Efficient temporal probabilistic reasoning via context-sensitive model construction

Ngo, Liem Viet; Haddawy, Peter; Krieger, Robert A.; Helwig, James

doi:10.1016/s0010-4825(97)00015-2

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…The Bayesian network in Kahn et al 24 used information from the patients' histories, such as the physical exam and mammography results, and the conditional probabilities were obtained by reviewing the indexed medical literature, census data and statistical health reports, i.e., conditional probabilities for architectural distortion, previous biopsy at the same site were estimated as well; values for demographic variables were derived from published epidemiological data; and statistical studies published in peerreviewed radiology journals provided most of the data for knowledge base, such as values of conditional probabilities findings and mammographic findings for breast cancer. The software used for learning conditional probabilities was BNG 26 , and IDEAL was used for inference 27 .…”

Section: Identification Of Studies and Eligibilitymentioning

confidence: 99%

Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

et al. 2015

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Section: Identification Of Studies and Eligibilitymentioning

confidence: 99%

Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

et al. 2015

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“…Haddawy proposed a provably correct BN generation algorithm that was later adapted by Ngo and Haddawy [19,20] to focus the knowledge base on the relevant information. In particular, they used logic expressions as contextual constraints for indexing the probabilistic information to reduce the size of the modeling network.…”

Section: Related Workmentioning

confidence: 99%

Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling

Sakhanenko

Luger²

2008

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We propose a context-sensitive probabilistic modeling system (COSMOS) that reasons about a complex, dynamic environment through a series of applications of smaller, knowledge-focused models representing contextually relevant information. COSMOS uses a failure-driven architecture to determine whether a context is supported, and consequently whether the current model remains applicable. The individual models are specified through sets of structured, hierarchically organized probabilistic logic statements using transfer functions that are then mapped into a representation supporting stochastic inferencing. We demonstrate COSMOS using data from a mechanical pump system.

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“…Here we briefly list some temporal reasoning methods in medicine (see [6] for a extensive bibliography) that are similar to REMIND in some aspects. Ngo et al [23] describe a temporal probabilistic reasoning method via context-sensitive model construction. Bellazi et al [2] describe a system that uses a Dynamic Bayesian Network to analyze the blood glucose level of a patient over a time interval.…”

Section: Review Of Related Workmentioning

confidence: 99%

Clinical and financial outcomes analysis with existing hospital patient records

Rao¹,

Sandilya²,

Niculescu

et al. 2003

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Existing patient records are a valuable resource for automated outcomes analysis and knowledge discovery. However, key clinical data in these records is typically recorded in unstructured form as free text and images, and most structured clinical information is poorly organized. Time-consuming interpretation and analysis is required to convert these records into structured clinical data. Thus, only a tiny fraction of this resource is utilized. We present REMIND, a Bayesian Framework for Reliable Extraction and Meaningful Inference from Nonstructured Data. REMIND integrates and blends the structured and unstructured clinical data in patient records to automatically created highquality structured clinical data. This structuring allows existing patient records to be mined for quality assurance, regulatory compliance, and to relate financial and clinical factors. We demonstrate REMIND on two medical applications: (a) Extract "recurrence", the key outcome for measuring treatment effectiveness, for colon cancer patients (ii) Extract key diagnoses and complications for acute myocardial infarction (heart attack) patients, and demonstrate the impact of these clinical factors on financial outcomes.

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Efficient temporal probabilistic reasoning via context-sensitive model construction

Cited by 13 publications

References 9 publications

Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

Metanálise do uso de redes bayesianas no diagnóstico de câncer de mama

Using Structured Knowledge Representation for Context-Sensitive Probabilistic Modeling

Clinical and financial outcomes analysis with existing hospital patient records

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