ABSTRACT
The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called "spaghetti-like" process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case.
ABSTRACT
INTRODUCTION: The most common renal neoplasms include clear cell, papillary, and chromophobe renal cell carcinomas. The simultaneous occurrence of different histological types of adjacent neoplasms in the same organ is known as a collision tumor. Collision kidney tumors have already been described but only in rare cases. CASE DESCRIPTION: In this case report we present a 68-year-old man with chronic kidney insufficiency under dialysis who underwent an open right nephrectomy in our department with the histological diagnosis of a collision kidney tumor consisting of clear cell and papillary type 1 renal cell carcinoma. CONCLUSION: To the best of our knowledge, our case of a collision kidney tumor consisting of clear cell RCC and papillary type 1 RCC, is unique in literature.
