Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Predictive modeling using artificial intelligence and machine learning algorithms on electronic health record data: advantages and challenges
MJ Patton, VX Liu - Critical Care Clinics, 2023 - criticalcare.theclinics.com
Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic
Health Record Data - Critical Care Clinics Skip to Main Content Skip to Main Menu …
Health Record Data - Critical Care Clinics Skip to Main Content Skip to Main Menu …
Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods
Abstract Background Clinical prediction models (CPMs) predict the risk of health outcomes
for individual patients. The majority of existing CPMs only harness cross-sectional patient …
for individual patients. The majority of existing CPMs only harness cross-sectional patient …
Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study
Introduction Mortality and morbidity following surgery are pressing public health concerns in
the USA. Traditional prediction models for postoperative adverse outcomes demonstrate …
the USA. Traditional prediction models for postoperative adverse outcomes demonstrate …
Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis
Background The incorporation of repeated measurements into multivariable prediction
research may greatly enhance predictive performance. However, the methodological …
research may greatly enhance predictive performance. However, the methodological …
Protocol for the effectiveness of an anesthesiology control tower system in improving perioperative quality metrics and clinical outcomes: the TECTONICS randomized …
Introduction: Perioperative morbidity is a public health priority, and surgical volume is
increasing rapidly. With advances in technology, there is an opportunity to research the …
increasing rapidly. With advances in technology, there is an opportunity to research the …
Machine learning methods for septic shock prediction
Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated
body response to infection. Sepsis is difficult to detect at an early stage, and when not …
body response to infection. Sepsis is difficult to detect at an early stage, and when not …
Detection of outlying patterns from sparse and irregularly sampled electronic health records data
X Wang, C Li, H Shi, C Wu, C Liu - Engineering Applications of Artificial …, 2023 - Elsevier
Within the intensive care unit (ICU), vital signs such as arterial blood pressure (ABP)
collected from electronic health records (EHRs) are typically recorded at different and …
collected from electronic health records (EHRs) are typically recorded at different and …
Dynamic prediction of hospital admission with medical claim data
Background Congestive heart failure is one of the most common reasons those aged 65 and
over are hospitalized in the United States, which has caused a considerable economic …
over are hospitalized in the United States, which has caused a considerable economic …
Time Associated Meta Learning for Clinical Prediction
Rich Electronic Health Records (EHR), have created opportunities to improve clinical
processes using machine learning methods. Prediction of the same patient events at …
processes using machine learning methods. Prediction of the same patient events at …
[KNYGA][B] Learning with Scalability and Compactness
W Chen - 2016 - search.proquest.com
Artificial Intelligence has been thriving for decades since its birth. Traditional AI features
heuristic search and planning, providing good strategy for tasks that are inherently search …
heuristic search and planning, providing good strategy for tasks that are inherently search …