J Biomed Inform - Predicting treatment process steps from events.

Tópicos

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Resumo

TIVATION: The primary economy-driven documentation of patient-specific information in clinical information systems leads to drawbacks in the use of these systems in daily clinical routine. Missing meta-data regarding underlying clinical workflows within the stored information is crucial for intelligent support systems. Unfortunately, there is still a lack of primary clinical needs-driven electronic patient documentation. Hence, physicians and surgeons must search hundreds of documents to find necessary patient data rather than accessing relevant information directly from the current process step. In this work, a completely new approach has been developed to enrich the existing information in clinical information systems with additional meta-data, such as the actual treatment phase from which the information entity originates.METHODS: Stochastic models based on Hidden Markov Models (HMMs) are used to create a mathematical representation of the underlying clinical workflow. These models are created from real-world anonymized patient data and are tailored to therapy processes for patients with head and neck cancer. Additionally, two methodologies to extend the models to improve the workflow recognition rates are presented in this work.RESULTS: A leave-one-out cross validation study was performed and achieved promising recognition rates of up to 90% with a standard deviation of 6.4%.CONCLUSIONS: The method presented in this paper demonstrates the feasibility of predicting clinical workflow steps from patient-specific information as the basis for clinical workflow support, as well as for the analysis and improvement of clinical pathways.

Resumo Limpo

tivat primari economydriven document patientspecif inform clinic inform system lead drawback use system daili clinic routin miss metadata regard under clinic workflow within store inform crucial intellig support system unfortun still lack primari clinic needsdriven electron patient document henc physician surgeon must search hundr document find necessari patient data rather access relev inform direct current process step work complet new approach develop enrich exist inform clinic inform system addit metadata actual treatment phase inform entiti originatesmethod stochast model base hidden markov model hmms use creat mathemat represent under clinic workflow model creat realworld anonym patient data tailor therapi process patient head neck cancer addit two methodolog extend model improv workflow recognit rate present workresult leaveoneout cross valid studi perform achiev promis recognit rate standard deviat conclus method present paper demonstr feasibl predict clinic workflow step patientspecif inform basi clinic workflow support well analysi improv clinic pathway

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