Machine learning and syncope management in the ed: The future is coming

Franca Dipaola, Dana Shiffer, Mauro Gatti, Roberto Menè, Monica Solbiati, Raffaello Furlan

Research output: Contribution to journalReview articlepeer-review

Abstract

In recent years, machine learning (ML) has been promisingly applied in many fields of clinical medicine, both for diagnosis and prognosis prediction. Aims of this narrative review were to summarize the basic concepts of ML applied to clinical medicine and explore its main applications in the emergency department (ED) setting, with a particular focus on syncope management. Through an extensive literature search in PubMed and Embase, we found increasing evidence suggesting that the use of ML algorithms can improve ED triage, diagnosis, and risk stratification of many diseases. However, the lacks of external validation and reliable diagnostic standards currently limit their implementation in clinical practice. Syncope represents a challenging problem for the emergency physician both because its diagnosis is not supported by specific tests and the available prognostic tools proved to be inefficient. ML algorithms have the potential to overcome these limitations and, in the future, they could support the clinician in managing syncope patients more efficiently. However, at present only few studies have addressed this issue, albeit with encouraging results.

Original languageEnglish
Article number351
JournalMedicina (Lithuania)
Volume57
Issue number4
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Artificial intelligence
  • Diagnosis
  • Emergency department
  • Risk stratification
  • Syncope

ASJC Scopus subject areas

  • Medicine(all)

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