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 A systematic review of risk prediction in adult cardiac surgery: considerations for future model development

2013 ◽  
Vol 43 (5) ◽  
pp. e121-e129 ◽  
Author(s):  
S. J. Head ◽  
R. L. J. Osnabrugge ◽  
N. J. Howell ◽  
N. Freemantle ◽  
B. Bridgewater ◽  
...  
Keyword(s):  
Systematic Review ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Model Development ◽  
Adult Cardiac Surgery

scholarly journals An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6928
Author(s):  
Łukasz Wojtecki ◽  
Sebastian Iwaszenko ◽  
Derek B. Apel ◽  
Tomasz Cichy
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Rock Mass ◽  
Decision Tree ◽  
Risk Prediction ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Hard Coal ◽  
Underground Excavations

Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.

P3429A novel prediction model for mortality after cardiac surgery using institutional case volume

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Lee ◽  
J B Park ◽  
Y J Cho ◽  
H G Ryu ◽  
E J Jang
Keyword(s):  
Cardiac Surgery ◽  
Hospital Mortality ◽  
Risk Prediction ◽  
Prediction Models ◽  
Validation Cohort ◽  
Mortality Data ◽  
P Value ◽  
Case Volume ◽  
Risk Prediction Models ◽  
Derivation Cohort

Abstract Purpose A number of risk prediction models have been developed to identify short term mortality after cardiovascular surgery. Most models include patient characteristics, laboratory data, and type of surgery, but no consideration for the amount of surgical experience. With numerous reports on the impact of case volume on patient outcome after high risk procedures, we attempted to develop a risk prediction models for in-hospital and 1-year mortality that takes institutional case volume into account. Methods We identified adult patients who underwent cardiac surgery from January 2008 to December 2017 from the National Health Insurance Service (NHIS) database by searching for patients with procedure codes of coronary artery bypass grafting, valve surgery, and surgery on thoracic aorta during the hospitalization. Study subjects were randomly assigned to either the derivation cohort or the validation cohort. In-hospital mortality and 1-year mortality data were collected using the NHIS database. Risk prediction models were developed from the derivation cohort using Cox proportional hazards regression. The prediction performances of models were evaluated in the validation cohort. Results The models developed in this study demonstrated fair discrimination for derivation cohort (N=22,004, c-statistics, 0.75 for in-hospital mortality; 0.73 for 1-year mortality) and acceptable calibration in the validation cohort. (N=22,003, Hosmer-Lemeshow χ2-test, P=0.08 and 0.16, respectively). Case volume was the key factor of mortality prediction models after cardiac surgery. (50≤ x <100 case per year. 100≤ x <200 case per year, ≥200 case per year are correlated with OR 3.29, 2.49, 1.85 in in-hospital mortality, 2.76, 1.99, 1.69 in 1-year mortality respectively, P value <0.001.) Annual case volume as risk factor Variables In-hospital mortality 1-year mortality OR (95% CI) p-value OR (95% CI) p-value Annual case-volume (reference: ≥200) – – 100–200 1.69 (1.48, 1.93) <0.001 1.85 (1.58, 2.18) <0.001 50–100 1.99 (1.75, 2.25) <0.001 2.49 (2.15, 2.89) <0.001 <50 2.76 (2.44, 3.11) <0.001 3.29 (2.85, 3.79) <0.001 OR: Odds ratio; CI: confidence interval; Ref: Reference. Discrimination and calibration Conclusion We developed and validated new risk prediction models for in-hospital and 1-year mortality after cardiac surgery using the NHIS database. These models may provide useful guides to predict mortality risks of patients with basic information and without laboratory findings.

Machine Learning for Web Proxy Analytics

2019 ◽  
Vol 1 (2) ◽  
pp. 30-41
Author(s):  
Mark Maldonado ◽  
Ayad Barsoum
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Everyday Life ◽  
Small Businesses ◽  
Electronic Device ◽  
Proxy Server ◽  
Proxy Servers ◽  
The Neural Network ◽  
Trained Neural Network

Proxy servers used around the globe are typically graded and built for small businesses to large enterprises. This does not dismiss any of the current efforts to keep the general consumer of an electronic device safe from malicious websites or denying youth of obscene content. With the emergence of machine learning, we can utilize the power to have smart security instantiated around the population's everyday life. In this work, we present a simple solution of providing a web proxy to each user of mobile devices or any networked computer powered by a neural network. The idea is to have a proxy server to handle the functionality to allow safe websites to be rendered per request. When a website request is made and not identified in the pre-determined website database, the proxy server will utilize a trained neural network to determine whether or not to render that website. The neural network will be trained on a vast collection of sampled websites by category. The neural network needs to be trained constantly to improve decision making as new websites are visited.

Effectiveness of a Hybrid Deep Learning Model Integrated with a Hybrid Parameterisation Model in Decision-Making Analysis

2020 ◽  
Author(s):  
Masurah Mohamad ◽  
Ali Selamat
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Missing Data ◽  
Machine Learning Technique ◽  
Processing Times ◽  
Learning Technique ◽  
Neural Network Algorithm ◽  
Deep Learning Model

Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model.

Should Older Patients Be Selectively Referred to High-Volume Centers for Abdominal Aortic Surgery?

Vascular ◽  
2004 ◽  
Vol 12 (1) ◽  
pp. 51-56 ◽  
Author(s):  
Justin B. Dimick ◽  
Peter J. Pronovost ◽  
John A. Cowan ◽  
Reid M. Wainess ◽  
Gilbert R. Upchurch
Keyword(s):  
Hospital Mortality ◽  
Hospital Volume ◽  
Aortic Surgery ◽  
High Volume ◽  
Mortality Rates ◽  
Annual Number ◽  
Case Mix ◽  
Patient Case ◽  
Abdominal Aortic Surgery ◽  
Abdominal Aortic

The objective of the current study was to determine the effect of hospital volume on outcomes of abdominal aortic surgery for patients older than and younger than 65 years. In order to perform this investigation, information on all adult patients who underwent abdominal aortic surgery in Maryland from 1994 to 1996 ( N = 2,987 patients) in 45 acute care hospitals was obtained. Hospitals were designated as low (< 20/year), medium (20 to 36/year), or high (> 36/year) volume according to the annual number of procedures performed. The relationship of hospital volume and mortality was determined for patients less than or greater than 65 years old. Two separate multiple logistic regression models were used to adjust for patient case-mix in each age category. Of the 2,987 patients, 2,067 (69%) were older than 65 years and 920 (31%) were younger. The crude in-hospital mortality rates according to hospital volume were 2.7% (low), 2.1% (medium), and 2.7% (high) for patients younger than 65 years old ( p = .8). For patients older than 65 years, in-hospital mortality rates were 11.9% (low), 9.9% (medium), and 6.9% (high) ( p = .005). After adjusting for patient case-mix in a multivariate analysis, high hospital volume was associated with a decreased risk of in-hospital mortality for patients older than 65 years (OR 0.57; 95% CI 0.37 to 0.86; p = .008) but not for patients under 65 years old. In conclusion, hospital volume was associated with decreased in-hospital mortality after abdominal aortic surgery only for patients greater than 65 years old. Because of this differential effect, targeting elderly patients for regionalization would achieve most potentially avoidable deaths for this common high-risk surgical procedure.

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