RISK PREDICTION OF SUFFERING AN ACUTE CORONARY SYNDROME THROUGH THE USE OF MACHINE LEARNING CLASSIFIERS

Machine Learning (ML) algorithms have proven promising methodologies in improving Cardiovascular (CV) risk predictors based on traditional statistics. In the present work, two case studies are reported: CV risk prediction in patients affected by Inflammatory Arthritis (IA), with attention to Psoriatic Arthritis (PsA), and patients who experienced Acute Coronary Syndrome (ACS).

Download Full-text

Machine Learning Based Prediction Model for Using Non-steroidal Anti-inflammatory Drugs on Risk of Adverse Events

10.21203/rs.3.rs-153020/v1 ◽

2021 ◽

Author(s):

Juan Lu ◽

Ling Wang ◽

Mohammed Bennamoun ◽

Isaac Ward ◽

Senjian An ◽

...

Keyword(s):

Machine Learning ◽

Acute Coronary Syndrome ◽

Adverse Event ◽

Adverse Events ◽

Risk Prediction ◽

Coronary Syndrome ◽

Event Risk ◽

Inflammatory Drugs ◽

Anti Inflammatory ◽

One Year

Abstract To investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of adverse events after supplied with non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or deaths within one year from the last supply date were outputs. Machine learning classification methods were used to build models to predict ACS and optimise their performance, measured by the area under the receiver operating characteristic curve, sensitivity and specificity. There were 69007 patients in the NSAIDs cohort with mean age 76 years and 54.3% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients dead within one year after their last supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The models achieved an average AUC-ROC score of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse event risk prediction. Further investigation of additional data and approaches are required further to improve the performance for adverse event risk prediction.

Download Full-text

A comparative study of machine learning classifiers for risk prediction of asthma disease

Photodiagnosis and Photodynamic Therapy ◽

10.1016/j.pdpdt.2019.10.011 ◽

2019 ◽

Vol 28 ◽

pp. 292-296 ◽

Cited By ~ 3

Author(s):

Rahat Ullah ◽

Saranjam Khan ◽

Hina Ali ◽

Iqra Ishtiaq Chaudhary ◽

Muhammad Bilal ◽

...

Keyword(s):

Machine Learning ◽

Comparative Study ◽

Risk Prediction ◽

Machine Learning Classifiers ◽

Learning Classifiers

Download Full-text

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Scientific Reports ◽

10.1038/s41598-021-97643-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Juan Lu ◽

Ling Wang ◽

Mohammed Bennamoun ◽

Isaac Ward ◽

Senjian An ◽

...

Keyword(s):

Machine Learning ◽

Acute Coronary Syndrome ◽

Risk Prediction ◽

Administrative Data ◽

Adverse Outcome ◽

Gradient Boosting ◽

Coronary Syndrome ◽

Gradient Boosting Machine ◽

Inflammatory Drugs ◽

One Year

AbstractOur aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction.

Download Full-text