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Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce…

Machine Learning · Computer Science 2018-11-12 Yikuan Li , Liang Yao , Chengsheng Mao , Anand Srivastava , Xiaoqian Jiang , Yuan Luo

Acute kidney injury (AKI) is a common and serious complication after a surgery which is associated with morbidity and mortality. The majority of existing perioperative AKI risk score prediction models are limited in their generalizability…

Computers and Society · Computer Science 2019-06-19 Lasith Adhikari , Tezcan Ozrazgat-Baslanti , Paul Thottakkara , Ashkan Ebadi , Amir Motaei , Parisa Rashidi , Xiaolin Li , Azra Bihorac

Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney damage. Methods: This retrospective cohort study includes…

Background: Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality, but tailored risk prediction tools are lacking. Early identification of high-risk individuals is crucial for clinical…

Machine Learning · Computer Science 2025-07-28 Yong Si , Junyi Fan , Li Sun , Shuheng Chen , Minoo Ahmadi , Elham Pishgar , Kamiar Alaei , Greg Placencia , Maryam Pishgar

Background: Cirrhosis is a progressive liver disease with high mortality and frequent complications, notably acute kidney injury (AKI), which occurs in up to 50% of hospitalized patients and worsens outcomes. AKI stems from complex…

Machine Learning · Computer Science 2025-08-15 Li Sun , Shuheng Chen , Junyi Fan , Yong Si , Minoo Ahmadi , Elham Pishgar , Kamiar Alaei , Maryam Pishgar

Acute kidney injury (AKI) commonly occurs in hospitalized patients and can lead to serious medical complications. In order to optimally predict AKI before it develops at any time during a hospital stay, we present a novel framework in which…

Quantitative Methods · Quantitative Biology 2019-02-28 Rohit J. Kate , Noah Pearce , Debesh Mazumdar , Vani Nilakantan

Objectives: We aim to quantify longitudinal acute kidney injury (AKI) trajectories and to describe transitions through progressing and recovery states and outcomes among hospitalized patients using multistate models. Methods: In this large,…

Quantitative Methods · Quantitative Biology 2023-03-13 Esra Adiyeke , Yuanfang Ren , Ziyuan Guan , Matthew M. Ruppert , Parisa Rashidi , Azra Bihorac , Tezcan Ozrazgat-Baslanti

Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major one being acute kidney injury (AKI). For septic patients, approximately 50% develop AKI,…

Machine Learning · Computer Science 2024-12-06 Aleyeh Roknaldin , Zehao Zhang , Jiayuan Xu , Kamiar Alaei , Maryam Pishgar

In critical care, intensivists are required to continuously monitor high dimensional vital signs and lab measurements to detect and diagnose acute patient conditions. This has always been a challenging task. In this study, we propose a…

Machine Learning · Computer Science 2019-01-15 Ziyuan Pan , Hao Du , Kee Yuan Ngiam , Fei Wang , Ping Shum , Mengling Feng

The incidence of Acute Kidney Injury (AKI) commonly happens in the Intensive Care Unit (ICU) patients, especially in the adults, which is an independent risk factor affecting short-term and long-term mortality. Though researchers in recent…

Machine Learning · Computer Science 2020-05-28 Yu Wang , JunPeng Bao , JianQiang Du , YongFeng Li

Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes preventable, there is great interest in prediction. Most existing…

Machine Learning · Computer Science 2019-03-06 Samuel J. Weisenthal , Caroline Quill , Samir Farooq , Henry Kautz , Martin S. Zand

Background: Acute kidney injury (AKI) is a clinical syndrome affecting almost one fifth of hospitalized patients, as well as more than half of the patients who are admitted to the intensive care unit (ICU). Stratifying AKI patients into…

Patients with acute kidney injury (AKI) are at high risk of developing chronic kidney disease (CKD), but identifying those at greatest risk remains challenging. We used electronic health record (EHR) data to dynamically track AKI patients'…

Computation and Language · Computer Science 2026-04-13 Yilu Fang , Jordan G. Nestor , Casey N. Ta , Jerard Z. Kneifati-Hayek , Chunhua Weng

Acute kidney injury (AKI) is a serious clinical condition that affects up to 20% of hospitalised patients. AKI is associated with short term unplanned hospital readmission and post-discharge mortality risk. Patient risk and healthcare…

Machine Learning · Computer Science 2024-10-24 Flavio S. Correa da Silva , Simon Sawhney

Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early…

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in…

Machine Learning · Computer Science 2024-01-10 Gabriel D. M. Manalu , Mulomba Mukendi Christian , Songhee You , Hyebong Choi

Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict…

Machine Learning · Computer Science 2025-05-20 Shuheng Chen , Junyi Fan , Elham Pishgar , Kamiar Alaei , Greg Placencia , Maryam Pishgar

Hyperkalemia is a life-threatening electrolyte disorder that is common in patients with chronic kidney disease and heart failure, yet frequent monitoring remains difficult outside hospital settings. We developed and validated Pocket-K, a…

Hypertension and atrial fibrillation (AF) often coexist in critically ill patients, significantly increasing mortality rates in the ICU. Early identification of high-risk individuals is crucial for targeted interventions. However, limited…

Applications · Statistics 2025-06-19 Shuheng Chen , Yong Si , Junyi Fan , Li Sun , Greg Placencia , Elham Pishgar , Kamiar Alaei , Maryam Pishgar

Heart attack remain one of the greatest contributors to mortality in the United States and globally. Patients admitted to the intensive care unit (ICU) with diagnosed heart attack (myocardial infarction or MI) are at higher risk of death.…

Machine Learning · Computer Science 2023-05-11 Munib Mesinovic , Peter Watkinson , Tingting Zhu
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