scholarly journals Machine Learning Models for Predicting In-Hospital Mortality in Acute Aortic Dissection Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Tuo Guo ◽  
Zhuo Fang ◽  
Guifang Yang ◽  
Yang Zhou ◽  
Ning Ding ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Dissection ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Acute Aortic Dissection ◽  
Characteristic Curve ◽  
Survivor Group ◽  
Treatment Type ◽  
Operating Characteristic Curve

Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection.Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model.Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860–0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

scholarly journals A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation

2018 ◽  
Vol 26 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Muhammad Faisal ◽  
Andy Scally ◽  
Robin Howes ◽  
Kevin Beatson ◽  
Donald Richardson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Operating Characteristic Curve

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n = 24,696) and compared the performance of these models in data from another hospital ( n = 13,477). We used two performance measures – the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well – calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.

scholarly journals Can machine learning improve mortality prediction following cardiac surgery?

2020 ◽  
Vol 58 (6) ◽  
pp. 1130-1136
Author(s):  
Umberto Benedetto ◽  
Shubhra Sinha ◽  
Matt Lyon ◽  
Arnaldo Dimagli ◽  
Tom R Gaunt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Surgery ◽  
Random Forest ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Abstract OBJECTIVES Interest in the clinical usefulness of machine learning for risk prediction has bloomed recently. Cardiac surgery patients are at high risk of complications and therefore presurgical risk assessment is of crucial relevance. We aimed to compare the performance of machine learning algorithms over traditional logistic regression (LR) model to predict in-hospital mortality following cardiac surgery. METHODS A single-centre data set of prospectively collected information from patients undergoing adult cardiac surgery from 1996 to 2017 was split into 70% training set and 30% testing set. Prediction models were developed using neural network, random forest, naive Bayes and retrained LR based on features included in the EuroSCORE. Discrimination was assessed using area under the receiver operating characteristic curve, and calibration analysis was undertaken using the calibration belt method. Model calibration drift was assessed by comparing Goodness of fit χ2 statistics observed in 2 equal bins from the testing sample ordered by procedure date. RESULTS A total of 28 761 cardiac procedures were performed during the study period. The in-hospital mortality rate was 2.7%. Retrained LR [area under the receiver operating characteristic curve 0.80; 95% confidence interval (CI) 0.77–0.83] and random forest model (0.80; 95% CI 0.76–0.83) showed the best discrimination. All models showed significant miscalibration. Retrained LR proved to have the weakest calibration drift. CONCLUSIONS Our findings do not support the hypothesis that machine learning methods provide advantage over LR model in predicting operative mortality after cardiac surgery.

scholarly journals The Optimal Cutoff Value of D-dimer Levels to Predict in Hospital Mortality in Severe Cases of Coronavirus Disease 2019

2021 ◽  
Vol 9 (B) ◽  
pp. 1561-1564
Author(s):  
Ngakan Ketut Wira Suastika ◽  
Ketut Suega
Keyword(s):  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Optimal Cutoff ◽  
Cutoff Value ◽  
Operating Characteristic Curve ◽  
D Dimer

Introduction: Coronavirus disease 2019 (Covid-19) can cause coagulation parameters abnormalities such as an increase of D-dimer levels especially in severe cases. The purpose of this study is to determine the differences of D-dimer levels in severe cases of Covid-19 who survived and non-survived and determine the optimal cut-off value of D-dimer levels to predict in-hospital mortality. Method: Data were obtained from confirmed Covid-19 patients who were treated from June to September 2020. The Mann-Whitney U test was used to determine differences of D-dimer levels in surviving and non-surviving patients. The optimal cut-off value and area under the curve (AUC) of the D-dimer level in predicting mortality were obtained by the receiver operating characteristic curve (ROC) method. Results: A total of 80 patients were recruited in this study. Levels of D-dimer were significantly higher in non-surviving patients (median 3.346 mg/ml; minimum – maximum: 0.939 – 50.000 mg/ml) compared to surviving patients (median 1.201 mg/ml; minimum – maximum: 0.302 – 29.425 mg/ml), p = 0.012. D-dimer levels higher than 1.500 mg/ml are the optimal cut-off value for predicting mortality in severe cases of Covid-19 with a sensitivity of 80.0%; specificity of 64.3%; and area under the curve of 0.754 (95% CI 0.586 - 0.921; p = 0.010). Conclusions: D-dimer levels can be used as a predictor of mortality in severe cases of Covid-19.

