scholarly journals Development of an electronic frailty index for predicting mortality in patients undergoing transcatheter aortic valve replacement using machine learning

2020 ◽  
Author(s):  
Yiyi Chen ◽  
Jiandong Zhou ◽  
Sharen Lee ◽  
Tong Liu ◽  
Sandeep S Hothi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cox Regression ◽  
Learning Algorithm ◽  
Frailty Index ◽  
Mortality Prediction ◽  
Frailty Models ◽  
Gradient Boosting ◽  
Cumulative Number ◽  
Analysis Model ◽  
All Cause Mortality

AbstractBackgroundElectronic frailty indices can be useful surrogate measures of frailty. We assessed the role of machine learning to develop an electronic frailty index, incorporating demographics, baseline comorbidities, healthcare utilization characteristics, electrocardiographic measurements, and laboratory examinations, and used this to predict all-cause mortality in patients undergoing transaortic valvular replacement (TAVR).MethodsThis was a multi-centre retrospective observational study of patients undergoing for TAVR. Significant univariate and multivariate predictors of all-cause mortality were identified using Cox regression. Importance ranking of variables was obtained with a gradient boosting survival tree (GBST) model, a supervised sequential ensemble learning algorithm, and used to build the frailty models. Comparisons were made between multivariate Cox, GBST and random survival forest models.ResultsA total of 450 patients (49% females; median age at procedure 82.3 (interquartile range, IQR 79.0-86.0)) were included, of which 22 died during follow-up. A machine learning survival analysis model found that the most important predictors of mortality were APTT, followed by INR, severity of tricuspid regurgitation, cumulative hospital stays, cumulative number of readmissions, creatinine, urate, ALP, and QTc/QT intervals. GBST significantly outperformed random survival forests and multivariate Cox regression (precision: 0.91, recall: 0.89, AUC: 0.93, C-index: 0.96, and KS-index: 0.50) for mortality prediction.ConclusionsAn electronic frailty index incorporating multi-domain data can efficiently predict all-cause mortality in patients undergoing TAVR. A machine learning survival learning model significantly improves the risk prediction performance of the frailty models.

scholarly journals Predicting in-Hospital Mortality of Patients with COVID-19 Using Machine Learning Techniques

2021 ◽  
Vol 11 (5) ◽  
pp. 343
Author(s):  
Fabiana Tezza ◽  
Giulia Lorenzoni ◽  
Danila Azzolina ◽  
Sofia Barbar ◽  
Lucia Anna Carmela Leone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hospital Mortality ◽  
Learning Algorithm ◽  
Vital Signs ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Techniques

The present work aims to identify the predictors of COVID-19 in-hospital mortality testing a set of Machine Learning Techniques (MLTs), comparing their ability to predict the outcome of interest. The model with the best performance will be used to identify in-hospital mortality predictors and to build an in-hospital mortality prediction tool. The study involved patients with COVID-19, proved by PCR test, admitted to the “Ospedali Riuniti Padova Sud” COVID-19 referral center in the Veneto region, Italy. The algorithms considered were the Recursive Partition Tree (RPART), the Support Vector Machine (SVM), the Gradient Boosting Machine (GBM), and Random Forest. The resampled performances were reported for each MLT, considering the sensitivity, specificity, and the Receiving Operative Characteristic (ROC) curve measures. The study enrolled 341 patients. The median age was 74 years, and the male gender was the most prevalent. The Random Forest algorithm outperformed the other MLTs in predicting in-hospital mortality, with a ROC of 0.84 (95% C.I. 0.78–0.9). Age, together with vital signs (oxygen saturation and the quick SOFA) and lab parameters (creatinine, AST, lymphocytes, platelets, and hemoglobin), were found to be the strongest predictors of in-hospital mortality. The present work provides insights for the prediction of in-hospital mortality of COVID-19 patients using a machine-learning algorithm.

scholarly journals Multicenter validation of a machine learning algorithm for 48 hour all-cause mortality prediction

