A case-based reasoning system for aiding detection and classification of nosocomial infections

Gómez-Vallejo, H.J.; Uriel-Latorre, B.; Sande-Meijide, María; Villamarín-Bello, Beatriz; Pavón, Reyes; Fdez-Riverola, Florentino; Glez-Peña, Daniel

doi:10.1016/j.dss.2016.02.005

Cited by 51 publications

(21 citation statements)

References 25 publications

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“…InNoCBR is able to detect and classify HAIs of multiple types including urinary, respiratory, bloodstream, surgical site, cutaneous, enteric, and other type. The system was described and partially validated in [10], but only with those cases that were automatically gathered with an acquisition process inside InNoCBR. In this sense, validation in [10] focused in the ability of InNoCBR to correctly learn from the user (expert) behavior when classifying the correct type of HAI of a suspicious case (by means of Machine Learning techniques).…”

Section: Introductionmentioning

confidence: 99%

“…The system was described and partially validated in [10], but only with those cases that were automatically gathered with an acquisition process inside InNoCBR. In this sense, validation in [10] focused in the ability of InNoCBR to correctly learn from the user (expert) behavior when classifying the correct type of HAI of a suspicious case (by means of Machine Learning techniques). In this sense, in [10] we could not, for example, assess false negatives i.e.,: HAI cases that were not acquired.…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, validation in [10] focused in the ability of InNoCBR to correctly learn from the user (expert) behavior when classifying the correct type of HAI of a suspicious case (by means of Machine Learning techniques). In this sense, in [10] we could not, for example, assess false negatives i.e.,: HAI cases that were not acquired. Moreover, the “gold standard” used in [10] was the InNoCBR user classifications by seeing the patient information gathered by InNoCBR.…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, in [10] we could not, for example, assess false negatives i.e.,: HAI cases that were not acquired. Moreover, the “gold standard” used in [10] was the InNoCBR user classifications by seeing the patient information gathered by InNoCBR.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gold Standard Evaluation of an Automatic HAIs Surveillance System

Villamarín-Bello

Uriel-Latorre

Fdez-Riverola

et al. 2019

BioMed Research International

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Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gold Standard Evaluation of an Automatic HAIs Surveillance System

Villamarín-Bello

Uriel-Latorre

Fdez-Riverola

et al. 2019

BioMed Research International

Self Cite

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show abstract

“…Much work done to address the challenge of effective prediction of nosocomial infections. Several techniques using rule-based approaches [18–20], Bayesian Networks [10, 21], Ontologies [22], landmark competing risk prediction models [23], statistical models [24–27], case based reasoning [28, 29], and others [30–33] have been suggested and implemented in the past. The problem of identifying new, unanticipated, and useful patterns in public health surveillance and hospital infection control data is discussed in [19].…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction Accuracy of “Central Line-Associated Blood Stream Infections” Using Data Mining Models

Noaman

Nadeem

Ragab

et al. 2017

BioMed Research International

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Prediction of nosocomial infections among patients is an important part of clinical surveillance programs to enable the related personnel to take preventive actions in advance. Designing a clinical surveillance program with capability of predicting nosocomial infections is a challenging task due to several reasons, including high dimensionality of medical data, heterogenous data representation, and special knowledge required to extract patterns for prediction. In this paper, we present details of six data mining methods implemented using cross industry standard process for data mining to predict central line-associated blood stream infections. For our study, we selected datasets of healthcare-associated infections from US National Healthcare Safety Network and consumer survey data from Hospital Consumer Assessment of Healthcare Providers and Systems. Our experiments show that central line-associated blood stream infections (CLABSIs) can be successfully predicted using AdaBoost method with an accuracy up to 89.7%. This will help in implementing effective clinical surveillance programs for infection control, as well as improving the accuracy detection of CLABSIs. Also, this reduces patients' hospital stay cost and maintains patients' safety.

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A Task-Oriented Dialogue System for Moral Education

Yan

Chen

et al. 2019

Lecture Notes in Computer Science

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A case-based reasoning system for aiding detection and classification of nosocomial infections

Cited by 51 publications

References 25 publications

Gold Standard Evaluation of an Automatic HAIs Surveillance System

Gold Standard Evaluation of an Automatic HAIs Surveillance System

Improving Prediction Accuracy of “Central Line-Associated Blood Stream Infections” Using Data Mining Models

A Task-Oriented Dialogue System for Moral Education

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