Publications by authors named "Fateme Nateghi Haredasht"
Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, and structured data alike. These breakthroughs in AI technology have spurred a wave of research in the medical field, leading to the creation of a variety of tools aimed at improving clinical decision-making, patient monitoring, image analysis, and emergency response systems.
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- The study evaluates the effectiveness of machine learning (ML) techniques in predicting glomerular filtration rate (GFR) compared to the traditional EKFC equation, which measures kidney function.
- Using data from 19,629 patients across 13 cohorts, the researchers tested various ML methods, particularly focusing on factors like age, sex, and serum creatinine levels.
- Results indicated that the random forest (RF) method performed similarly to EKFC, with slight advantages for RF in younger patients, suggesting ML could enhance future GFR prediction methods.
Background And Aims: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors.
Design: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data.
Acute Kidney Injury (AKI) is a sudden episode of kidney failure that is frequently seen in critically ill patients. AKI has been linked to chronic kidney disease (CKD) and mortality. We developed machine learning-based prediction models to predict outcomes following AKI stage 3 events in the intensive care unit.
View Article and Find Full Text PDFBackground: Acute Kidney Injury (AKI) is frequently seen in hospitalized and critically ill patients. Studies have shown that AKI is a risk factor for the development of acute kidney disease (AKD), chronic kidney disease (CKD), and mortality.
Methods: A systematic review is performed on validated risk prediction models for developing poor renal outcomes after AKI scenarios.
Background: Acute kidney injury (AKI) in critically ill patients is associated with a significant increase in mortality as well as long-term renal dysfunction and chronic kidney disease (CKD). Serum creatinine (SCr), the most widely used biomarker to evaluate kidney function, does not always accurately predict the glomerular filtration rate (GFR), since it is affected by some non-GFR determinants such as muscle mass and recent meat ingestion. Researchers and clinicians have gained interest in cystatin C (CysC), another biomarker of kidney function.
View Article and Find Full Text PDFBackground: Due to the existence of different AKI definitions, analyzing AKI incidence and associated outcomes is challenging. We investigated the incidence of AKI events defined by 4 different definitions (standard AKIN and KDIGO, and modified AKIN-4 and KDIGO-4) and its association with in-hospital mortality.
Methods: A total of 7242 adult Greek subjects were investigated.