Mining Multimorbidity Trajectories and Co-Medication Effects from Patient Data to Predict Post–Hip Fracture Outcomes

Abstract

Hip fractures have profound impacts on patients’ conditions and quality of life, even when they receive therapeutic treatments. Many patients face the risk of poor prognosis, physical impairment, and even mortality, especially older patients. Accurate patient outcome estimates after an initial fracture are critical to physicians’ decision-making and patient management. Effective predictions might benefit from analyses of patients’ multimorbidity trajectories and medication usages. If adequately modeled and analyzed, they could help identify patients at higher risk of recurrent fractures or mortality. Most analytics methods overlook the onset, co-occurrence, and temporal sequence of distinct chronic diseases in the trajectory, and they also seldom consider the combined effects of different medications. To support effective predictions, we develop a novel deep learning–based method that uses a cross-attention mechanism to model patient progression by obtaining “contextual information” from multimorbidity trajectories. This method also incorporates a nested self-attention network that captures the combined effects of distinct medications by learning the interactions among medications and how dosages might influence post-fracture outcomes. A real-world patient data set is used to evaluate the proposed method, relative to six benchmark methods. The comparative results indicate that our method consistently outperforms all the benchmarks in precision, recall, F-measures, and area under the curve. The proposed method is generalizable and can be implemented as a decision support system to identify patients at greater risk of recurrent hip fractures or mortality, which should help clinical decision-making and patient management.