scholarly journals A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children

2020 ◽  
Vol 31 (6) ◽  
pp. 1348-1357 ◽  
Author(s):  
Ibrahim Sandokji ◽  
Yu Yamamoto ◽  
Aditya Biswas ◽  
Tanima Arora ◽  
Ugochukwu Ugwuowo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Health Record ◽  
Hospitalized Children ◽  
Operating Characteristic Curve ◽  
Electronic Health

BackgroundTimely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges.MethodsWe retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay.ResultsAmong 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points.ConclusionsUsing various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.

scholarly journals Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension

2018 ◽  
Vol 129 (4) ◽  
pp. 675-688 ◽  
Author(s):  
Samir Kendale ◽  
Prathamesh Kulkarni ◽  
Andrew D. Rosenberg ◽  
Jing Wang
Keyword(s):  
Machine Learning ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Predictive Analytics ◽  
Characteristic Curve ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Machine Learning Methods ◽  
Gradient Boosting Machine ◽  
Operating Characteristic Curve

AbstractEditor’s PerspectiveWhat We Already Know about This TopicWhat This Article Tells Us That Is NewBackgroundHypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine-learning methods can provide prediction for the risk of postinduction hypotension.MethodsData was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine-learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation.ResultsOut of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77).ConclusionsThe success of this technique in predicting postinduction hypotension demonstrates feasibility of machine-learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.

scholarly journals Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study (Preprint)

2021 ◽  
Author(s):  
Tao Zhong ◽  
Zian Zhuang ◽  
Xiaoli Dong ◽  
Ka Hing Wong ◽  
Wing Tak Wong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Injury ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Alanine Transaminase ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Operating Characteristic Curve

BACKGROUND Tuberculosis (TB) is a pandemic, being one of the top 10 causes of death and the main cause of death from a single source of infection. Drug-induced liver injury (DILI) is the most common and serious side effect during the treatment of TB. OBJECTIVE We aim to predict the status of liver injury in patients with TB at the clinical treatment stage. METHODS We designed an interpretable prediction model based on the XGBoost algorithm and identified the most robust and meaningful predictors of the risk of TB-DILI on the basis of clinical data extracted from the Hospital Information System of Shenzhen Nanshan Center for Chronic Disease Control from 2014 to 2019. RESULTS In total, 757 patients were included, and 287 (38%) had developed TB-DILI. Based on values of relative importance and area under the receiver operating characteristic curve, machine learning tools selected patients’ most recent alanine transaminase levels, average rate of change of patients’ last 2 measures of alanine transaminase levels, cumulative dose of pyrazinamide, and cumulative dose of ethambutol as the best predictors for assessing the risk of TB-DILI. In the validation data set, the model had a precision of 90%, recall of 74%, classification accuracy of 76%, and balanced error rate of 77% in predicting cases of TB-DILI. The area under the receiver operating characteristic curve score upon 10-fold cross-validation was 0.912 (95% CI 0.890-0.935). In addition, the model provided warnings of high risk for patients in advance of DILI onset for a median of 15 (IQR 7.3-27.5) days. CONCLUSIONS Our model shows high accuracy and interpretability in predicting cases of TB-DILI, which can provide useful information to clinicians to adjust the medication regimen and avoid more serious liver injury in patients.

