Taxonomy development and knowledge representation of nurses' personal cognitive artifacts.

Nurses prepare knowledge representations, or summaries of patient clinical data, each shift. These knowledge representations serve multiple purposes, including support of working memory, workload organization and prioritization, critical thinking, and reflection. This summary is integral to internal knowledge representations, working memory, and decision-making. Study of this nurse knowledge representation resulted in development of a taxonomy of knowledge representations necessary to nursing practice.This paper describes the methods used to elicit the knowledge representations and structures necessary for the work of clinical nurses, described the development of a taxonomy of this knowledge representation, and discusses translation of this methodology to the cognitive artifacts of other disciplines. Understanding the development and purpose of practitioner's knowledge representations provides important direction to informaticists seeking to create information technology alternatives. The outcome of this paper is to suggest a process template for transition of cognitive artifacts to an information system.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Knowledge, Attitudes, and Utilization of BRCA Testing Among Obstetricians and Gynecologists

Hereditary breast and ovarian cancer (HBOC) is an inherited cancer syndrome that is associated with mutations in the BRCA1 and BRCA2 genes. Carriers of BRCA mutations, both men and women, are at an increased risk for developing certain cancers. Carriers are most notably at an increased risk to develop breast and ovarian cancers; however an increased risk for prostate cancer, melanoma, and pancreatic cancers has also been associated with these mutations. In 2009 the American Congress of Obstetricians and Gynecologists (ACOG) released a practice bulletin stating that evaluating a patient’s risk for HBOC should be a routine part of obstetric and gynecologic practice. A survey was created and completed by 83 obstetricians and gynecologists in the greater Houston, TX area. The survey consisted of four sections designed to capture demographic information, attitudes towards HBOC and BRCA testing, utilization of BRCA testing, and the overall knowledge of respondents with regards to HBOC and BRCA testing. This study found that the majority of participants indicated that they felt that obstetricians and gynecologists should have the primary responsibility of identifying patients who may be at increased risk of carrying a BRCA mutation. Moreover, this study found that the majority of participants indicated that they felt comfortable or very comfortable in identifying patients at an increased risk of carrying a BRCA mutation. However, only about a quarter of participants indicated that they order BRCA genetic testing one to two times per month or more. Lastly, this study demonstrates that the overall knowledge of HBOC and BRCA testing among this population of obstetricians and gynecologists is poor. The results of this study stress the need for more education regarding HBOC, genetic testing, and strategies for identifying patients that may be at risk for having a mutation in a BRCA gene. Furthermore, it reiterates the importance of raising awareness to current practice guidelines and recommendations that can assist obstetricians and gynecologist to better identify and manage patients that may be at an increased risk of having HBOC.