2018 ◽  
Author(s):  
Hamid Mohamadlou ◽  
Saarang Panchavati ◽  
Jacob Calvert ◽  
Anna Lynn-Palevsky ◽  
Christopher Barton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Learning Algorithm ◽  
Clinical Care ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Health Centers ◽  
Training Data ◽  
All Cause Mortality ◽  
Test Sets

AbstractPurposeThis study evaluates a machine-learning-based mortality prediction tool.Materials and MethodsWe conducted a retrospective study with data drawn from three academic health centers. Inpatients of at least 18 years of age and with at least one observation of each vital sign were included. Predictions were made at 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated on hold-out test data from the same institution and data from the remaining institutions. Predictions were compared to those of qSOFA and MEWS using area under the receiver operating characteristic curve (AUROC).ResultsFor training and testing on data from a single institution, machine learning predictions averaged AUROCs of 0.97, 0.96, and 0.95 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, the algorithm achieved AUROC up to 0.95, 0.93, and 0.91, for 12-, 24-, and 48-hour predictions, respectively. MEWS and qSOFA had average 48-hour AUROCs of 0.86 and 0.82, respectively.ConclusionThis algorithm may help identify patients in need of increased levels of clinical care.

scholarly journals Electronic frailty index for predicting mortality outcome of patients undergoing transaortic valvular implantation

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
Y Chen ◽  
J Zhou ◽  
S Lee ◽  
T Liu ◽  
W Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cox Regression ◽  
Frailty Index ◽  
Mortality Prediction ◽  
P Value ◽  
Mortality Outcome ◽  
Random Survival Forests ◽  
Hospital Stays ◽  
Laboratory Examinations ◽  
Risk Predictors

Abstract Funding Acknowledgements Type of funding sources: None. Objective Electronic frailty index for predicting mortality outcome of patients undergoing transaortic valvular implantation (TAVI) served as useful surrogates but is associated with a poor prognosis since it needs long time to determine the frailty status and develop the index based on electronic health records. We identify significant risk mortality predictors and tested the hypothesis that an electronic frailty index incorporating ECG measurements and laboratory examinations using a machine learning survival analysis approach can improve TAVI mortality prediction. Design A territory-wide observational study which involved a total of 450 patients (49.11% females, 22 mortalities) diagnosed undergoing TAVI and admitted to public hospitals from Hong Kong. Methods Demographics (TAVI presentation age, gender, severity of TR, AR, MR, PR, INR of TAVI presentation), prior comorbidities before TAVI presentation, ECG measurements, and CBC and LRFT laboratory examinations were analyzed. Cox regression and a supervised sequential ensemble learning algorithm: gradient boosting survival tree (GBST) model, was applied to predict mortality. Significant univariate and multivariate risk predictors of mortality were identified. Importance ranking of variables were obtained with GBST model and used to build the frailty models. Comparisons were provided with baseline models of random survival forests and multivariate Cox regression. Results The median TAVI presentation age was 82.3 years (83.8 years in mortalities, and 82.1 years in alive patients). INR of TAVI presentation in mortalities (median: 1.32) is much higher than alive ones (median: 1.07). Severe TR (hazard ratio, HR: 8.93, 95% CI: [3.22, 24.78], p value < 0.0001), INR of TAVI presentation (HR: 2.74, 95% CI: [1.84, 4.09], p value < 0.0001), cumulative hospital stays (HR: 1.01, 95% CI: [1.00, 1.01], p value = 0.0008), aspartate transaminase (HR: 1.01, 95% CI: [0.98, 1.002], p value = 0.0002), and bilirubin (HR: 1.02, 95% CI: [1.01, 1.02], p value = 0.0003) are significant mortality risk predictors. Machine learning survival analysis model found that APTT demonstrates the most important strength, followed by INR of TAVI presentation, severe TR status, cumulative hospital stays, cumulative readmission times, creatinine test, urate test ALP test, and ECG measurements of QTc and QT. GBST significantly outperformed random survival forests and multivariate Cox regression (precision: 0.91, recall: 0.89, AUC: 0.93, C-index: 0.96, and KS-index: 0.50) for mortality prediction. Conclusions  Electronic frailty index based on demographics, prior comorbidities, hospitalization characteristics, ECG measurements, and laboratory examinations can efficiently predict mortality outcome of patients undergoing TAVI. Machine learning survival learning model significantly improves the risk prediction performance and improves the construction of the frailty models for tailored interventions of TAVI patients in clinical practices.

