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Asthma-I: Introduction01:29

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Asthma is a chronic respiratory ailment that requires careful management due to its varying symptoms and influencing factors. It is characterized by airway inflammation, bronchial hyperresponsiveness, and reversible airflow obstruction, leading to symptoms like wheezing, shortness of breath, chest tightness, and coughing. The symptom frequency and intensity may vary considerably over time. It is also linked to immune system responses to allergens and irritants, highlighting the complex...
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The nursing management of asthma is a comprehensive approach that relies heavily on the expertise and dedication of healthcare professionals. It involves thorough assessment, accurate diagnosis, strategic planning, effective implementation, and diligent evaluation. By meticulously following this step-by-step process, healthcare professionals play a crucial role in providing the best possible care and treatment for patients with asthma, enhancing their overall health and well-being.
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Asthma: Pathogenesis and Management01:20

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Asthma is a chronic pulmonary condition involving inflammation of the airways, hyper-reactivity, and reversible obstruction of the airways. This condition can significantly impact a person's quality of life, making breathing difficult and leading to distressing symptoms.
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Asthma-II: Pathophysiology and Classification01:26

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Asthma is a prevalent chronic respiratory condition marked by inflammation and hyperresponsiveness of the airways. Its pathophysiology involves complex interactions among inflammatory pathways, immune responses, and neural mechanisms.
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Asthma-III: Symptoms and Complications01:24

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Asthma, a common chronic respiratory condition, is classified considering the frequency and severity of symptoms alongside lung function impairment. Understanding this classification is essential for appropriate treatment and management. Here's a detailed look at the classification of asthma and its clinical features and complications:
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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kBot: Knowledge-enabled Personalized Chatbot for Asthma Self-Management.

Dipesh Kadariya1, Revathy Venkataramanan1, Hong Yung Yip1

  • 1Kno.e.sis - Wright State University Dayton, USA.

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Summary
This summary is machine-generated.

A new chatbot, kBot, aids pediatric asthma patients in managing their condition through personalized monitoring of medication adherence and environmental triggers. This innovative system shows high technology acceptance and usability among clinicians and researchers.

Keywords:
Chatbot for HealthcareConversational AgentIoT for Personalized HealthPatient Generated Health DataPediatric Asthma ManagementPersonalized chatbotSelf ManagementVirtual Assistant

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Area of Science:

  • Digital Health
  • Pediatric Asthma Management
  • Conversational AI in Healthcare

Background:

  • Asthma management requires continuous monitoring of adherence, triggers, and health signals, which is challenging in traditional clinical settings.
  • General-purpose conversational systems lack the personalization and contextualization needed for effective health applications.
  • There is a growing need for proactive, technology-driven solutions to improve asthma care and reduce healthcare costs.

Purpose of the Study:

  • To introduce kBot, a knowledge-enabled personalized chatbot system for pediatric asthma patients (ages 8-15).
  • To enable continuous monitoring of medication adherence, environmental triggers, and health signals.
  • To provide a hyper-personalized and interactive tool for better asthma control.

Main Methods:

  • kBot is an Android application with a voice/text chat interface and a cloud-based backend for data processing and dialogue management.
  • Contextualization is achieved through domain knowledge from online sources and clinical partner input.
  • Personalization is driven by patient questionnaires and ongoing conversations.

Main Results:

  • Preliminary evaluation involved eight asthma clinicians and eight researchers.
  • kBot achieved an overall technology acceptance value greater than 8 (11-point Likert scale).
  • The system demonstrated a mean System Usability Score (SUS) greater than 80.

Conclusions:

  • kBot shows promise as a usable and acceptable tool for enhancing pediatric asthma management.
  • The system's ability to contextualize and personalize interactions is key to its potential effectiveness.
  • Further development and evaluation are warranted to explore its impact on patient outcomes.