Predicting intensive care need in women with preeclampsia using machine learning – a pilot study

Camilla Edvinssona Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden;b Division of Anaesthesia and Intensive Care, Department of Clinical Sciences Lund, Lund University, Lund, Sweden;c Department of Anaesthesia and Intensive Care, Helsingborg Hospital, Helsingborg, SwedenCorrespondence[email protected]
View further author information

Ola Björnssond Division of Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Lund, Sweden;e Department of Energy Sciences, Faculty of Engineering, Lund University, Lund, SwedenView further author information

Lena Erlandssona Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, SwedenView further author information

Stefan R. Hanssona Division of Obstetrics and Gynecology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden;f Department of Obstetrics and Gynecology, Skåne University Hospital, Lund/Malmö, Sweden

https://orcid.org/0000-0002-7838-9074 View further author information

Predicting intensive care need in women with preeclampsia using machine learning – a pilot study

Information for

Open access

Opportunities

Help and information

Your download is now in progress and you may close this window

Login or register to access this feature

Predicting intensive care need in women with preeclampsia using machine learning – a pilot study

Reprints and Permissions

Related research

To cite this article:

Download citation

Information for

Open access

Opportunities

Help and information

Keep up to date