Machine Learning in Risk Prediction for Cardiac Surgery – An Emerging Trend?

2021 ◽  
Author(s):  
Jaishankar Raman ◽  
Svetha Venkatesh ◽  
Rinaldo Bellomo
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Risk Prediction

scholarly journals Machine learning for risk prediction of in-hospital mortality in adult cardiac surgery

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L Myslivecek ◽  
U.B Benedetto
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Cardiac Surgery ◽  
Hospital Mortality ◽  
Risk Prediction ◽  
Case Mix ◽  
Patient Case ◽  
Logistic Euroscore ◽  
Adult Cardiac Surgery

Abstract Background Risk prediction plays a central role in clinical decision-making for patients undergoing cardiac surgery. The logistic EuroSCORE has demonstrated a dangerous calibration drift with the changing patient case-mix, resulting in a significant overestimation of mortality and risk-averse practice. Despite these limitations, it continues being used in the United Kingdom due to a lack of alternative validated models. It is urgent to find a replacement for EuroSCORE with a better calibrated prediction model. Machine learning models are increasingly used for risk prediction in medicine due to their potential of overcoming limitations of regression models. Precisely quantifying the risk of in-hospital mortality may better inform patient-centred decision-making and direct targeted quality improvement interventions. Methods This is a retrospective monocentric cohort study using prospectively collected fully anonymised data from the National Adult Cardiac Surgery Audit database, restricted to patients undergoing adult cardiac surgery at our institute from 1996 to 2017 (n=28,761). The aim was to develop a predictive model with improved discriminatory power and calibration using machine learning methods. Model calibration was assessed using the calibration belt method. Discrimination power of each model (area under the receiver operating characteristic curve [AUC]) was compared with the logistic EuroSCORE using the De Long's test. Results A time series of the observed:expected (O:E) ratio for the logistic EuroSCORE showed a linear decrease with a slope of −7.4x10–3. The calibration belt showed a significant risk overestimation across all risk categories (p<0.001). Model discrimination was excellent over time, with a marginal but significant linear trend in reducing the AUC (p=0.03). Although miscalibration was detected for all models (p<0.05), neural network achieved the best calibration with a test statistic of 13.3, followed by logistic regression (18.0), and EuroSCORE (228.7). The neural network achieved the highest AUC (0.82, 95% CI 0.78–0.85) of all models and was marginally non-significantly higher than that of the logistic EuroSCORE (0.79, 95% CI 0.75–0.83, p=0.056). Conclusion Our neural network model of cardiac surgery in-hospital mortality achieves slightly improved discriminatory power and significantly better calibration compared to that of EuroSCORE, making it more appropriate for dealing with the changing patient case-mix. Further model training on larger datasets with larger demographics is necessary. Clinical implementation of such models may reduce risk of overestimation of mortality. Funding Acknowledgement Type of funding source: None

scholarly journals Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

2020 ◽  
Vol 9 (6) ◽  
pp. 1767 ◽  
Author(s):  
Charat Thongprayoon ◽  
Panupong Hansrivijit ◽  
Tarun Bathini ◽  
Saraschandra Vallabhajosyula ◽  
Poemlarp Mekraksakit ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Kidney Injury ◽  
Risk Scores ◽  
Learning Approaches ◽  
Risk Prediction Models ◽  
General Utility

Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

scholarly journals Development of machine learning models for mortality risk prediction after cardiac surgery

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Yunlong Fan ◽  
Junfeng Dong ◽  
Yuanbin Wu ◽  
Ming Shen ◽  
Siming Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Learning Models ◽  
Machine Learning Models

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

Sign in / Sign up

Export Citation Format

Share Document