Stopping Rules for Computer Adaptive Testing When Item Banks Have Nonuniform Information.

The () stopping rule, which terminates a (CAT) when the SE is less than a threshold, is effective when there are informative questions for all trait levels. However, in domains such as patient reported outcomes, the items in a bank might all target one end of the trait continuum (e.g.

Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis.

Background: Even though we have established a few risk factors for metastatic breast cancer (MBC) through epidemiologic studies, these risk factors have not proven to be effective in predicting an individual's risk of developing metastasis. Therefore, identifying critical risk factors for MBC continues to be a major research imperative, and one which can lead to advances in breast cancer clinical care. The objective of this research is to leverage Bayesian Networks (BN) and information theory to identify key risk factors for breast cancer metastasis from data.

A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis.

Objective: A Clinical Decision Support System (CDSS) that can amass Electronic Health Record (EHR) and other patient data holds promise to provide accurate classification and guide treatment choices. Our objective is to develop the Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer Patients (DPAC), which is a CDSS learned from data that recommends the optimal treatment decisions based on a patient's features.

Method: We developed a Bayesian network architecture called Causal Modeling with Internal Layers (CAMIL), and an algorithm called Treatment Feature Interactions (TFI), which learns from data the interactions needed in a CAMIL model.

Using natural language processing and machine learning to identify breast cancer local recurrence.

Background: Identifying local recurrences in breast cancer from patient data sets is important for clinical research and practice. Developing a model using natural language processing and machine learning to identify local recurrences in breast cancer patients can reduce the time-consuming work of a manual chart review.

Methods: We design a novel concept-based filter and a prediction model to detect local recurrences using EHRs.

Conjugated equine estrogen and medroxyprogesterone acetate are associated with decreased risk of breast cancer relative to bioidentical hormone therapy and controls.

Objective: By the 1990s it became popular for women to use hormone therapy (HT) to ease menopause symptoms. Bioidentical estrogen and progesterone are supplements whose molecular structures are identical to what is made in the human body, while synthetic supplements are ones whose structures are not. After the Women's Health Initiative found that the combined use of the synthetics conjugated equine estrogen (CEE) and medroxyprogesterone acetate (MPA) increased breast cancer risk, prescriptions for synthetic HT declined considerably.

Advancing the efficiency and efficacy of patient reported outcomes with multivariate computer adaptive testing.

Objective: The Patient Reported Outcomes Measurement Information System (PROMIS) initiative developed an array of patient reported outcome (PRO) measures. To reduce the number of questions administered, PROMIS utilizes unidimensional item response theory and unidimensional computer adaptive testing (UCAT), which means a separate set of questions is administered for each measured trait. Multidimensional item response theory (MIRT) and multidimensional computer adaptive testing (MCAT) simultaneously assess correlated traits.

Discovering causal interactions using Bayesian network scoring and information gain.

Background: The problem of learning causal influences from data has recently attracted much attention. Standard statistical methods can have difficulty learning discrete causes, which interacting to affect a target, because the assumptions in these methods often do not model discrete causal relationships well. An important task then is to learn such interactions from data.

A Primer on Bayesian Decision Analysis With an Application to a Kidney Transplant Decision.

A clinical decision support system (CDSS) is a computer program, which is designed to assist health care professionals with decision making tasks. A well-developed CDSS weighs the benefits of therapy versus the cost in terms of loss of quality of life and financial loss and recommends the decision that can be expected to provide maximum overall benefit. This article provides an introduction to developing CDSSs using Bayesian networks, such CDSS can help with the often complex decisions involving transplants.

Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.

Background: The problems of correlation and classification are long-standing in the fields of statistics and machine learning, and techniques have been developed to address these problems. We are now in the era of high-dimensional data, which is data that can concern billions of variables. These data present new challenges.

Pan-cancer analysis of TCGA data reveals notable signaling pathways.

Background: A signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is important to discover which STPs are implicated in a cancer or cancer-subtype.

Evaluation of a two-stage framework for prediction using big genomic data.

We are in the era of abundant 'big' or 'high-dimensional' data. These data afford us the opportunity to discover predictors of an event of interest, and to estimate occurrence of the event based on values of these predictors. For example, 'genome-wide association studies' examine millions of single-nucleotide polymorphisms (SNPs), along with disease status.

Study of integrated heterogeneous data reveals prognostic power of gene expression for breast cancer survival.

Background: Studies show that thousands of genes are associated with prognosis of breast cancer. Towards utilizing available genetic data, efforts have been made to predict outcomes using gene expression data, and a number of commercial products have been developed. These products have the following shortcomings: 1) They use the Cox model for prediction.

LEAP: biomarker inference through learning and evaluating association patterns.

Single nucleotide polymorphism (SNP) high-dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease.

Utilizing Multidimensional Computer Adaptive Testing to Mitigate Burden With Patient Reported Outcomes.

Utilization of patient-reported outcome measures (PROs) had been limited by the lack of psychometrically sound measures scored in real-time. The Patient Reported Outcomes Measurement Information System (PROMIS) initiative developed a broad array of high-quality PRO measures. Towards reducing the number of items administered in measuring PROs, PROMIS employs Item Response Theory (IRT) and Computer Adaptive Testing (CAT).

Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data.

This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community.

Modeling the altered expression levels of genes on signaling pathways in tumors as causal bayesian networks.

This paper concerns a study indicating that the expression levels of genes in signaling pathways can be modeled using a causal Bayesian network (BN) that is altered in tumorous tissue. These results open up promising areas of future research that can help identify driver genes and therapeutic targets. So, it is most appropriate for the cancer informatics community.

A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.

Objective: The objective of this investigation is to evaluate binary prediction methods for predicting disease status using high-dimensional genomic data. The central hypothesis is that the Bayesian network (BN)-based method called efficient Bayesian multivariate classifier (EBMC) will do well at this task because EBMC builds on BN-based methods that have performed well at learning epistatic interactions.

Method: We evaluate how well eight methods perform binary prediction using high-dimensional discrete genomic datasets containing epistatic interactions.

A new method for predicting patient survivorship using efficient bayesian network learning.

The purpose of this investigation is to develop and evaluate a new Bayesian network (BN)-based patient survivorship prediction method. The central hypothesis is that the method predicts patient survivorship well, while having the capability to handle high-dimensional data and be incorporated into a clinical decision support system (CDSS). We have developed EBMC_Survivorship (EBMC_S), which predicts survivorship for each year individually.

Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.

Background: The interaction between loci to affect phenotype is called epistasis. It is strict epistasis if no proper subset of the interacting loci exhibits a marginal effect. For many diseases, it is likely that unknown epistatic interactions affect disease susceptibility.

Learning genetic epistasis using Bayesian network scoring criteria.

Background: Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR).

A fast algorithm for learning epistatic genomic relationships.

Genetic epidemiologists strive to determine the genetic profile of diseases. Epistasis is the interaction between two or more genes to affect phenotype. Due to the often non-linearity of the interaction, it is difficult to detect statistical patterns of epistasis.