  Machine learning (ML) has demonstrated promising results in the
identification of clinical markers for Acute Coronary Syndrome (ACS) from
electronic health records (EHR). In the past, the ACS was perceived as a health
problem mainly for men and women were under-represented in clinical trials,
which led to both sexes receiving the same clinical attention. Although some
approaches have emphasized the importance of distinguishing markers, these
distinctions remain unclear. This study aims at exploiting ML methods for
identifying in-hospital mortality markers by sex and age-group for patients
with ST-elevation myocardial infarction (STEMI) and the Non-ST-elevation
myocardial infarction (NSTEMI) from EHR. From the MIMIC-III database, we
extracted 1,299 patients with STEMI and 2,820 patients with NSTEMI. We trained
and validated mortality prediction models with different hyperparameters,
clinical sets, and ML methods. Using the best performing model and a
game-theoretic approach to interpret predictions, we identified risk markers
for patients with STEMI and NSTEMI separately. The models based on Extreme
Gradient Boosting achieved the highest performance: AUC=0.92 (95\%
CI:0.87-0.98) for STEMI and AUC=0.87 (95\% CI:0.80-0.93) for NSTEMI. For STEMI,
the top markers for both sexes are the presence of hyponatremia, and metabolic
acidosis. More specific markers for women are acute kidney failure, and age>75
years, while for men are chronic kidney failure, and age>70 years. In contrast,
for NSTEMI, the top markers for both sexes are advanced age, and intubation
procedures. The specific markers for women are low creatinine levels and age>60
years, whilst, for men are damage to the left atrium and age>70 years. We
consider that distinguishing markers for sexes could lead to more appropriate
treatment strategies, thus improving clinical outcomes.
