On Development and Evaluation of Prototype Mobile Decision Support for Hospital Triage

Pedro, J. San; Burstein, Frada; Wassertheil, Jeff; Arora, Neha; Чурилов, Леонид; Zaslavsky, Arkady

doi:10.1109/hicss.2005.464

Cited by 17 publications

(20 citation statements)

References 5 publications

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“…For instance, iTriage [8] considers only 8 inputs for modeling a patient's condition while FMTS is more comprehensive and includes 49 reasons for arriving at an emergency center and 35 input linguistic variables for modeling symptoms related to each reason.…”

Section: Summary and Future Workmentioning

confidence: 99%

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FMTS: A fuzzy implementation of the Manchester triage system

Cremeens

Khorasani

2014

2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW)

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The Manchester triage System is an algorithmic standard designed to aid the triage nurse in choosing an appropriate triage category using a five-point scale. Many of the terms used in the Manchester triage algorithm to describe patients' symptoms and their urgency levels are imprecise and overlap, making the system a good candidate for fuzzy modeling. The paper develops a dynamic fuzzy triage system based on the Manchester triage algorithm. The system serves as a decision aid for the triage nurse while allowing medical experts to configure the meaning of the linguistic terms using a fuzzy mouse.

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Section: Summary and Future Workmentioning

confidence: 99%

“…iTriage [8] is an expert system that implements the Australian Triage Standard. iTriage benefits from an advanced user infterface and produces robust decisions for urgent scenarios.…”

Section: Related Workmentioning

confidence: 99%

FMTS: A fuzzy implementation of the Manchester triage system

Cremeens

Khorasani

2014

2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW)

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“…It should be done within a very short time approximately two to three minutes (San Pedro et al 2004) to sort the patients into the most appropriate assessment and treatment area. It is a process to categorize the casualties, based on their need for medical attention (Wilk et al 2005; Sadeghi et al 2006; Michalowski et al 2005).…”

Section: Introductionmentioning

confidence: 99%

“…There are many well-known advantages to use computerized tools and expert systems, such as reduction of missing data, better collection of data, no omission of questions, no data transcription and broader coverage of diagnoses. It is envisaged that by providing decision support tools to assist the triage officer in making correct and timely triage decisions that are consistent with standard triage scales can contribute to the improvement in the quality of life for patients and also reduce costs occurring from mistreatment (San Pedro et al 2004). …”

Section: Introductionmentioning

confidence: 99%

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department

et al. 2013

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Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient’s emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician’s burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department.

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“…One project in Australia, offering prototype mobile decision-support for hospital triage, uses linguistic terms such as immediately or imminently life-threatening, potentially life-threatening or lifeserious as well as physiological attributes including mild or moderate, pink or pale to help guide a clinician's decisions when a patient presents as an ambiguous triage case [10]. Another uses fuzzy logic and decision trees to make classification of a patient's urgency level in the shortest possible time with minimum error [9].…”

Section: Fuzzy Futurementioning

confidence: 99%

Fuzzy Logic in Health Care Settings: Moral Math for Value-Laden Choices

Voss¹

2016

jhummath

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SynopsisThis essay is intended as an example of "moral math", i.e., ideas culled from mathematics which can positively impact social behavior. Specifically, it combines fuzzy logic with the ethical decisions which hospital staff and others are sometimes forced to make about health care (e.g., euthanasia issues following Hurricane Katrina). The assumption is that such decisions involve value-laden choices which lend themselves to "fuzzy" or "smart" protocols. The article discusses the history of fuzzy logic -what it is, how it is used, and how it might be even better-used as a support basis for making difficult choices in the health care setting.

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On Development and Evaluation of Prototype Mobile Decision Support for Hospital Triage

Cited by 17 publications

References 5 publications

FMTS: A fuzzy implementation of the Manchester triage system

FMTS: A fuzzy implementation of the Manchester triage system

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department

Fuzzy Logic in Health Care Settings: Moral Math for Value-Laden Choices

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