BMC Medical Informatics and Decision Making

Table 1 Clinical characteristics of heart failure patients included in the model

From: Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data

	Features	Units	Positive	Negative
Demographics	Ages	years	53(14)	58 (14)
Demographics	Male Gender	%	81.3	70.3
Vital signs	Systolic blood pressure	mmHg	\(102.74 \pm 14.95\)	\(121.68 \pm 22.74\)
Vital signs	Heart rate	bpm	\(86.09\pm 18.85\)	\(86.15\pm 16.68\)
Lab metabolites	Chloride	mmol/L	\(100.62\pm 4.88\)	\(101.60\pm 5.79\)
Lab metabolites	Sodium	mmol/L	\(137.48\pm 3.58\)	\(138.06\pm 4.35\)
Cormorbidity	Diabetes	%	43.00	62.10
Cormorbidity	Hypertension	%	67.40	89.30

Clinical characteristics of patients requiring HT/LVAD evaluation (“Positive”) and those too well for HF advanced therapies (“Negative”). Displayed are the mean (standard deviation) for continuous variables and N (%) for comorbidities

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ISSN: 1472-6947

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