An automated technique for identifying associations between medications, laboratory results and problems

Wright, Adam; Chen, Elizabeth; Maloney, Francine L.

doi:10.1016/j.jbi.2010.09.009

Cited by 119 publications

(106 citation statements)

References 21 publications

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“…Such transitive relationships frequently occur in EHR data mining. 39 We can see this in our network: 'sickle cell disease' and 'friends use drugs' are connected, probably because both are more common in our African-American population. Although it is computationally intractable to learn networks of more than a few hundred variables, feature selection can be used on a broad dataset to choose relevant contextual variables for the structure-learning algorithm.…”

Section: Limitations and Future Directionsmentioning

confidence: 89%

Patient-tailored prioritization for a pediatric care decision support system through machine learning

Klann

Anand

Downs

2013

J Am Med Inform Assoc

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Objective Over 8 years, we have developed an innovative computer decision support system that improves appropriate delivery of pediatric screening and care. This system employs a guidelines evaluation engine using data from the electronic health record (EHR) and input from patients and caregivers. Because guideline recommendations typically exceed the scope of one visit, the engine uses a static prioritization scheme to select recommendations. Here we extend an earlier idea to create patient-tailored prioritization. Materials and methods We used Bayesian structure learning to build networks of association among previously collected data from our decision support system. Using area under the receiver-operating characteristic curve (AUC) as a measure of discriminability (a sine qua non for expected value calculations needed for prioritization), we performed a structural analysis of variables with high AUC on a test set. Our source data included 177 variables for 29 402 patients. Results The method produced a network model containing 78 screening questions and anticipatory guidance (107 variables total). Average AUC was 0.65, which is sufficient for prioritization depending on factors such as population prevalence. Structure analysis of seven highly predictive variables reveals both face-validity (related nodes are connected) and non-intuitive relationships. Discussion We demonstrate the ability of a Bayesian structure learning method to 'phenotype the population' seen in our primary care pediatric clinics. The resulting network can be used to produce patient-tailored posterior probabilities that can be used to prioritize content based on the patient's current circumstances. Conclusions This study demonstrates the feasibility of EHR-driven population phenotyping for patient-tailored prioritization of pediatric preventive care services. BACKGROUND AND SIGNIFICANCE

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Section: Limitations and Future Directionsmentioning

confidence: 89%

Patient-tailored prioritization for a pediatric care decision support system through machine learning

Klann

Anand

Downs

2013

J Am Med Inform Assoc

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“…DSS can thus more actively collaborate with physicians in order to provide advice on diagnosis, drug interactions or on imaging [1,2, 3,5,12].…”

Section: Introductionmentioning

confidence: 99%

Decision Support: Time for Collaboration

Guardia¹

2011

Yearb Med Inform

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SummaryObjectives: To summarize current outstanding research in the field of decision support. Methods: A selection of excellent research articles published in 2010 in the field of computerized clinical decision support systems. Results and Conclusions: This selection of articles shows that decision support systems (DSS) are getting better integrated into the electronic health record systems (EHR) and into the clinician's workflow. As a result, there is a better collaboration between physicians and DSS, which improves the care of patients.

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“…The literature contains a number of standard class association rule mining solutions in biomedical domain to discover patterns in medical data [90][91][92][93][94]. For instance, Yang et al [95] adopt Apriori and support and confidence as the main underlying measures to mine meaningful patterns.…”

Section: Discussion and Implications Of Findingsmentioning

confidence: 99%

Decision support methods for community-driven knowledge curation platforms in the biomedical domain

Paul¹

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The skeletal dysplasia domain is characterised by highly complex, heterogeneous and sparse data.In this domain, the analysis and interpretation of new patient cases relies on comparisons to past case studies due to the absence of defined guidelines and the lack of mature domain knowledge for this group of genetic disorders. In order to carry out the analysis and interpretation of new patient cases, practitioners currently query many heterogeneous data sources and aggregate diverse types of data. This integration represents a significant challenge due to the extreme heterogeneity of the data models, metadata schemas, vocabularies and data formats and inconsistencies in naming and identification conventions. Therefore, there is an urgent need to investigate the development of medical decision support systems which can assist clinicians and researchers to improve their understanding of the causes, behaviours, symptoms and attributes of the diseases and assist them in the decision-making process (e.g., diagnosis).In the skeletal dysplasia domain, the absence of mature domain knowledge and the lack of documented and well-structured past cases, in addition to the general sparseness of skeletal dysplasia data, hinder the development of reliable decision support methods. In this thesis, the first step is taken towards developing a decision support framework in order to assist clinicians, doctors and researchers in this domain.The developed framework is a phenotype-disorder lifecycle to assist practitioners in finalising patient cases by going from phenotypes to disorders and vice versa. The framework was developed in four phases and combines ontological techniques with inductive and statistical reasoning techniques. In the initial phase, the underlying data characteristics were analyzed and a novel machine learning approach was developed to produce probabilistic candidate rankings, which can serve as support for medical decision-making (e.g., diagnosis). In the second phase, the semantics encoded in the domain were exploited in order to find possible disorders for a new patient case by developing a semantic similarity-based approach. In the third phase, to discover the implicit relationships between different ontological concepts (e.g., phenotypes and disorders), semantic similarity methods, formulated using the intrinsic structure of a given ontology, were combined with traditional interestingness measures in the process of discovering association rules. The final phase proposes a data mining approach for discovering characteristic features in the context of a set of disorders.From the data and domain knowledge perspective, the developed approaches and evaluation strategies led to a number of significant findings towards building a fully-fledged decision support framework; (i) the properties of data in the domain are rareness, sparseness, and high dimensionality; (ii) the domain knowledge introduced more noise compared to the noise produced ii by patient cases; and (iii) semantic similarity improves the overall...

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An automated technique for identifying associations between medications, laboratory results and problems

Abstract: Association rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches.

Cited by 119 publications

References 21 publications

Patient-tailored prioritization for a pediatric care decision support system through machine learning

Patient-tailored prioritization for a pediatric care decision support system through machine learning

Decision Support: Time for Collaboration

Decision support methods for community-driven knowledge curation platforms in the biomedical domain

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