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Table 1 Variables and their assignments

From: A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections

Factors

Abbreviations

Assignments/units

Age

Age(X1)

continuous variable, year

Weight

Weight(X2)

continuous variable, Kg

Estimated glomerular filtration rate

EGFR(X3)

continuous variable, ml/min

Blood urea nitrogen

BUN(X4)

continuous variable, mmol/L

Serum Albumin

ALB(X5)

continuous variable, g/L

White Blood Cell Count

WBC(X6)

continuous variable,×109/L

D-dimer

DD(X7)

continuous variable, mg/L

C-Reactive Protein

CRP(X8)

continuous variable, mg/L

Procalcitonin

PCT(X9)

continuous variable, ng/ml

Neutrophil-to-Lymphocyte Ratio

NLR(X10)

continuous variable, dimensionless

Lymphocyte count

LYM(X11)

continuous variable, ×109/L

Body Temperature

Temp(X12)

>=37.3 °C = 1; <37.3 °C = 0

Sex

Sex(X13)

Female = 0; Male = 1

Respiratory Failure

RF(X14)

YES = 1; NO = 0

Recent hospitalization history(in 1 month)

RH(X15)

YES = 1; NO = 0

Diabetes Mellitus

DM(X16)

YES = 1; NO = 0

Active tumor

CA(X17)

YES = 1; NO = 0

Chronic Obstructive Pulmonary Disease

COPD(X18)

YES = 1; NO = 1

Bronchiectasis

BE(X19)

YES = 1; NO = 0

Interstitial lung disease

ILD(X20)

YES = 1; NO = 0

Cerebrovascular disease

CVD(X21)

YES = 1; NO = 0

Heart Failure

HF(X22)

YES = 1; NO = 0

Outpatient treatment history before admission

OPTH (X23)

YES = 1; NO = 0

Dosage of Piperacillin/Tazobactam

TZPD(X24)

2.5 g, Q8H = 1; 3.125 g, Q8H = 0

Combining erythromycin

Ery(X25)

YES = 1; NO = 0

Combining Nemonoxacin

NXC(X26)

YES = 1; NO = 0

Clinical outcomes (Result)

Failure = 1; Success = 0