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 Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction

2019 ◽  
Vol 26 (3) ◽  
pp. 1912-1925 ◽  
Author(s):  
Hamid Mohamadlou ◽  
Saarang Panchavati ◽  
Jacob Calvert ◽  
Anna Lynn-Palevsky ◽  
Sidney Le ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Clinical Care ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Health Centers ◽  
Training Data ◽  
Early Warning Score ◽  
Support Vector ◽  
Failure Assessment ◽  
Test Sets

In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sepsis-Related Organ Failure Assessment (qSOFA), and Modified Early Warning Score (MEWS) using area under the receiver operating characteristic curve (AUROC). For training and testing GBT on data from the same institution, the average AUROCs were 0.96, 0.95, and 0.94 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, GBT AUROCs achieved up to 0.98, 0.96, and 0.96, for 12-, 24-, and 48-hour predictions, respectively. Average AUROC for 48-hour predictions for LR, SVM, MEWS, and qSOFA were 0.85, 0.79, 0.86 and 0.82, respectively. GBT predictions may help identify patients who would benefit from increased clinical care.

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 Machine Learning Based Prediction versus Human-as-a-Security-Sensor

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Safwana Haque ◽  
George Loukas
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Social Engineering ◽  
Sensitive Information ◽  
The World ◽  
Operating Characteristic Curve ◽  
Client System

Phishing is one of the most common cyber threats in the world today. It is a type of social engineering attack where the attacker lures unsuspecting victims into carrying out certain tasks mostly to steal personal and sensitive information. These stolen information are exploited to commit further crimes e.g. blackmails, data theft, financial theft, malware installation etc. This study was carried out to tackle this problem by designing an anti-phishing learning algorithm to detect phishing emails and also to study the accuracies of human phishing prediction to machine prediction. A graphical user interface was designed to emulate an email-client system that popped-up a warning on detecting a phishing mail successfully and collection of predictions made by expert and non-expert users on anti-phishing techniques. These predictions were compared to the predictions made by the machine learning algorithm to compare the efficiencies of all predictions considered in this research. The performance of the classifier used was measured with metrics such as confusion matrix, accuracy, receiver operating characteristic curve and area under graph

scholarly journals P.2.36 Assessment of serum and urinary biomarkers for pneumoconiosis in a cohort of stone workers exposed to asbestos-contaminated minerals

2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A96.2-A96
Author(s):  
Hsiao-Yu Yang ◽  
Pau-Chung Chen
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Screening Method ◽  
Urinary Biomarkers ◽  
Machine Learning Algorithm ◽  
Dose Response Relationship

BackgroundPneumoconiosis is still a problem in workers process non-asbestiform asbestos minerals and serpentinite rocks, such as nephrite, antigorite or talc that may contaminate with paragenetic asbestos minerals. An effective screening method is still lacking. The objective of this study was to assess the diagnostic accuracy using the serum and urinary biomarkers for pneumoconiosis in workers exposed to asbestos-contaminated minerals.MethodsPrediction models of pneumoconiosis were constructed from 140 stone workers (48 cases of pneumoconiosis and 118 controls) exposed to asbestos-contaminated minerals. We measured serum soluble mesothelin-related peptide (SMRP), fibulin-3, carcinoembryonic antigen, and urinary 8-Oxo-2’-deoxyguanosine (8-OHdG)/creatinine levels. Using the ILO international classification of radiographs of pneumoconiosis profusion subcategory ≥1/0 as the reference standard, we established a prediction model by machine learning algorithm. We assessed the accuracy by the area under the receiver operating characteristic curve (AUROC).ResultsThe SMRP level increased in workers exposed to nephrite. A dose-response relationship was found between the SMRP level and the severity of pneumoconiosis in workers exposed to asbestos-contaminated minerals. Machine learning algorithm composed of sex, age, and 4 serum and urinary biomarkers is able to predict pneumoconiosis with high accuracy (AUROC ranged from 0.76 to 1.00).ConclusionOur finding highlight the use of serum and urinary biomarkers can be developed as a screening tool for pneumoconiosis in workers exposed to potential asbestos contaminated minerals.

scholarly journals A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome

2019 ◽  
Vol 26 (2) ◽  
pp. 1289-1304 ◽  
Author(s):  
Syed Waseem Abbas Sherazi ◽  
Yu Jun Jeong ◽  
Moon Hyun Jae ◽  
Jang-Whan Bae ◽  
Jong Yun Lee
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Acute Coronary Syndrome ◽  
Prediction Model ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Coronary Syndrome ◽  
Mortality Prediction Model ◽  
Operating Characteristic Curve