Prediction of extubation failure in the paediatric cardiac ICU using machine learning and high-frequency physiologic data

2021 ◽  
pp. 1-8
Author(s):  
Sydney R. Rooney ◽  
Evan L. Reynolds ◽  
Mousumi Banerjee ◽  
Sara K. Pasquali ◽  
John R. Charpie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Extubation Failure ◽  
Machine Learning Methods ◽  
Operating Characteristic Curve ◽  
Physiologic Data ◽  
Receiver Operating

Abstract Background: Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease. Methods: This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve. Results: Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved. Conclusion: Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.

Revisiting performance metrics for prediction with rare outcomes

2021 ◽  
Vol 30 (10) ◽  
pp. 2352-2366
Author(s):  
Samrachana Adhikari ◽  
Sharon-Lise Normand ◽  
Jordan Bloom ◽  
David Shahian ◽  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
True Positive ◽  
Post Surgery ◽  
Operating Characteristic Curve ◽  
Prediction Problems ◽  
Receiver Operating

Machine learning algorithms are increasingly used in the clinical literature, claiming advantages over logistic regression. However, they are generally designed to maximize the area under the receiver operating characteristic curve. While area under the receiver operating characteristic curve and other measures of accuracy are commonly reported for evaluating binary prediction problems, these metrics can be misleading. We aim to give clinical and machine learning researchers a realistic medical example of the dangers of relying on a single measure of discriminatory performance to evaluate binary prediction questions. Prediction of medical complications after surgery is a frequent but challenging task because many post-surgery outcomes are rare. We predicted post-surgery mortality among patients in a clinical registry who received at least one aortic valve replacement. Estimation incorporated multiple evaluation metrics and algorithms typically regarded as performing well with rare outcomes, as well as an ensemble and a new extension of the lasso for multiple unordered treatments. Results demonstrated high accuracy for all algorithms with moderate measures of cross-validated area under the receiver operating characteristic curve. False positive rates were [Formula: see text]1%, however, true positive rates were [Formula: see text]7%, even when paired with a 100% positive predictive value, and graphical representations of calibration were poor. Similar results were seen in simulations, with the addition of high area under the receiver operating characteristic curve ([Formula: see text]90%) accompanying low true positive rates. Clinical studies should not primarily report only area under the receiver operating characteristic curve or accuracy.

scholarly journals Premio de Investigación SCHOT 2020: desarrollo y validación de un modelo multivariables de predicción de estadía hospitalaria en pacientes mayores de 65 años sometidos artroplastia total de cadera electiva en Chile utilizando aprendizaje de máquinas

