Search

Objective: Clinicians face numerous information needs during patient care activities and most of these needs are not met. Infobuttons are information retrieval tools that help clinicians to fulfill their information needs by providing links to on-line health information resources from within an electronic medical record (EMR) system. The aim of this study was to produce classification models based on medication infobutton usage data to predict the medication-related content topics (e.g., dose, adverse effects, drug interactions, patient education) that a clinician is most likely to choose while entering medication orders in a particular clinical context.Design: We prepared a dataset with 3078 infobutton sessions and 26 attributes describing characteristics of the user, the medication, and the patient. In these sessions, users selected one out of eight content topics. Automatic attribute selection methods were then applied to the dataset to eliminate redundant and useless attributes. The reduced dataset was used to produce nine classification models from a set of state-of-the-art machine learning algorithms. Finally, the performance of the models was measured and compared.Measurements: Area under the ROC curve (AUC) and agreement (kappa) between the content topics predicted by the models and those chosen by clinicians in each infobutton session.Results: The performance of the models ranged from 0.49 to 0.56 (kappa). The AUC of the best model ranged from 0.73 to 0.99. The best performance was achieved when predicting choice of the adult dose, pediatric dose, patient education, and pregnancy category content topics.Conclusion: The results suggest that classification models based on infobutton usage data are a promising method for the prediction of content topics that a clinician would choose to answer patient care questions while using an EMR system. [ABSTRACT FROM AUTHOR]

Outbreaks of infectious diseases such as influenza are a significant threat to human health. Because there are different strains of influenza which can cause independent outbreaks, and influenza can affect demographic groups at different rates and times, there is a need to recognize and characterize multiple outbreaks of influenza. This paper describes a Bayesian system that uses data from emergency department patient care reports to create epidemiological models of overlapping outbreaks of influenza. Clinical findings are extracted from patient care reports using natural language processing. These findings are analyzed by a case detection system to create disease likelihoods that are passed to a multiple outbreak detection system. We evaluated the system using real and simulated outbreaks. The results show that this approach can recognize and characterize overlapping outbreaks of influenza. We describe several extensions that appear promising., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies., Method: We initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s)., Results: Simple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvement beyond that using biomedical-literature features alone (p-value<0.001). The performance of the classifier built from known diseases to predict associated concepts for new diseases showed no significant difference from the performance of the classifier built and tested using the new disease's dataset., Conclusion: It is feasible to use classification approaches to automatically predict the relevance of a concept to a disease of interest. It is useful to combine features from disparate sources for the task of classification. Classifiers built from known diseases were generalizable to new diseases., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Objective: Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies., Methods: A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches., Results: The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines., Conclusions: The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature., (Copyright © 2016 Elsevier Inc. All rights reserved.)

The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation's health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation's many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or 'liquidity' of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed., (Copyright © 2012 Elsevier Inc. All rights reserved.)

The family health history has long been recognized as an effective way of understanding individuals' susceptibility to familial disease; yet electronic tools to support the capture and use of these data have been characterized as inadequate. As part of an ongoing effort to build patient-facing tools for entering detailed family health histories, we have compiled a set of concepts specific to familial disease using multi-source sampling. These concepts were abstracted by analyzing family health history data patterns in our enterprise data warehouse, collection patterns of consumer personal health records, analyses from the local state health department, a healthcare data dictionary, and concepts derived from genetic-oriented consumer education materials. Collectively, these sources yielded a set of more than 500 unique disease concepts, represented by more than 2500 synonyms for supporting patients in entering coded family health histories. We expect that these concepts will be useful in providing meaningful data and education resources for patients and providers alike.