Cardiovascular disease is the leading cause of death worldwide so, early prediction and diagnosis of cardiovascular disease is essential for patients affected by this fatal disease. The goal of this article is to propose a machine learning–based 1-year mortality prediction model after discharge in clinical patients with acute coronary syndrome. We used the Korea Acute Myocardial Infarction Registry data set, a cardiovascular disease database registered in 52 hospitals in Korea for 1 November 2005–30 January 2008 and selected 10,813 subjects with 1-year follow-up traceability. The ranges of hyperparameters to find the best prediction model were selected from four different machine learning models. Then, we generated each machine learning–based mortality prediction model with hyperparameters completed the range fitness via grid search using training data and was evaluated by fourfold stratified cross-validation. The best prediction model with the highest performance was found, and its hyperparameters were extracted. Finally, we compared the performance of machine learning–based mortality prediction models with GRACE in area under the receiver operating characteristic curve, precision, recall, accuracy, and F-score. The area under the receiver operating characteristic curve in applied machine learning algorithms was averagely improved up to 0.08 than in GRACE, and their major prognostic factors were different. This implementation would be beneficial for prediction and early detection of major adverse cardiovascular events in acute coronary syndrome patients.

scholarly journals A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255977
Author(s):  
Bernard Aguilaniu ◽  
David Hess ◽  
Eric Kelkel ◽  
Amandine Briault ◽  
Marie Destors ◽  
...  
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Real Life ◽  
Learning Approach ◽  
Copd Patients ◽  
Machine Learning Approach ◽  
Using Data ◽  
Walking Time

Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity and mortality in COPD patients. Apart from patients with obvious EI, the identification of a such behavior during a real-life consultation is unreliable. We therefore describe a machine learning algorithm to screen for EI, as actimetry measurements are difficult to implement. Complete datasets for 1409 COPD patients were obtained from COLIBRI-COPD, a database of clinicopathological data submitted by French pulmonologists. Patient- and pulmonologist-reported estimates of PA quantity (daily walking time) and intensity (domestic, recreational, or fitness-directed) were first used to assign patients to one of four PA groups (extremely inactive [EI], overtly active [OA], intermediate [INT], inconclusive [INC]). The algorithm was developed by (i) using data from 80% of patients in the EI and OA groups to identify ‘phenotype signatures’ of non-PA-related clinical variables most closely associated with EI or OA; (ii) testing its predictive validity using data from the remaining 20% of EI and OA patients; and (iii) applying the algorithm to identify EI patients in the INT and INC groups. The algorithm’s overall error for predicting EI status among EI and OA patients was 13.7%, with an area under the receiver operating characteristic curve of 0.84 (95% confidence intervals: 0.75–0.92). Of the 577 patients in the INT/INC groups, 306 (53%) were reclassified as EI by the algorithm. Patient- and physician- reported estimation may underestimate EI in a large proportion of COPD patients. This algorithm may assist physicians in identifying patients in urgent need of interventions to promote PA.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

scholarly journals Machine learning for identification of surgeries with high risks of cancellation

2018 ◽  
Vol 26 (1) ◽  
pp. 141-155 ◽  
Author(s):  
Li Luo ◽  
Fengyi Zhang ◽  
Yao Yao ◽  
RenRong Gong ◽  
Martina Fu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Sampling Methods ◽  
Characteristic Curve ◽  
Support Vector ◽  
Chi Square ◽  
Stable Performance ◽  
Operating Characteristic Curve

Surgery cancellations waste scarce operative resources and hinder patients’ access to operative services. In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models – random forest, support vector machine, and XGBoost – were used for the identification of surgeries with high risks of cancellation. The optimal performances of the identification models were as follows: sensitivity − 0.615; specificity − 0.957; positive predictive value − 0.454; negative predictive value − 0.904; accuracy − 0.647; and area under the receiver operating characteristic curve − 0.682. Of the three models, the random forest model achieved the best performance. Thus, the effective identification of surgeries with high risks of cancellation is feasible with stable performance. Models and sampling methods significantly affect the performance of identification. This study is a new application of machine learning for the identification of surgeries with high risks of cancellation and facilitation of surgery resource management.

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