Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the
utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and …

Importance Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among
hospitalized patients. Personalized risk estimation and risk factor identification may allow …

Predicting future blood glucose (BG) levels for diabetic patients will help them avoid
potentially critical health issues. We demonstrate the use of machine learning models to …

Kidney diseases form part of the major health burdens experienced all over the world.
Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great …

Further research is required to overcome barriers to the validation and implementation of ML
models for AKI care. Simpler electronic systems within the electronic medical record can …

Breast cancer is the most common malignancy diagnosed in women worldwide. The
prevalence and incidence of breast cancer is increasing every year; therefore, early …

Background One of the most prevalent complications of Partial Nephrectomy (PN) is Acute
Kidney Injury (AKI), which could have a negative impact on subsequent renal function and …

Background and objectives Early identification of risk factors and timely intervention can
reduce the occurrence of cognitive frailty in elderly patients with multimorbidity and improve …

Recurrent Neural Networks (RNNs) are often used for sequential modeling of adverse
outcomes in electronic health records (EHRs) due to their ability to encode past clinical …

Results A total of 6093 patients (2442 in training and 3651 in external validation) were
included in the final cohort. Of the different modeling approaches used, XGBoost without …