A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Abstract

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence‐based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment‐based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component‐based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.

Evaluation of the impact of body mass index on venous thromboembolism risk factors

“…In medical science, supervised classification techniques have been employed to identify risk factors for a specific disease or to predict disease occurrence such as VTE. Among a large number of available supervised ML techniques, kernel machine learning [ 30 ], various decision trees [ 31 , 32 ], artificial neural networks [ 33 – 35 ], random forest [ 36 , 37 ], support vector machines [ 38 , 39 ], Bayesian decision rules [ 40 , 41 ], supervised principal component analysis [ 42 ], penalized regression models [ 43 ] have been applied in medical science. Although the choice of ML techniques is often based on the minimum loss function, it is difficult to make an informed decision on the most appropriate method.…”

CNN Ensemble Approach to Detect COVID-19 from Computed Tomography Chest Images

“…One of the main types of ensemble learning is called stacking or stacked generalization [26][27][28]. Stacking has been successfully implemented in regression, density estimations, distance learning, and classification, and has been used in many medical applications [29][30][31]. This technique works because it allows multiple algorithms to collaborate to solve the same problem; the various solutions can be aggregated into one better final solution.…”

Objective-Driven Modular and Hybrid Approach Combining Machine Learning and Ontology

“…In Informed Machine Learning, prior knowledge is incorporated into the machine learning process at various stages [17]. Prior knowledge is often represented by an ontology that can be used in the feature engineering phase for selection [18,19], extraction [20][21][22], or augmentation [23,24], in order to acquire more relevant features. They can also be used to facilitate the choice of the most suitable model structure [25] or be directly integrated into the machine learning algorithm [26][27][28][29][30][31].…”