Abstract
Incorporating expert knowledge at the time machine learning models are trained holds promise for producing models that are easier to interpret. The main objectives of this study were to use a feature engineering approach to incorporate clinical expert knowledge prior to applying machine learning techniques, and to assess the impact of the approach on model complexity and performance. Four machine learning models were trained to predict mortality with a severe asthma case study. Experiments to select fewer input features based on a discriminative score showed low to moderate precision for discovering clinically meaningful triplets, indicating that discriminative score alone cannot replace clinical input. When compared to baseline machine learning models, we found a decrease in model complexity with use of fewer features informed by discriminative score and filtering of laboratory features with clinical input. We also found a small difference in performance for the mortality prediction task when comparing baseline ML models to models that used filtered features. Encoding demographic and triplet information in ML models with filtered features appeared to show performance improvements from the baseline. These findings indicated that the use of filtered features may reduce model complexity, and with little impact on performance.
Highlights
Through further assessment of MIscore rankings, we found that similar model performance for the mortality prediction task can be achieved with decreased model complexity
When considering model performance for approaches that decreased model complexity, we found that logistic regression, gradient boosting, and neural network machine learning (ML) models were robust to feature removal informed by discriminative score
We assessed the impact of two characteristics of the approach on the complexity and performance of ML models for a mortality prediction task for a severe asthma case study: ranking features by discriminative score (e.g., MIscore sum), and filtering laboratory features according to input on lab-event-lab triplets that are clinically meaningful
Summary
We explored severe asthma as a case study given the multiple limitations of current computational methods to optimize asthma care management. Incorporating clinical expert knowledge at the time that computational models are trained may help to overcome these limitations. Incorporate expert knowledge into the computational model building process has potential to produce ML models that show performance improvements. For example, found that including known risk factors of heart failure (HF) as features during training yielded the greatest improvement in the performance of models to predict HF onset [12]. Different from that approach, we use a feature engineering approach to incorporate clinical expert knowledge
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