scholarly journals Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arturo Moncada-Torres ◽  
Marissa C. van Maaren ◽  
Mathijs P. Hendriks ◽  
Sabine Siesling ◽  
Gijs Geleijnse
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Explicit Knowledge ◽  
Cox Regression ◽  
Metastatic Breast ◽  
Gradient Boosting ◽  
Support Vector ◽  
Netherlands Cancer Registry ◽  
Extreme Gradient Boosting ◽  
The Impact

AbstractCox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ c -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ($$c$$ c -index $$\sim \,0.63$$ ∼ 0.63 ), and in the case of XGB even better ($$c$$ c -index $$\sim 0.73$$ ∼ 0.73 ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.

scholarly journals Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design

2018 ◽  
Author(s):  
Jatin Kumar ◽  
Qianxiao Li ◽  
Karen Y.T. Tang ◽  
Tonio Buonassisi ◽  
Anibal L. Gonzalez-Oyarce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Point ◽  
Disordered Systems ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Inverse Design ◽  
Gradient Boosting ◽  
Multiple Length Scales ◽  
The Status

<div><div><div><p>Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4°C root mean squared error (RMSE) in a temperature range of 24– 90°C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80°C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.</p></div></div></div>

scholarly journals Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mohammad Nahid Hossain ◽  
Mohammad Helal Uddin ◽  
K. Thapa ◽  
Md Abdullah Al Zubaer ◽  
Md Shafiqul Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Cognitive Impairment ◽  
Learning Algorithm ◽  
Negative Impact ◽  
Gradient Boosting ◽  
Feature Selection Technique ◽  
Activity Data ◽  
Physical Data ◽  
Machine Learning Model

Cognitive impairment has a significantly negative impact on global healthcare and the community. Holding a person’s cognition and mental retention among older adults is improbable with aging. Early detection of cognitive impairment will decline the most significant impact of extended disease to permanent mental damage. This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. Keystroke and smartwatch have been used to extract individuals’ neurophysical and physical data, respectively. An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental State Examination (SMMSE) questionnaire scores. The statistical method “Pearson’s correlation” and the wrapper feature selection technique have been used to analyze and select the best features. Then, we have conducted our proposed algorithm GBM on those features. And the result has shown an accuracy of more than 94%. This paper has added a new dimension to the state-of-the-art to predict cognitive impairment by implementing neurophysical data and physical data together.

scholarly journals High alert drugs screening using gradient boosting classifier

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pakpoom Wongyikul ◽  
Nuttamon Thongyot ◽  
Pannika Tantrakoolcharoen ◽  
Pusit Seephueng ◽  
Piyapong Khumrin
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Drug Prescription ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Negative Consequences ◽  
Inappropriate Use ◽  
Prescription Errors ◽  
Screening Protocol

AbstractPrescription errors in high alert drugs (HAD), a group of drugs that have a high risk of complications and potential negative consequences, are a major and serious problem in medicine. Standardized hospital interventions, protocols, or guidelines were implemented to reduce the errors but were not found to be highly effective. Machine learning driven clinical decision support systems (CDSS) show a potential solution to address this problem. We developed a HAD screening protocol with a machine learning model using Gradient Boosting Classifier and screening parameters to identify the events of HAD prescription errors from the drug prescriptions of out and inpatients at Maharaj Nakhon Chiang Mai hospital in 2018. The machine learning algorithm was able to screen drug prescription events with a risk of HAD inappropriate use and identify over 98% of actual HAD mismatches in the test set and 99% in the evaluation set. This study demonstrates that machine learning plays an important role and has potential benefit to screen and reduce errors in HAD prescriptions.

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