Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Risk markers by sex for in-hospital mortality in patients with acute
  coronary syndrome: a machine learning approach   Background: Several studies have highlighted the importance of considering
sex differences in the diagnosis and treatment of Acute Coronary Syndrome
(ACS). However, the identification of sex-specific risk markers in ACS
sub-populations has been scarcely studied. The present study aims to explore
machine learning (ML) models to identify in-hospital mortality markers for
women and men in ACS sub-populations collected from a public database of
electronic health records (EHR). Methods: We extracted 1,299 patients with
ST-elevation myocardial infarction (STEMI) and 2,820 patients with
non-ST-elevation myocardial infarction (NSTEMI) from the Medical Information
Mart for Intensive Care (MIMIC)-III database. We trained and validated
mortality prediction models and used an interpretability technique to identify
sex-specific markers for each sub-population. Results: The models based on
eXtreme Gradient Boosting (XGBoost) achieved the highest performance: area
under the curve (AUC) = 0.94 (95\% CI:0.84-0.96) for STEMI and AUC = 0.94 (95\%
CI:0.80-0.90) for NSTEMI. For STEMI, the top markers in women are chronic
kidney failure, high heart rate, and age over 70 years. For men, the top
markers are acute kidney failure, high troponin T levels, and age over 75
years. However, for NSTEMI, the top markers in women are low troponin levels,
high urea levels, and age over 80 years. For men, the top markers are high
heart rate, creatinine levels, and age over 70 years. Conclusions: Our results
show possible significant and coherent sex-specific risk markers of different
ACS sub-populations by interpreting ML mortality models trained on EHRs.
Differences are observed in the identified risk markers between women and men,
highlighting the importance of considering sex-specific markers in implementing
more appropriate treatment strategies and better clinical outcomes.

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The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph