Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning. / Zucco, Adrian G.; Agius, Rudi; Svanberg, Rebecka; Moestrup, Kasper S.; Marandi, Ramtin Z.; MacPherson, Cameron Ross; Lundgren, Jens; Ostrowski, Sisse R.; Niemann, Carsten U.
I: Scientific Reports, Bind 12, 13879, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning
AU - Zucco, Adrian G.
AU - Agius, Rudi
AU - Svanberg, Rebecka
AU - Moestrup, Kasper S.
AU - Marandi, Ramtin Z.
AU - MacPherson, Cameron Ross
AU - Lundgren, Jens
AU - Ostrowski, Sisse R.
AU - Niemann, Carsten U.
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2022
Y1 - 2022
N2 - Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.
AB - Interpretable risk assessment of SARS-CoV-2 positive patients can aid clinicians to implement precision medicine. Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,938 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. A discrete-time framework for survival modelling enabled us to predict personalized survival curves and explain individual risk factors. Performance on the test set was measured with a weighted concordance index of 0.95 and an area under the curve for precision-recall of 0.71. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors. Our explainable survival model developed on EHR data also revealed temporal dynamics of the 22 selected risk factors. Upon further validation, this model may allow direct reporting of personalized survival probabilities in routine care.
U2 - 10.1038/s41598-022-17953-y
DO - 10.1038/s41598-022-17953-y
M3 - Journal article
C2 - 35974050
AN - SCOPUS:85135990651
VL - 12
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
M1 - 13879
ER -
ID: 319804358