Fuzzy-Based Bioengineering System for Predicting and Diagnosing Diseases of the Nervous System

PAPER manual Critical reviews in biomedical engineering 2024 Review Effect: harm Evidence: Insufficient

Read original paper → DOI: 10.1615/critrevbiomedeng.2024053240 PMID: 38884210 Evidence Lab

Abstract

Fuzzy-Based Bioengineering System for Predicting and Diagnosing Diseases of the Nervous System Triggered by the Interaction of Industrial Frequency Electromagnetic Fields Korenevskiy NA, Al-Kasasbeh RT, Krikunova EA, Rodionova SN, Shaqdan A, Al-Habahbeh OM, Filist S, Alshamasin MS, Khrisat MS, Ilyash M. Fuzzy-Based Bioengineering System for Predicting and Diagnosing Diseases of the Nervous System Triggered by the Interaction of Industrial Frequency Electromagnetic Fields. Crit Rev Biomed Eng. 2024;52(5):1-16. doi: 10.1615/CritRevBiomedEng.2024053240. Abstract The study aims to enhance the standard of medical care for individuals working in the electric power industry who are exposed to industrial frequency electromagnetic fields and other relevant risk factors. This enhancement is sought through the integration of fuzzy mathematical models with contemporary information and intellectual technologies. The study addresses the challenges of forecasting and diagnosing illnesses within a specific demographic characterized by a combination of poorly formalized issues with interconnected conditions. To tackle this complexity, a methodological framework was developed for synthesizing hybrid fuzzy decision rules. This approach combines clinical expertise with artificial intelligence methodologies to promote innovative problem-solving strategies. Additionally, the researchers devised an original method to evaluate the body's protective capacity, which was integrated into these decision rules to enhance the precision and efficacy of medical decision-making processes. The research findings indicate that industrial frequency electromagnetic fields contribute to illnesses of societal significance. Additionally, it highlights that these effects are worsened by other risk factors such as adverse microclimates, noise, vibration, chemical exposure, and psychological stress. Diseases of the neurological, immunological, cardiovascular, genitourinary, respiratory, and digestive systems are caused by these variables in conjunction with unique physical traits. The development of mathematical models in this study makes it possible to detect and diagnose disorders in workers exposed to electromagnetic fields early on, especially those pertaining to the autonomic nervous system and heart rhythm regulation. The results can be used in clinical practice to treat personnel in the electric power industry since expert evaluation and modeling showed high confidence levels in decision-making accuracy. pubmed.ncbi.nlm.nih.gov

AI evidence extraction

At a glance

Study type

Review

Effect direction

harm

Population

Workers in the electric power industry exposed to industrial frequency electromagnetic fields (and other occupational risk factors).

Sample size

—

Exposure

ELF occupational

Evidence strength

Insufficient

Confidence: 62% · Peer-reviewed: yes

Main findings

The authors report that industrial frequency electromagnetic fields contribute to illnesses of societal significance, with effects worsened by co-exposures/risk factors (adverse microclimate, noise, vibration, chemical exposure, psychological stress). They describe development of hybrid fuzzy decision rules and models intended to enable earlier detection/diagnosis of disorders in exposed workers, particularly autonomic nervous system and heart rhythm regulation issues, and state that expert evaluation/modeling showed high confidence in decision-making accuracy.

Outcomes measured

Diseases/illnesses of societal significance (general)
Neurological disorders (including autonomic nervous system)
Heart rhythm regulation disorders
Immunological diseases
Cardiovascular diseases
Genitourinary diseases
Respiratory diseases
Digestive diseases

Limitations

No sample size or study design details are provided in the abstract.
Exposure characteristics (exact frequency, intensity, duration) are not specified.
Findings are described broadly and may be confounded by multiple co-exposures (noise, vibration, chemicals, stress, microclimate) mentioned in the abstract.
No quantitative effect estimates or statistical methods/results are reported in the abstract.

Suggested hubs

occupational-exposure (0.9)
Focuses on electric power industry workers exposed to industrial frequency electromagnetic fields and other workplace risk factors.

View raw extracted JSON

{
    "study_type": "review",
    "exposure": {
        "band": "ELF",
        "source": "occupational",
        "frequency_mhz": null,
        "sar_wkg": null,
        "duration": null
    },
    "population": "Workers in the electric power industry exposed to industrial frequency electromagnetic fields (and other occupational risk factors).",
    "sample_size": null,
    "outcomes": [
        "Diseases/illnesses of societal significance (general)",
        "Neurological disorders (including autonomic nervous system)",
        "Heart rhythm regulation disorders",
        "Immunological diseases",
        "Cardiovascular diseases",
        "Genitourinary diseases",
        "Respiratory diseases",
        "Digestive diseases"
    ],
    "main_findings": "The authors report that industrial frequency electromagnetic fields contribute to illnesses of societal significance, with effects worsened by co-exposures/risk factors (adverse microclimate, noise, vibration, chemical exposure, psychological stress). They describe development of hybrid fuzzy decision rules and models intended to enable earlier detection/diagnosis of disorders in exposed workers, particularly autonomic nervous system and heart rhythm regulation issues, and state that expert evaluation/modeling showed high confidence in decision-making accuracy.",
    "effect_direction": "harm",
    "limitations": [
        "No sample size or study design details are provided in the abstract.",
        "Exposure characteristics (exact frequency, intensity, duration) are not specified.",
        "Findings are described broadly and may be confounded by multiple co-exposures (noise, vibration, chemicals, stress, microclimate) mentioned in the abstract.",
        "No quantitative effect estimates or statistical methods/results are reported in the abstract."
    ],
    "evidence_strength": "insufficient",
    "confidence": 0.61999999999999999555910790149937383830547332763671875,
    "peer_reviewed_likely": "yes",
    "keywords": [
        "industrial frequency electromagnetic fields",
        "ELF",
        "occupational exposure",
        "electric power industry",
        "fuzzy models",
        "decision rules",
        "diagnosis",
        "autonomic nervous system",
        "heart rhythm regulation",
        "risk factors"
    ],
    "suggested_hubs": [
        {
            "slug": "occupational-exposure",
            "weight": 0.90000000000000002220446049250313080847263336181640625,
            "reason": "Focuses on electric power industry workers exposed to industrial frequency electromagnetic fields and other workplace risk factors."
        }
    ]
}

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AI-extracted fields are generated from the abstract/metadata and may be incomplete or incorrect. This content is for informational purposes only and is not medical advice.

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