Machine Learning in Medical Emergencies: a Systematic Review and Analysis

Mendo, Inés Robles; Marques, Gonçalo; Díez, Isabel de la Torre; López-Coronado, Miguel; Martin-Rodríguez, Francisco

doi:10.1007/s10916-021-01762-3

Cited by 22 publications

(14 citation statements)

References 38 publications

(24 reference statements)

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“…All articles were from the Science Citation Index (SCI) and Social Science Citation Index (SSCI) databases. After the literature was reviewed, we obtained the related keywords with the high number of occurrences as queries with the following terms: remote technology (6,31,32); remote learning (6,31,33); online learning or teaching (7,10); emergency or disaster response (34,35); disaster medicine education (30, 36); blended learning (37)(38)(39); mobile edge computing (20, 40); virtual or augmented reality (23,25,41,42); drone (14,20,43); machine learning (44,45); deep learning (46,47); and federated learning (48,49). Similar articles included in references were also screened.…”

Section: Searching Strategies and Inclusion And Exclusion Criteriamentioning

confidence: 99%

The development of new remote technologies in disaster medicine education: A scoping review

Kao

Chien

Wang

et al. 2023

Front. Public Health

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BackgroundRemote teaching and online learning have significantly changed the responsiveness and accessibility after the COVID-19 pandemic. Disaster medicine (DM) has recently gained prominence as a critical issue due to the high frequency of worldwide disasters, especially in 2021. The new artificial intelligence (AI)-enhanced technologies and concepts have recently progressed in DM education.ObjectivesThe aim of this article is to familiarize the reader with the remote technologies that have been developed and used in DM education over the past 20 years.Literature scoping reviewsMobile edge computing (MEC), unmanned aerial vehicles (UAVs)/drones, deep learning (DL), and visual reality stimulation, e.g., head-mounted display (HMD), are selected as promising and inspiring designs in DM education.MethodsWe performed a comprehensive review of the literature on the remote technologies applied in DM pedagogy for medical, nursing, and social work, as well as other health discipline students, e.g., paramedics. Databases including PubMed (MEDLINE), ISI Web of Science (WOS), EBSCO (EBSCO Essentials), Embase (EMB), and Scopus were used. The sourced results were recorded in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart and followed in accordance with the PRISMA extension Scoping Review checklist. We included peer-reviewed articles, Epubs (electronic publications such as databases), and proceedings written in English. VOSviewer for related keywords extracted from review articles presented as a tabular summary to demonstrate their occurrence and connections among these DM education articles from 2000 to 2022.ResultsA total of 1,080 research articles on remote technologies in DM were initially reviewed. After exclusion, 64 articles were included in our review. Emergency remote teaching/learning education, remote learning, online learning/teaching, and blended learning are the most frequently used keywords. As new remote technologies used in emergencies become more advanced, DM pedagogy is facing more complex problems.DiscussionsArtificial intelligence-enhanced remote technologies promote learning incentives for medical undergraduate students or graduate professionals, but the efficacy of learning quality remains uncertain. More blended AI-modulating pedagogies in DM education could be increasingly important in the future. More sophisticated evaluation and assessment are needed to implement carefully considered designs for effective DM education.

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Section: Searching Strategies and Inclusion And Exclusion Criteriamentioning

confidence: 99%

The development of new remote technologies in disaster medicine education: A scoping review

Kao

Chien

Wang

et al. 2023

Front. Public Health

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“…Here, the machine learning model is utilized to analyze the behavior elements of the medical systems. Mendo, et al [20] provided a thorough analysis of the many machine learning models employed to raise the capability of mobile health systems. We also explored the rise of mobile healthcare devices and applications for reviewing patient health records.…”

Section: Related Workmentioning

confidence: 99%

AI based Dynamic Prediction Model for Mobile Health Application System

Ramesh¹,

Subbaraya²,

Kumar³

2023

IJACSA

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In recent decades, mobile health (m-health) applications have gained significant attention in the healthcare sector due to their increased support during critical cases like cardiac disease, spinal cord problems, and brain injuries. Also, m-health services are considered more valuable, mainly where facilities are deficient. In addition, it supports wired and advanced wireless technologies for data transmission and communication. In this work, an Artificial Intelligence (AI)based deep learning model is implemented to predict healthcare data, where the data handling is performed to improve the dynamic prediction performance. It includes the working modules of data collection, normalization, AI-based classification, and decision-making. Here, the m-health data are obtained from the smart devices through the service providers, which comprises the health information related to blood pressure, heart rate, glucose level, etc. The main contribution of this paper is to accurately predict Cardio Vascular Disease (CVD) from the patient dataset stored in cloud using the AIbased m-health system. After obtaining the data, preprocessing can be performed for noise reduction and normalization because prediction performance highly depends on data quality. Consequently, we use the Gorilla Troop Optimization Algorithm (GTOA) to select the most relevant functions for classifier training and testing. Classify his CVD type according to a selected set of features using bidirectional long-term memory (Bi-LSTM). Moreover, the proposed AI-based prediction model's performance is validated and compared using different measures.

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“…The ML approaches have recently gained popularity in medicine because of their ability to improve modelling algorithms autonomously. In particular, ML has shown promising results in medical services and medical emergencies, positively impacting areas including pre-hospital care and disease screening, clinical decisions, and mobile health [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and machine learning for hemorrhagic trauma care

et al. 2023

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Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage. A literature search was carried out on PubMed and Google scholar. Titles and abstracts were screened and, if deemed appropriate, the full articles were reviewed. We included 89 studies in the review. These studies could be grouped into five areas: (1) prediction of outcomes; (2) risk assessment and injury severity for triage; (3) prediction of transfusions; (4) detection of hemorrhage; and (5) prediction of coagulopathy. Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models. However, most studies were retrospective, focused on prediction of mortality, and development of patient outcome scoring systems. Few studies performed model assessment via test datasets obtained from different sources. Prediction models for transfusions and coagulopathy have been developed, but none is in widespread use. AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care. Comparison and application of ML algorithms using different datasets from initial training, testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.

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Machine Learning in Medical Emergencies: a Systematic Review and Analysis

Cited by 22 publications

References 38 publications

The development of new remote technologies in disaster medicine education: A scoping review

The development of new remote technologies in disaster medicine education: A scoping review

AI based Dynamic Prediction Model for Mobile Health Application System

Artificial intelligence and machine learning for hemorrhagic trauma care

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