title: A machine learning framework for auto classification of imaging system exams in hospital setting for utilization optimization.
abstract: In clinical environment, Interventional X-Ray (IXR) system is used on various anatomies and for various types of the procedures. It is important to classify correctly each exam of IXR system into respective procedures and/or assign to correct anatomy. This classification enhances productivity of the system in terms of better scheduling of the Cath lab, also provides means to perform device usage/revenue forecast of the system by hospital management and focus on targeted treatment planning for a disease/anatomy. Although it may appear classification of each exam into respective procedure/anatomy a simple task. However, in real-life hospital settings, it is well-known that same system settings are used to perform different types of procedures. Though, such usage leads to under-utilization of the system. In this work, a method is developed to classify exams into respective anatomical type by applying machine-learning techniques (SVM, KNN and decision trees) on log information of the systems. The classification result is promising with accuracy of greater than 90%.
authors.forename: Meru A
authors.initials: MA
authors.lastname: Patil
authors.forename: Ravindra B
authors.initials: RB
authors.lastname: Patil
authors.forename: P
authors.initials: P
authors.lastname: Krishnamoorthy
authors.forename: Jacob
authors.initials: J
authors.lastname: John
mesh_terms: D000465:Algorithms; D001071:Appointments and Schedules; D003663:Decision Trees; D006751:Hospital Information Systems; D006761:Hospitals; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015642:Radiology, Interventional / Q000379:methods*; D060388:Support Vector Machine; D014965:X-Rays