Identification, explanation and clinical evaluation of hospital patient subtypes

Patients admitted to hospital constitute a heterogeneous population with different levels of illness severity, morbidities, response to treatments and outcomes [9]. Therefore, predicting the right treatment is challenging even when patients are initially diagnosed with the same conditions. For diagnosis and determining treatment options, physicians rely on factors including the patient's medical history [6], their own clinical experience and their professional intuition [9]. Advances in computing technologies and the introduction of electrical health records (EHR) mean that more information is available to physicians than ever before. However, hospitals are still in the process of transitioning from paper records to EHR, which leads to challenges when analyzing the data and inferring high-level information [6]. As intensive care units (ICUs) are the most data-rich hospital department, machine learning approaches have mostly focused on these environments [27, 9, 3, 19]. Recent progress has also been made for general wards [8, 21, 15, 10]. Outcome prediction and risk scoring are of high clinical importance. Several risk scoring methods have been developed and deployed, e.g.