Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification

Panchavati, Saarang; Lam, Carson; Zelin, Nicole Sitkin; Pellegrini, Emily; Barnes, Gina; Hoffman, Jana; Garikipati, Anurag; Calvert, Jacob; Mao, Qingqing; Das, Ritankar

doi:10.1049/htl2.12017

Cited by 9 publications

(6 citation statements)

References 52 publications

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“…Work [ 29 ] also discuss the development of a heart disease prediction model (on benchmarking datasets). Article [ 17 ] proposes the development of a machine learning algorithm to predict myocardial infarction diagnosis using electronic health record data readily available during Emergency Department assessments. Work [ 22 ] is a state of the art for using the Internet of Things with quantum dots in medicine.…”

Section: State Of the Artmentioning

confidence: 99%

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

Elvas,

Nunes,

Ferreira

et al. 2023

JPM

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Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.

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Section: State Of the Artmentioning

confidence: 99%

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

Elvas,

Nunes,

Ferreira

et al. 2023

JPM

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“…XGBoost: XGBoost is an ensemble machine learning technique based on decision trees that employ a gradient boosting approach [ 20 ]. The parameters used for the proposed XGBoost classifier are as follows: estimators’ maximum depth = 4 and ‘binary logistic’ objective function.…”

Section: Proposed Systemmentioning

confidence: 99%

Diabetes prediction using machine learning and explainable AI techniques

Tasin

Nabil

Islam

et al. 2022

Healthcare Tech Letters

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Globally, diabetes affects 537 million people, making it the deadliest and the most common non-communicable disease. Many factors can cause a person to get affected by diabetes, like excessive body weight, abnormal cholesterol level, family history, physical inactivity, bad food habit etc. Increased urination is one of the most common symptoms of this disease. People with diabetes for a long time can get several complications like heart disorder, kidney disease, nerve damage, diabetic retinopathy etc. But its risk can be reduced if it is predicted early. In this paper, an automatic diabetes prediction system has been developed using a private dataset of female patients in Bangladesh and various machine learning techniques. The authors used the Pima Indian diabetes dataset and collected additional samples from 203 individuals from a local textile factory in Bangladesh. Feature selection algorithm mutual information has been applied in this work. A semi-supervised model with extreme gradient boosting has been utilized to predict the insulin features of the private dataset. SMOTE and ADASYN approaches have been employed to manage the class imbalance problem. The authors used machine learning classification methods, that is, decision tree, SVM, Random Forest, Logistic Regression, KNN, and various ensemble techniques, to determine which algorithm produces the best prediction results. After training on and testing all the classification models, the proposed system provided the best result in the XGBoost classifier with the ADASYN approach with 81% accuracy, 0.81 F1 coefficient and AUC of 0.84. Furthermore, the domain adaptation method has been implemented to demonstrate the versatility of the proposed system. The explainable AI approach with LIME and SHAP frameworks is implemented to understand how the model predicts the final results. Finally, a website framework and an Android smartphone application have been developed to input various features and predict diabetes instantaneously. The private dataset of female Bangladeshi patients and programming codes are available at the following link: https://github.com/tansin-nabil/Diabetes-Prediction-Using-Machine-Learning.

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“…Finding Implications: Data Missingness This is the study that aimed to sex and race differences in data missingness of key EHR features that are commonly used as input variables in ML models. 47,[56][57][58] Although different approaches, such as imputation methods, have been implemented to treat missing data, 59 little is known about how race and sex differences in data missingness can perpetuate biases in ML performance. Data quality can be impacted by the data collection process or other factors.…”

Section: Background and Comparison To Previous Researchmentioning

confidence: 99%

Assessment of sex and racial biases in electronic health records of emergency department patients with acute coronary syndrome

Zeng

Xiang

Allen

et al. 2022

Preprint

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Background Electronic health records (EHRs) contain individualized patient data that can be used to develop diagnostic and risk prediction models with artificial intelligence (AI) algorithms. Explicit and implicit sources of bias embedded in EHRs may hinder generalizable model performance and perpetuate bias. Objective This study explores the question of how existing sex and racial disparities in the clinical assessment and management of acute coronary syndrome (ACS) are reflected in patients’ EHRs. We outlined recommendations on how these sources of bias should be examined and mitigated within the framework of development and validation of AI-based diagnosis and risk stratification algorithms. Methods This retrospective study examined several previously unrecognized EHR-embedded biases in a multisite emergency departments (ED) setting. We assessed sex and race differences in EHR data missingness and timeliness of several key ED procedures following ACS suspicion. We additionally conducted a data-driven clustering analysis to detect latent groups of ACS patients. Results 10,043 ACS-associated ED visits were included. We identified sex and race differences in the prevalence of ACS symptoms, data missingness (vitals and labs), and waiting time for troponin order and treatment initiation within 24 hours post-admission. Our cluster analysis discovered four groups of ACS patients corresponding to common symptoms. Additionally, we identified differences in clinical management and patient demographics across these clusters. Conclusions We discovered several sources of bias inherent in the clinical practice of ACS diagnosis and treatment that are also present in EHR data. Our study supports the inclusion of a wider range of symptoms into AI-based diagnosis and risk stratification tools. These models should be also validated in ED admissions without chest pain and in patients who experience a longer waiting time for ED procedures.

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Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification

Cited by 9 publications

References 52 publications

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

Diabetes prediction using machine learning and explainable AI techniques

Assessment of sex and racial biases in electronic health records of emergency department patients with acute coronary syndrome

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