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Electromagnetic Fields Literature Analysis for Precision Medicine.

Studies in health technology and informatics · 2023 · Unclear · Insufficient evidence

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UnclearInsufficient evidencePaper 5794

Text-mining of more than 30,000 EMF-related publications identified 3,653 unique disease MeSH terms and 9,966 unique genes associated with exposure to six different subsets of EMFs; 4,340 of the genes were human.

Study snapshot

PublicationStudies in health technology and informatics · 2023
Study typeother
Reported directionUnclear
Evidence ratingInsufficient

Abstract

During the last century technological advances have increased the number of anthropogenic electromagnetic fields (EMFs) and therefore human exposures. In this work we have mined from more than 30,000 EMF-related publications the genes, diseases and molecular mechanisms associated with the exposure to six different subsets of EMFs. Results show 3653 unique disease MeSH terms and 9966 unique genes identified of which only 4340 genes are human. Overall, our approach highlights the molecular aspects of the increasing exposure to EMFs.

Machine Enhanced Logic

AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeother
Effect directionunclear
PopulationNot supplied
Sample sizeNot supplied
ExposureNot supplied
Evidence strengthInsufficientConfidence: 74% · Peer reviewed: yes

Main findings

Text-mining of more than 30,000 EMF-related publications identified 3,653 unique disease MeSH terms and 9,966 unique genes associated with exposure to six different subsets of EMFs; 4,340 of the genes were human.

Outcomes measured

  • genes
  • diseases (MeSH terms)
  • molecular mechanisms

Limitations

  • No specific EMF sources, frequencies, or exposure metrics are described in the abstract.
  • Findings are based on literature mining rather than direct measurement or experimental/epidemiologic assessment.
View raw extracted JSON
{
  "study_type": "other",
  "exposure": {
    "band": null,
    "source": null,
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": null
  },
  "population": null,
  "sample_size": null,
  "outcomes": [
    "genes",
    "diseases (MeSH terms)",
    "molecular mechanisms"
  ],
  "main_findings": "Text-mining of more than 30,000 EMF-related publications identified 3,653 unique disease MeSH terms and 9,966 unique genes associated with exposure to six different subsets of EMFs; 4,340 of the genes were human.",
  "effect_direction": "unclear",
  "limitations": [
    "No specific EMF sources, frequencies, or exposure metrics are described in the abstract.",
    "Findings are based on literature mining rather than direct measurement or experimental/epidemiologic assessment."
  ],
  "evidence_strength": "insufficient",
  "confidence": 0.74,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "electromagnetic fields",
    "EMF",
    "literature mining",
    "text mining",
    "genes",
    "diseases",
    "MeSH",
    "molecular mechanisms",
    "precision medicine"
  ],
  "suggested_hubs": []
}
AI can be wrong. Always verify against the paper.

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

Identifiers and source

DOI: 10.3233/shti230349
PubMed: 37203583

Read the original paper →

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