A machine learning tool for identifying patients with newly diagnosed diabetes in primary care.

Abstract

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.
• Marcelina Wierzbicka
  Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden.
• Karolina Sigurdsson
  Department of Emergency and Internal Medicine, Skånes University Hospital, Malmö, Sweden; Department of Clinical Sciences Malmö, Lund University & Department of Internal Medicine, Skåne, Sweden.
• Johan Ärnlöv
  Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Huddinge, Sweden; School of Health and Social Studies, Dalarna University, Falun, Sweden.
• 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.

Keywords

External Resources