A System for Mining Temporal Physiological Data Streams for Advanced Prognostic Decision Support

Sun, Jimeng; Sow, Daby; Hu, Jianying; Ebadollahi, Shahram

doi:10.1109/icdm.2010.102

Cited by 40 publications

(32 citation statements)

References 3 publications

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“…For example, Pechenizkiy et al analyze streams of recordings to predict whether a patient will need a re-hospitalization after a health failure treatment [7]. Patient monitoring is also the application area of [10], where the emphasis is on computing the similarity between streams of patients for prediction.…”

Section: Related Workmentioning

confidence: 98%

Can We Classify the Participants of a Longitudinal Epidemiological Study from Their Previous Evolution?

Niemann

Hielscher

Spiliopoulou

et al. 2015

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

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Medical research can greatly benefit from advances in data mining. We propose a mining approach for cohort analysis in a longitudinal population-based epidemiological study, and show that modeling and exploiting the evolution of cohort participants over time improves classification quality towards an outcome (a disease). Our mining workflow encompasses steps for tracing the evolution of the cohort participants and for using evolution features in classification. We show that our approach separates better between classes and that change in the values of variables is predictive. We report on results for the liver disorder hepatic steatosis (high fat accumulation in the liver), but our approach is appropriate for classification of longitudinal epidemiological data on further disorders.

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Section: Related Workmentioning

confidence: 98%

Can We Classify the Participants of a Longitudinal Epidemiological Study from Their Previous Evolution?

Niemann

Hielscher

Spiliopoulou

et al. 2015

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

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show abstract

“…Time is increasingly taken into account in the implementation of decision support systems [8] [63] [79]. It has become a critical dimension in decision-making.…”

Section: Visual Dynamic Dss Based On Kddmentioning

confidence: 99%

Combination of cognitive and HCI modeling for the design of KDD-based DSS used in dynamic situations

Ltifi

Kolski

Ayed

2015

Decision Support Systems

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“…Its exploration capabilities are also used for the mining of sensor data for the early detection of complications in neurological ICUs [29]. OHA has been extended with patient similarity concepts to help physicians take decisions while leveraging past experiences gathered from similar patients that have been monitored in the past [30]. In [31], the MI-TRA system, introduced as an extension of OHA, allows physicians to query for similar patients and use records from these similar patients to make predictions on the health evolution of a patient of interest.…”

Section: Systems For Data Mining In Intensive Carementioning

confidence: 99%

Mining of Sensor Data in Healthcare: A Survey

Sow¹,

Turaga²,

Schmidt

2012

Managing and Mining Sensor Data

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Historically, healthcare has been mainly provided in a reactive manner that limits its usefulness. With progress in sensor technologies, the instrumentation of the world has offered unique opportunities to better observe patients physiological signals in order to provide healthcare in a more proactive manner. To reach this goal, it is essential to be able to analyze patient data and turn it into actionable information using data mining. This chapter surveys existing applications of sensor data mining technologies in healthcare. It starts with a description of healthcare data mining challenges before presenting an overview of applications of data mining in both clinical and non clinical settings.

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A System for Mining Temporal Physiological Data Streams for Advanced Prognostic Decision Support

Cited by 40 publications

References 3 publications

Can We Classify the Participants of a Longitudinal Epidemiological Study from Their Previous Evolution?

Can We Classify the Participants of a Longitudinal Epidemiological Study from Their Previous Evolution?

Combination of cognitive and HCI modeling for the design of KDD-based DSS used in dynamic situations

Mining of Sensor Data in Healthcare: A Survey

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