Hospital be involved in readmission? Highly accurate models that

Hospital readmission (admission to a hospital within 30 days of discharge) is disruptive phenomena to both patients and healthcare providers. Although it is sometimes inevitable, it is frequently caused due to human error or premature hospital dishcharge which leads to distressing experience for the patients, longer treatment times, or even increase of probability of mortality. The analysis of hospital readmission continues to be challenging based on the multitude of influencing factors (e.g. seasonal variations) and is considered a critical metric of quality and cost of healthcare cite{stiglic2014readmission}. Based on cite{srivastava2013pediatric} report, readmission rate within 30 days is 19.6\%, 34.0\% within 90 days and 56.1\% within one year following discharge. According to the Institute for Healthcare Improvement, of the 5 million U.S. hospital readmissions, approximately 76\% can be prevented, generating the annual cost of about US$25 billion cite{srivastava2013pediatric}.

Potential benefits of accurate models for readmission risk prediction led to many types of research based on patient data embedded in electronic health records (EHRs) cite{saunders2015impact, stiglic2015comprehensible}. However, all these approaches attempt to quantify the risk of readmission on patient’s discharge, but do not try to answer the very important question: which diagnoses are likely to be involved in readmission? Highly accurate models that could answer this question would provide not only indicator of readmission risk but also assessment of the risk of specific complications (diagnoses or symptoms) on next admission. These models could provide valuable decision support for doctors in time of discharge (they could decide if additional monitoring or testing is required for a specific patient) and push analytic models from predictive towards a prescriptive role in healthcare decision support.

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In order to address this problem we propose a method for multi-label classification of readmission risk based on integration of expert- and data-driven modelling. Compared to frequently explored single-label classification, multi-label classification allows simultaneous prediction of diagnoses and symptoms at time of readmission. As the basis for multi-label classification we used well-known Predictive Clustering Trees (PCTs) cite{blockeel1998top, vens2008decision, kocev2013tree}. Since PCTs performs Decision Tree-like clustering of diagnoses with which patient is likely to be re-admitted, it is easy to interpret these models. We adapted PCTs for usage of domain-knowlegde in form hierarchy, namely Clinical Classification Software (CCS) cite{healthcare2010clinical}. More specifically, instead of directly predicting the set of readmitted diagnosis, we try to predict their taxonomies from the CCS hierarchy which is called hierarchical multi-label classification. Finally, besides utilizing the expert-knowledge provided by the CCS hierarchy, we try to derive a hierarchy from the data that appear in the output space of the classification problem and use this hierarchy in the learning and prediction phases in order to improve the predictive performance. Construction of the hierarchies from the dataset is done using a hierarchical clustering approaches based on balanced k-means and agglomerative clustering. Here, we strive to investigate how the data-driven hierarchies of medical concepts which are not formally written (but occurs in practice) can influence on the predictive performance of the classification models.

Having this in mind, we applied this approach on data obtained from hospital discharge data from the California, State Inpatient Databases (SID), Healthcare Cost and Utilization Project cite{hcupnet2003utilization}, Agency for Healthcare Research and Quality. Obtained models are interpreted, analyzed and evaluated for compliance with current medical findings.