A supervised machine learning approach to generate the auto rule for clinical decision support system

Sanjib Raj Pandey, Jixin Ma, Choi-Hong Lai, Prakash Raj Regmi
2020 Trends in Medicine  
 Supervised learning, which trains a model on known inputs and output data to predict future outputs  Unsupervised learning, which finds hidden patterns or intrinsic structures in the input data  Semi-supervised learning, which uses a mixture of both techniques; some learning uses supervised data, some learning uses unsupervised learning Machine Learning Unsupervised Learning Supervised learning Develop model based on both input and output data Group and interpret data based only on input
more » ... a Clustering Classification Regression Predicting cardiovascular disease using electronic health records  681 UK General Practices  383,592 patients free from CVD registered 1 st of January 2005 followed up for years  Two-fold cross validation (similar to other epidemiological studies): n = 295,267 "training set"; n = 82,989 "validation set"  30 separate included features including biometrics, clinical history, lifestyle, test results, prescribing  Four types of models: logistic, random forest, gradient boosting machines, and neural networks
doi:10.15761/tim.1000232 fatcat:yeph7fvwr5bf5helhtelrf4i64