Integration of Artifact Detection in Clinical Decision Support Systems

Abstract

Artifact detection (AD) is a critical component of Clinical Decision Support Systems (CDSS) to assess the quality of data and issue clinical alarms with high specificity and low false alarm rates. An in-depth review of the literature reveals that, although many advanced AD algorithms have been developed, very few have been evaluated in a realtime environment or reached actual clinical implementation. Furthermore, no single AD algorithm is necessarily best-suited to serve the varying needs of a CDSS. This identifies the need to develop and evaluate a framework that supports the interoperability of a range of configurable AD algorithms that could be integrated with CDSS. This thesis develops an AD framework to address six different shortcomings identified by the literature review. This framework supports the following six features: (f.1) Flexibility to serve the needs of patient populations from different types of Critical Care Units (CCU) through generalization and customizability; (f.2) Reusability across multiple types of physiologic data harvested by different Original Equipment Manufacturer (OEM) monitors; (f.3) Standardized definitions of Signal Quality Indicators (SQI) that promote interoperability and comparison between independently developed components; (f.4) Reusability and scalability by mixing and matching several AD, Parameter Derivation (PD), and Clinical Event Detection (CED) components in various combinations; (f.5) Customizability to evaluate and compare the performance of multiple combinations of independently developed components on offline and potentially real-time patient data when integrated with clinical workflows; and (f.6) Standardized component interfaces that can potentially support real-time clinical implementation of AD. The developed framework provides for a unique test bed with the ability to create new AD composition pipelines by mixing and matching independently developed I would like to express profound gratitude towards my thesis supervisors, Professor James R. Green and Professor Carolyn McGregor. They have both provided comprehensive guidance and tremendous support during my doctoral journey at Carleton.