Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer

The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming (ILP) and conditional probabilities, and validate this association in an independent dataset. We ran our ILP algorithm on 62,219 mammographic abnormalities. We set the Aleph ILP system to generate 10,000 rules per malignant finding with a recall >5% and precision >25%. Aleph reported the best rule for each malignant finding. A total of 80 unique rules were learned. A radiologist reviewed all rules and identified potentially interesting rules. High breast mass density appeared in 24% of the learned rules. We confirmed each interesting rule by calculating the probability of malignancy given each mammographic descriptor. High mass density was the fifth highest ranked predictor. To validate the association between mass density and malignancy in an independent dataset, we collected data from 180 consecutive breast biopsies performed between 2005 and 2007. We created a logistic model with benign or malignant outcome as the dependent variable while controlling for potentially confounding factors. We calculated odds ratios based on dichomotized variables. In our logistic regression model, the independent predictors high breast mass density (OR 6.6, CI 2.5–17.6), irregular mass shape (OR 10.0, CI 3.4–29.5), spiculated mass margin (OR 20.4, CI 1.9–222.8), and subject age (β = 0.09, p < 0.0001) significantly predicted malignancy. Both ILP and conditional probabilities show that high breast mass density is an important adjunct predictor of malignancy, and this association is confirmed in an independent data set of prospectively collected mammographic findings

The Contextual Approach in Health Research: Two Empirical Studies

Researchers are being encouraged to consider contextual influences on health-related outcomes. To support this perspective, two context-sensitive studies were conducted. The first study explored the utilization of a research report by Ontario public health units, and examined whether utilization differed by involvement in the research process. Research utilization was conceptualized as a three stage process (reading, information processing and application). Using a case study design, results from three involved public health units and three uninvolved units demonstrated that inclusion in the research process led to a greater understanding of the analysis and increased the value associated with the report. Involvement did not, however, lead to greater research utilization. An associated contextual analysis provided a rich backdrop, highlighting the general challenges of implementing research-based guidelines given front-line workers\u27 current realities. The second study examined the influence of contextual level (e.g., health region level) socioeconomic status on a woman\u27s lifetime mammography screening uptake. A secondary data analysis was conducted using Ontario data from the 1996 National Population Health Survey. Logistic hierarchical multilevel modelling was used to examine the regional variation in mammography uptake, and to examine the role of contextual and individual level variables on regional variation. The estimated average proportion of Ontario women, aged 50-69, who reported ever having had a mammogram was 0.86. Results demonstrated modest variations among health regions in ever having had a mammogram. These variations could not be explained by the variables considered in this study. Individual level variables demonstrated an association with mammography uptake, as did regional level education and regional median family income. Furthermore, each of these latter two contextual variables demonstrated interaction effects with the individual level variable, social involvement. Thus, contextual variables played a significant role in mammography uptake. Contextual circumstances ought to be considered during the development of breast health promotion programs and policies

DigiScope Collector - Unobtrosive collection and annotating of auscultations in real hospital environments

Mestrado em Informática MédicaMaster Programme in Medical Informatic

Classifiers for modeling of mineral potential

[Extract] Classification and allocation of land-use is a major policy objective in most countries. Such an undertaking, however, in the face of competing demands from different stakeholders, requires reliable information on resources potential. This type of information enables policy decision-makers to estimate socio-economic benefits from different possible land-use types and then to allocate most suitable land-use. The potential for several types of resources occurring on the earth's surface (e.g., forest, soil, etc.) is generally easier to determine than those occurring in the subsurface (e.g., mineral deposits, etc.). In many situations, therefore, information on potential for subsurface occurring resources is not among the inputs to land-use decision-making [85]. Consequently, many potentially mineralized lands are alienated usually to, say, further exploration and exploitation of mineral deposits. Areas with mineral potential are characterized by geological features associated genetically and spatially with the type of mineral deposits sought. The term 'mineral deposits' means .accumulations or concentrations of one or more useful naturally occurring substances, which are otherwise usually distributed sparsely in the earth's crust. The term 'mineralization' refers to collective geological processes that result in formation of mineral deposits. The term 'mineral potential' describes the probability or favorability for occurrence of mineral deposits or mineralization. The geological features characteristic of mineralized land, which are called recognition criteria, are spatial objects indicative of or produced by individual geological processes that acted together to form mineral deposits. Recognition criteria are sometimes directly observable; more often, their presence is inferred from one or more geographically referenced (or spatial) datasets, which are processed and analyzed appropriately to enhance, extract, and represent the recognition criteria as spatial evidence or predictor maps. Mineral potential mapping then involves integration of predictor maps in order to classify areas of unique combinations of spatial predictor patterns, called unique conditions [51] as either barren or mineralized with respect to the mineral deposit-type sought

Clinical information extraction for preterm birth risk prediction

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records