and their visibility is getting accelerated for the last couple of years. As the population sees older ages all over the world, researchers expect to see the rate of this acceleration much higher. However, unfortunately, there is no known pharmacological cure for both, although some help to reduce the rate of cognitive decline speed. This is why we encounter with non-pharmacological treatment and tracking methods more for the last five years. Many researchers, including well-known associations and hospitals, lean towards using non-pharmacological methods to support cognitive function and improve the patient’s life quality. As the dementia symptoms related to mind, learning, memory, speaking, problem-solving, social abilities and daily activities gradually worsen over the years, many researchers know that cognitive support should start from the very beginning of the symptoms in order to slow down the decline. At this point, life of a patient and caregiver can be improved with some daily activities and applications. These activities include but not limited to basic word puzzles, daily cleaning activities, taking notes. Later, these activities and their results should be observed carefully and it is only possible during patient/caregiver and M.D. in-person meetings in hospitals. These meetings can be quite time-consuming, exhausting and financially ineffective for hospitals, medical doctors, caregivers and especially for patients. On the other hand, digital support systems are showing positive results for all stakeholders of healthcare systems. This can be observed in countries that started Telemedicine systems. The biggest potential of our system is setting the inter-user communication up in the best possible way. In our project, we propose Machine Learning based digitalization of validated traditional cognitive tests (e.g. MOCA, Afazi, left-right hemisphere), their analyses for high-quality follow-up and communication systems for all stakeholders. R. Bakir and G. Kayar are with Gefeasoft, Inc, R&D – Software Development and Health Technologies company. Emails: ramazan, gizem @ gefeasoft.com This platform has a high potential not only for patient tracking but also for making all stakeholders feel safe through all stages. As the registered hospitals assign corresponding medical doctors to the system, these MDs are able to register their own patients and assign special tasks for each patient. With our integrated machine learning support, MDs are able to track the failure and success rates of each patient and also see general averages among similarly progressed patients. In addition, our platform also supports multi-player technology which helps patients play with their caregivers so that they feel much safer at any point they are uncomfortable. By also gamifying the daily household activities, the patients will be able to repeat their social tasks and we will provide non-pharmacological reminiscence therapy (RT – life review therapy). All collected data will be mined by our data scientists and analyzed meaningfully. In addition, we will also add gamification modules for caregivers based on Naomi Feil’s Validation Therapy. Both are behaving positively to the patient and keeping yourself mentally healthy is important for caregivers. We aim to provide a therapy system based on gamification for them, too. When this project accomplishes all the above-written tasks, patients will have the chance to do many tasks at home remotely and MDs will be able to follow them up very effectively. We propose a complete platform and the whole project is both time and cost-effective for supporting all stakeholders.

Keywords: alzheimer’s, dementia, cognitive functionality, cognitive tests, serious games, machine learning, artificial intelligence, digitalization, non-pharmacological, data analysis, telemedicine, e-health, health-tech, gamification

Procedia PDF Downloads 133

Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

Procedia PDF Downloads 79