Study Objective: The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models., Methods: Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources., Results: Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources., Conclusion: Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Objective: Infobuttons are decision support tools that offer links to information resources based on the context of the interaction between a clinician and an electronic medical record (EMR) system. The objective of this study was to explore machine learning and web usage mining methods to produce classification models for the prediction of information resources that might be relevant in a particular infobutton context., Design: Classification models were developed and evaluated with an infobutton usage dataset. The performance of the models was measured and compared with a reference implementation in a series of experiments., Measurements: Level of agreement (kappa) between the models and the resources that clinicians actually used in each infobutton session., Results: The classification models performed significantly better than the reference implementation (p<.0001). The performance of these models tended to decrease over time, probably due to a phenomenon known as concept drift. However, the performance of the models remained stable when concept drift handling techniques were used., Conclusions: The results suggest that classification models are a promising method for the prediction of information resources that a clinician would use to answer patient care questions.

When machine learning algorithms are applied to data collected during the course of clinical care, it is generally accepted that the data has not been consistently collected. The absence of expected data elements is common and the mechanism through which a data element is missing often involves the clinical relevance of that data element in a specific patient. Therefore, the absence of data may have information value of its own. In the process of designing an application intended to support a medical problem list, we have studied whether the "missingness" of clinical data can provide useful information in building prediction models. In this study, we experimented with four methods of treating missing values in a clinical data set-two of them explicitly model the absence or "missingness" of data. Each of these data sets were used to build four different kinds of Bayesian classifiers-a naive Bayes structure, a human-composed network structure, and two networks based on structural learning algorithms. We compared the performance between groups with and without explicit models of missingness using the area under the ROC curve. The results showed that in most cases the classifiers trained using the explicit missing value treatments performed better. The result suggests that information may exist in "missingness" itself. Thus, when designing a decision support system, we suggest one consider explicitly representing the presence/absence of data in the underlying logic.

In this study, we evaluate the performance of a Natural Language Processing (NLP) application designed to extract medical problems from narrative text clinical documents. The documents come from a patient's electronic medical record and medical problems are proposed for inclusion in the patient's electronic problem list. This application has been developed to help maintain the problem list and make it more accurate, complete, and up-to-date. The NLP part of this system-analyzed in this study-uses the UMLS MetaMap Transfer (MMTx) application and a negation detection algorithm called NegEx to extract 80 different medical problems selected for their frequency of use in our institution. When using MMTx with its default data set, we measured a recall of 0.74 and a precision of 0.756. A custom data subset for MMTx was created, making it faster and significantly improving the recall to 0.896 with a non-significant reduction in precision.

We compared the performance of expert-crafted rules, a Bayesian network, and a decision tree at automatically identifying chest X-ray reports that support acute bacterial pneumonia. We randomly selected 292 chest X-ray reports, 75 (25%) of which were from patients with a hospital discharge diagnosis of bacterial pneumonia. The reports were encoded by our natural language processor and then manually corrected for mistakes. The encoded observations were analyzed by three expert systems to determine whether the reports supported pneumonia. The reference standard for radiologic support of pneumonia was the majority vote of three physicians. We compared (a) the performance of the expert systems against each other and (b) the performance of the expert systems against that of four physicians who were not part of the gold standard. Output from the expert systems and the physicians was transformed so that comparisons could be made with both binary and probabilistic output. Metrics of comparison for binary output were sensitivity (sens), precision (prec), and specificity (spec). The metric of comparison for probabilistic output was the area under the receiver operator characteristic (ROC) curve. We used McNemar's test to determine statistical significance for binary output and univariate z-tests for probabilistic output. Measures of performance of the expert systems for binary (probabilistic) output were as follows: Rules--sens, 0.92; prec, 0.80; spec, 0.86 (Az, 0.960); Bayesian network--sens, 0.90; prec, 0.72; spec, 0.78 (Az, 0.945); decision tree--sens, 0.86; prec, 0.85; spec, 0.91 (Az, 0.940). Comparisons of the expert systems against each other using binary output showed a significant difference between the rules and the Bayesian network and between the decision tree and the Bayesian network. Comparisons of expert systems using probabilistic output showed no significant differences. Comparisons of binary output against physicians showed differences between the Bayesian network and two physicians. Comparisons of probabilistic output against physicians showed a difference between the decision tree and one physician. The expert systems performed similarly for the probabilistic output but differed in measures of sensitivity, precision, and specificity produced by the binary output. All three expert systems performed similarly to physicians.