Length of stay prediction for clinical treatment process using temporal similarity

In clinical treatment processes, inpatient length of stay (LOS) is not only a readily available indicator of hospital activity, but also a reasonable proxy of resource consumption. Accurate inpatient LOS prediction has strong implications for health service delivery. Major techniques proposed (statistical approaches or artificial neuronal networks) consider a priori knowledge, such as demographics or patient physical factors, providing accurate methods to estimate LOS at early stages of the patient (admission). However, unexpected scenarios and variations are commonplaces of clinical treatment processes that have a dramatical impact on the LOS. Therefore, these predictors should deal with adaptability, considering the temporal evolution of the patient. In this paper, we propose an inpatient LOS prediction approach across various stages of clinical treatment processes. This proposal relies on a kind of regularity assumption demanding that patient traces of the specific treatment process with similar medical behaviors have similar LOS. Therefore, this approach follows a Case-based Reasoning methodology since it predicts an inpatient LOS of a partial patient trace by referring to the past traces of clinical treatment processes that have similar medical behaviors with the current one. The proposal is evaluated using 284 patient traces from the pulmonary infection CTPs, extracted from Zhejiang Huzhou Central Hospital of China.