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To test the hypothesis that accuracy, discrimination, and precision in predicting postoperative complications improve when using both preoperative and intraoperative data input features versus preoperative data alone. Models that predict…

Despite advances in surgical techniques and care, postoperative complications are prevalent and effects up to 15% of the patients who underwent a major surgery. The objective of this study is to develop and validate models for predicting…

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: Artificial Intelligence (AI) clinical decision support (CDS) systems have the potential to augment surgical risk assessments, but successful adoption depends on an understanding of end-user needs and current workflows. This…

Human-Computer Interaction · Computer Science 2025-04-04 Andrea E Davidson , Jessica M Ray , Yulia Levites Strekalova , Parisa Rashidi , Azra Bihorac

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate…

The association between preoperative cognitive status and surgical outcomes is a critical, yet scarcely explored area of research. Linking intraoperative data with postoperative outcomes is a promising and low-cost way of evaluating…

Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the…

Machine Learning · Computer Science 2024-12-31 Junbo Shen , Bing Xue , Thomas Kannampallil , Chenyang Lu , Joanna Abraham

The widespread digitization of patient data via electronic health records (EHRs) has created an unprecedented opportunity to use machine learning algorithms to better predict disease risk at the patient level. Although predictive models…

Purpose: Subarachnoid haemorrhage is a potentially fatal consequence of intracranial aneurysm rupture, however, it is difficult to predict if aneurysms will rupture. Prophylactic treatment of an intracranial aneurysm also involves risk,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-09 Karan Daga , Siddharth Agarwal , Zaeem Moti , Matthew BK Lee , Munaib Din , David Wood , Marc Modat , Thomas C Booth

Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification…

Quantitative Methods · Quantitative Biology 2025-06-05 Tinghuan Li , Shuheng Chen , Junyi Fan , Elham Pishgar , Kamiar Alaei , Greg Placencia , Maryam Pishgar

Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures. Understanding the risk of preoperative…

Machine Learning · Computer Science 2022-05-18 Yuming Sun , Jian Kang , Chad Brummett , Yi Li

Coronary artery disease remains one of the leading causes of mortality globally. Despite advances in revascularization treatments like PCI and CABG, postoperative stroke is inevitable. This study aims to develop and evaluate a sophisticated…

Machine Learning · Computer Science 2025-03-18 Haonan Pan , Shuheng Chen , Elham Pishgar , Kamiar Alaei , Greg Placencia , Maryam Pishgar

The COVID-19 pandemic has had far-reaching implications for global public health. As we continue to grapple with its consequences, it becomes increasingly clear that post-COVID-19 complications are a significant concern. Among these…

Accurate prediction of postoperative complications can inform shared decisions regarding prognosis, preoperative risk-reduction, and postoperative resource use. We hypothesized that multi-task deep learning models would outperform random…

Accurate prediction of clinical outcomes using Electronic Health Records (EHRs) is critical for early intervention, efficient resource allocation, and improved patient care. EHRs contain multimodal data, including both structured data and…

Machine Learning · Computer Science 2025-08-29 Rituparna Datta , Jiaming Cui , Zihan Guan , Vishal G. Reddy , Joshua C. Eby , Gregory Madden , Rupesh Silwal , Anil Vullikanti

Existing prognostic tools mainly focus on predicting the risk of mortality among patients with coronavirus disease 2019. However, clinical evidence suggests that COVID-19 can result in non-mortal complications that affect patient prognosis.…

Predicting the risk of mortality for patients with acute myocardial infarction (AMI) using electronic health records (EHRs) data can help identify risky patients who might need more tailored care. In our previous work, we built…

Machine Learning · Computer Science 2019-04-30 Seyedeh Neelufar Payrovnaziri , Laura A. Barrett , Daniel Bis , Jiang Bian , Zhe He

Patients resuscitated from cardiac arrest (CA) face a high risk of neurological disability and death, however pragmatic methods are lacking for accurate and reliable prognostication. The aim of this study was to build computational models…

Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures influence downstream risk. We constructed a fixed-window EHR cohort (90-day observation,…

Machine Learning · Computer Science 2026-04-28 Kalyani P. Pande , Evan Yang , Bryan Zhu , Sandeep K. Mallipattu , Alisa Yurovsky , Tengfei Ma

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
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