Back

Development and Validation of Machine Learning Models for Adverse Events after Cardiac Surgery

2025-02-25 surgery Title + abstract only
View on medRxiv
Show abstract

ImportanceEarly recognition of adverse events after cardiac surgery is vital for treatment. However, the widely used Society of Thoracic Surgery (STS) risk model has modest performance in predicting adverse events and only applies <80% of cardiac surgeries. ObjectiveTo develop and validate machine learning (ML) models for predicting outcomes after cardiac surgery. Design, setting, and participantsML models, referred as Roux-MMC model, were developed and validated using a retrospective cohort e...

Predicted journal destinations

1
PLOS ONE
1737 training papers
Top 19% 25.8%
2
Scientific Reports
701 training papers
Top 20% 9.1%
3
npj Digital Medicine
85 training papers
Top 1% 9.1%
4
BMJ Open
553 training papers
Top 16% 9.1%
5
Journal of Clinical Medicine
77 training papers
Top 3% 4.5%
6
BMC Medical Informatics and Decision Making
36 training papers
Top 4% 3.2%
7
Journal of the American Heart Association
92 training papers
Top 8% 3.2%
8
JAMA Network Open
125 training papers
Top 7% 2.3%
9
Biology Methods and Protocols
19 training papers
#1 1.8%
10
Frontiers in Cardiovascular Medicine
33 training papers
Top 5% 1.8%
11
PLOS Digital Health
88 training papers
Top 11% 1.6%
12
Frontiers in Medicine
99 training papers
Top 11% 1.6%
13
JMIR Medical Informatics
16 training papers
Top 4% 1.4%
14
Heart Rhythm
16 training papers
Top 4% 1.3%
15
eLife
262 training papers
Top 39% 1.2%
16
Journal of Medical Internet Research
81 training papers
Top 18% 0.8%
17
Heliyon
57 training papers
Top 11% 0.8%
18
Trials
24 training papers
Top 3% 0.8%
19
Human Brain Mapping
53 training papers
Top 12% 0.5%
20
PLOS Computational Biology
141 training papers
Top 16% 0.5%
21
Cureus
64 training papers
Top 27% 0.5%
22
Computers in Biology and Medicine
39 training papers
Top 11% 0.5%
23
eBioMedicine
82 training papers
Top 17% 0.5%
24
Medicine
29 training papers
Top 8% 0.5%
25
Nature Communications
483 training papers
Top 64% 0.5%