An integrated deep learning and natural language processing approach for continuous remote monitoring in digital health

Shastry, K. Aditya; Shastry, Aravind

doi:10.1016/j.dajour.2023.100301

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…In addition, it appeared that deep learning approaches [44] are rapidly evolving in healthcare and are being used to analyze and interpret complex medical data in various health domains. New use cases are constantly being explored, and deep learning algorithms are applied in various fields such as the NLP healthcare sector, disease diagnosis and management, telemedicine, genomics and precision medicine and medical imaging [56,57].…”

Section: Discussionmentioning

confidence: 99%

“…• The development of anomaly detection algorithms in health and medicine is crucial for identifying deviations from normal patterns, aiding in the early diagnosis of health conditions. More specifically, it helps in identifying outliers in physiological signals, abnormal heart rate variability, and unusual patterns in patient data, such as changes in speech or language [56].…”

Section: Machine Learning Sectors In Evidence-based Telehealth and Sm...mentioning

confidence: 99%

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Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Christopoulou

2024

BioMedInformatics

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Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, and managing health issues like pulmonary diseases, heart failure, diabetes screening, and intraoperative risks. However, a systematic review of machine learning’s use in evidence-based telehealth and smart care is lacking, as evidence-based practice aims to eliminate biases and subjective opinions. Methods: The author conducted a mixed methods review to explore machine learning applications in evidence-based telehealth and smart care. A systematic search of the literature was performed during 16 June 2023–27 June 2023 in Google Scholar, PubMed, and the clinical registry platform ClinicalTrials.gov. The author included articles in the review if they were implemented by evidence-based health informatics and concerned with telehealth and smart care technologies. Results: The author identifies 18 key studies (17 clinical trials) from 175 citations found in internet databases and categorizes them using problem-specific groupings, medical/health domains, machine learning models, algorithms, and techniques. Conclusions: Machine learning combined with the application of evidence-based practices in healthcare can enhance telehealth and smart care strategies by improving quality of personalized care, early detection of health-related problems, patient quality of life, patient-physician communication, resource efficiency and cost-effectiveness. However, this requires interdisciplinary expertise and collaboration among stakeholders, including clinicians, informaticians, and policymakers. Therefore, further research using clinicall studies, systematic reviews, analyses, and meta-analyses is required to fully exploit the potential of machine learning in this area.

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learning Sectors In Evidence-based Telehealth and Sm...mentioning

confidence: 99%

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Christopoulou

2024

BioMedInformatics

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“…mHealth platforms enable healthcare providers to collect and analyze feedback from patients in real-time, allowing for the identification of emerging health trends, concerns, and issues. By leveraging data analytics and machine learning algorithms, healthcare providers can detect patterns and anomalies in patient-reported data, enabling timely interventions and proactive management of population health (Shastry and Shastry, 2023). Below is the schematic diagram of proposed approach of both spatial and temporal information to achieve improved classification performance.…”

Section: Role Of Mhealth In Public Health Feedbackmentioning

confidence: 99%

Mobile Health (Mhealth) Innovations for Public Health Feedback: A Global Perspective

Chioma Anthonia Okolo,

Oloruntoba Babawarun,

Tolulope Oyinlola Olorunsogo

2024

imsrj

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In recent years, Mobile Health (mHealth) innovations have emerged as a transformative force in the realm of public health, revolutionizing the way healthcare is delivered and monitored worldwide. This review presents a comprehensive overview of the latest advancements in mHealth technologies, focusing particularly on their role in soliciting and utilizing public health feedback on a global scale. The proliferation of smartphones and mobile applications has paved the way for novel approaches to healthcare delivery, monitoring, and data collection. Through leveraging the ubiquity and connectivity of mobile devices, mHealth initiatives have facilitated enhanced communication between healthcare providers and patients, enabling real-time monitoring of health metrics, adherence to treatment regimens, and timely interventions. One of the key aspects of mHealth innovations is their capacity to engage diverse populations and solicit feedback regarding various aspects of public health, ranging from disease outbreaks to healthcare service quality. Through interactive platforms and user-friendly interfaces, individuals can provide valuable insights, report symptoms, and participate in surveys, thereby contributing to the generation of actionable data for public health interventions. Furthermore, mHealth solutions have demonstrated significant promise in overcoming barriers to healthcare access, particularly in underserved and remote communities. By delivering health information, diagnostic tools, and remote consultations via mobile platforms, these innovations have expanded the reach of essential healthcare services, thereby bridging gaps in healthcare delivery and improving health outcomes on a global scale. However, challenges such as privacy concerns, technological literacy, and disparities in digital access persist, underscoring the importance of equitable deployment and user-centered design in mHealth initiatives. Moreover, the integration of mHealth solutions into existing healthcare infrastructure requires careful coordination among stakeholders, including policymakers, healthcare providers, and technology developers. Mobile Health (mHealth) innovations hold immense potential to revolutionize public health feedback mechanisms on a global scale. By harnessing the power of mobile technologies, these initiatives can empower individuals, enhance healthcare delivery, and inform evidence-based public health policies for a healthier future. Keywords: Mobile Health, mHealth, Public Health, Healthcare, Innovation, Review.

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Analyzing Clinical Data for Improved Diagnosis Using AI/ML Algorithms in Smart Health Care

Lakshmisha,

Alam,

Bhuvana

et al. 2024

Lecture Notes in Electrical Engineering

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An integrated deep learning and natural language processing approach for continuous remote monitoring in digital health

Cited by 12 publications

References 31 publications

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Mobile Health (Mhealth) Innovations for Public Health Feedback: A Global Perspective

Analyzing Clinical Data for Improved Diagnosis Using AI/ML Algorithms in Smart Health Care

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