A Pattern-Based Approach for Representing Condition-Action Clinical Rules into DSSs

Minutolo, Aniello; Esposito, Massimo; Pietro, Giuseppe De

doi:10.1007/978-1-4614-3535-8_65

Cited by 6 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Integrating computerized Clinical Decision Support Systems (CDSS) in critical care workflows can lead to reduced rates of patient mortality and morbidity [1][2][3][4]. CDSS can effectively integrate patient data and analyze those using Parameter Derivation (PD) and Clinical Event Detection (CED) algorithms.…”

Section: Introductionmentioning

confidence: 99%

An Artifact Detection Framework for Clinical Decision Support Systems

2015

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

An Artifact Detection Framework for Clinical Decision Support Systems

2015

View full text Add to dashboard Cite

“…and (6) examining the generalizability of the results" [46]. This thesis creates pipelines of components to address specific problems.…”

Section: Methodsmentioning

confidence: 99%

“…Physiologic signals exhibit trends, dynamics and correlations reflecting the complexity of underlying patient physiology [1][2][3] Parameter Derivation (PD), which derive clinically useful low frequency parameters from high frequency input data, and Clinical Event Detection (CED) algorithms, which identify clinically significant events and early onset indicators of various pathophysiologies. Past research in CDSS has shown the potential to lower patient mortality and morbidity rates when integrated in critical care workflows [5][6][7][8][9].…”

Section: Research Significancementioning

confidence: 99%

“…Parameter Derivation (PD) techniques derive clinically useful low frequency parameters from high frequency input data as in [102][103][104][105][106][107][108]. Past research in CDSS has shown the potential to lower patient mortality and morbidity rates when integrated in critical care workflows [6][7][8][9]. However, the inherent impact of signal artifacts on quality of physiologic data presents a challenge that affects the reliability and accuracy of analytical results [10].…”

Section: Artifact Detectionmentioning

confidence: 99%

“…research groups have rarely found their way into clinical implementation. This leads to the inference that a gap exists between research efforts in AD and their utilization in real-time clinical workflows.Whereas real-time clinical implementation of AD algorithms is noticeably lacking, there is growing interest amongst clinicians to use CED and PD for automated clinical decision support in CCU, such as in[6,31,96,[212][213][214][215][216][217][218][219][220][221][222]. Physiologic signal quality assessment through integration of AD can improve the outcome, reliability and accuracy of CED and PD research.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Integration of Artifact Detection in Clinical Decision Support Systems

Nizami¹

View full text Add to dashboard Cite

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.

show abstract

Implementation of Artifact Detection in Critical Care: A Methodological Review

Nizami

Green

McGregor

2013

IEEE Rev. Biomed. Eng.

View full text Add to dashboard Cite

Artifact detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in critical care units (CCU) by assessing quality of data prior to clinical event detection (CED) and parameter derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: 1) CCU; 2) physiologic data source; 3) harvested data; 4) data analysis; 5) clinical evaluation; and 6) clinical implementation. Review results show that most published algorithms: a) are designed for one specific type of CCU; b) are validated on data harvested only from one OEM monitor; c) generate signal quality indicators (SQI) that are not yet formalized for useful integration in clinical workflows; d) operate either in standalone mode or coupled with CED or PD applications; e) are rarely evaluated in real-time; and f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: 1) type; 2) frequency; 3) length; and 4) SQIs. This shall promote: a) reusability of algorithms across different CCU domains; b) evaluation on different OEM monitor data; c) fair comparison through formalized SQIs; d) meaningful integration with other AD, CED and PD algorithms; and e) real-time implementation in clinical workflows.

show abstract

A Pattern-Based Approach for Representing Condition-Action Clinical Rules into DSSs

Cited by 6 publications

References 7 publications

An Artifact Detection Framework for Clinical Decision Support Systems

An Artifact Detection Framework for Clinical Decision Support Systems

Integration of Artifact Detection in Clinical Decision Support Systems

Implementation of Artifact Detection in Critical Care: A Methodological Review

Contact Info

Product

Resources

About