Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining

Publication Type Journal Article
Authors Oliver Haas, Luis Ignacio Lopera Gonzalez, Sonja Hofmann, Christoph Ostgathe, Andreas Maier, Eva Rothgang, Oliver Amft, Tobias Steigleder
Title Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining
Abstract We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.
Publication Digital Health
Pages 724049
Date 2021
Journal Abbr Front. Digit. Health
DOI 10.3389/fdgth.2021.724049
URL Publisher's website
Library Catalog Frontiers
Friedrich-Alexander-Universität Erlangen-Nürnberg