2021 ◽  
Vol 62 (03) ◽  
pp. e180-e192
Author(s):  
Claudio Díaz-Ledezma ◽  
David Díaz-Solís ◽  
Raúl Muñoz-Reyes ◽  
Jonathan Torres Castro
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Support Vector ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Resumen Introducción La predicción de la estadía hospitalaria luego de una artroplastia total de cadera (ATC) electiva es crucial en la evaluación perioperatoria de los pacientes, con un rol determinante desde el punto de vista operacional y económico. Internacionalmente, se han empleado macrodatos (big data, en inglés) e inteligencia artificial para llevar a cabo evaluaciones pronósticas de este tipo. El objetivo del presente estudio es desarrollar y validar, con el empleo del aprendizaje de máquinas (machine learning, en inglés), una herramienta capaz de predecir la estadía hospitalaria de pacientes chilenos mayores de 65 años sometidos a ATC por artrosis. Material y Métodos Empleando los registros electrónicos de egresos hospitalarios anonimizados del Departamento de Estadísticas e Información de Salud (DEIS), se obtuvieron los datos de 8.970 egresos hospitalarios de pacientes sometidos a ATC por artrosis entre los años 2016 y 2018. En total, 15 variables disponibles en el DEIS, además del porcentaje de pobreza de la comuna de origen del paciente, fueron incluidos para predecir la probabilidad de que un paciente presentara una estadía acortada (< 3 días) o prolongada (> 3 días) luego de la cirugía. Utilizando técnicas de aprendizaje de máquinas, 8 algoritmos de predicción fueron entrenados con el 80% de la muestra. El 20% restante se empleó para validar las capacidades predictivas de los modelos creados a partir de los algoritmos. La métrica de optimización se evaluó y ordenó en un ranking utilizando el área bajo la curva de característica operativa del receptor (area under the receiver operating characteristic curve, AUC-ROC, en inglés), que corresponde a cuan bien un modelo puede distinguir entre dos grupos. Resultados El algoritmo XGBoost obtuvo el mejor desempeño, con una AUC-ROC promedio de 0,86 (desviación estándar [DE]: 0,0087). En segundo lugar, observamos que el algoritmo lineal de máquina de vector de soporte (support vector machine, SVM, en inglés) obtuvo una AUC-ROC de 0,85 (DE: 0,0086). La importancia relativa de las variables explicativas demostró que la región de residencia, el servicio de salud, el establecimiento de salud donde se operó el paciente, y la modalidad de atención son las variables que más determinan el tiempo de estadía de un paciente. Discusión El presente estudio desarrolló algoritmos de aprendizaje de máquinas basados en macrodatos chilenos de libre acceso, y logró desarrollar y validar una herramienta que demuestra una adecuada capacidad discriminatoria para predecir la probabilidad de estadía hospitalaria acortada versus prolongada en adultos mayores sometidos a ATC por artrosis. Conclusión Los algoritmos creados a traves del empleo del aprendizaje de máquinas permiten predecir la estadía hospitalaria en pacientes chilenos operado de artroplastia total de cadera electiva.

scholarly journals Claims‐Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine‐Learning Approaches

2020 ◽  
Vol 9 (19) ◽  
Author(s):  
Mei‐Sing Ong ◽  
Jeffrey G. Klann ◽  
Kueiyu Joshua Lin ◽  
Bradley A. Maron ◽  
Shawn N. Murphy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Decision Rules ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background Real‐world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts—a crucial first step underpinning the validity of research results—remains a challenge. We developed and evaluated claims‐based case ascertainment algorithms for pulmonary hypertension (PH), comparing conventional decision rules with state‐of‐the‐art machine‐learning approaches. Methods and Results We analyzed an electronic health record‐Medicare linked database from two large academic tertiary care hospitals (years 2007–2013). Electronic health record charts were reviewed to form a gold standard cohort of patients with (n=386) and without PH (n=164). Using health encounter data captured in Medicare claims (including patients’ demographics, diagnoses, medications, and procedures), we developed and compared 2 approaches for identifying patients with PH: decision rules and machine‐learning algorithms using penalized lasso regression, random forest, and gradient boosting machine. The most optimal rule‐based algorithm—having ≥3 PH‐related healthcare encounters and having undergone right heart catheterization—attained an area under the receiver operating characteristic curve of 0.64 (sensitivity, 0.75; specificity, 0.48). All 3 machine‐learning algorithms outperformed the most optimal rule‐based algorithm ( P <0.001). A model derived from the random forest algorithm achieved an area under the receiver operating characteristic curve of 0.88 (sensitivity, 0.87; specificity, 0.70), and gradient boosting machine achieved comparable results (area under the receiver operating characteristic curve, 0.85; sensitivity, 0.87; specificity, 0.70). Penalized lasso regression achieved an area under the receiver operating characteristic curve of 0.73 (sensitivity, 0.70; specificity, 0.68). Conclusions Research‐grade case identification algorithms for PH can be derived and rigorously validated using machine‐learning algorithms. Simple decision rules commonly applied in published literature performed poorly; more complex rule‐based algorithms may potentially address the limitation of this approach. PH research using claims data would be considerably strengthened through the use of validated algorithms for cohort ascertainment.

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