A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.

Journal: ESC heart failure
PMID: 39428319

Abstract

AIMS: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals with diabetes in primary health care (PHC) through the analysis of diagnostic data.

Authors

  • Per Wändell
    Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden.
  • Axel C Carlsson
    Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden; Academic Primary Health Care Centre, Region Stockholm, Stockholm, Sweden. Electronic address: axel.carlsson@ki.se.
  • Julia Eriksson
    Division of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
  • Caroline Wachtler
    Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden.
  • Toralph Ruge
    Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden.

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