Using concept hierarchies to improve calculation of patient similarity.

Objective: We introduce a new distance measure that is better suited than traditional methods at detecting similarities in patient records by referring to a concept hierarchy.

Materials And Methods: The new distance measure improves on distance measures for categorical values by taking the path distance between concepts in a hierarchy into account. We evaluate and compare the new measure on a data set of 836 patients.

cFinder: definition and quantification of multiple haplotypes in a mixed sample.

Background: Next-generation sequencing allows for determining the genetic composition of a mixed sample. For instance, when performing resistance testing for BCR-ABL1 it is necessary to identify clones and define compound mutations; together with an exact quantification this may complement diagnosis and therapy decisions with additional information. Moreover, that applies not only to oncological issues but also determination of viral, bacterial or fungal infection.

A prospective study of mobile phones for dermatology in a clinical setting.

We evaluated the accuracy of diagnoses made from pictures taken with the built-in cameras of mobile phones in a 'real-life' clinical setting. A total of 263 patients took part, who photographed their own lesions where possible, and provided clinical information via a questionnaire. After the teledermatology procedure, each patient was examined face-to-face and a gold standard diagnosis was made.

Testing the calibration of classification models from first principles.

The accurate assessment of the calibration of classification models is severely limited by the fact that there is no easily available gold standard against which to compare a model's outputs. The usual procedures group expected and observed probabilities, and then perform a χ(2) goodness-of-fit test. We propose an entirely new approach to calibration testing that can be derived directly from the first principles of statistical hypothesis testing.

The CHOP postnatal weight gain, birth weight, and gestational age retinopathy of prematurity risk model.

Objective: To develop a birth weight (BW), gestational age (GA), and postnatal-weight gain retinopathy of prematurity (ROP) prediction model in a cohort of infants meeting current screening guidelines.

Methods: Multivariate logistic regression was applied retrospectively to data from infants born with BW less than 1501 g or GA of 30 weeks or less at a single Philadelphia hospital between January 1, 2004, and December 31, 2009. In the model, BW, GA, and daily weight gain rate were used repeatedly each week to predict risk of Early Treatment of Retinopathy of Prematurity type 1 or 2 ROP.

Differences in examination characteristics of pigmented skin lesions: results of an eye tracking study.

Objective: To use computer-based eye tracking technology to record and evaluate examination characteristics of the diagnosis of pigmented skin lesions.

Methodology: 16 study participants with varying levels of diagnostic expertise (little, intermediate, superior) were recorded while diagnosing a series of 28 digital images of pigmented skin lesions, obtained by non-invasive digital dermatoscopy, on a computer screen. Eye tracking hardware recorded the gaze track and fixations of the physicians while they examined the lesion images.

Effect of data combination on predictive modeling: a study using gene expression data.

Background: The quality of predictive modeling in biomedicine depends on the amount of data available for model building.

Objective: To study the effect of combining microarray data sets on feature selection and predictive modeling performance.

Methods: Empirical evaluation of stability of feature selection and discriminatory power of classifiers using three previously published gene expression data sets, analyzed both individually and in combination.

Outlier Detection with One-Class SVMs: An Application to Melanoma Prognosis.

Background: Medical diagnosis and prognosis using machine learning methods is usually represented as a supervised classification problem, where a model is built to distinguish "normal" from "abnormal" cases. If cases are available from only one class, this approach is not feasible.

Objective: To evaluate the performance of classification via outlier detection by one-class support vector machines (SVMs) as a means of identifying abnormal cases in the domain of melanoma prognosis.

A clinical prediction model to stratify retinopathy of prematurity risk using postnatal weight gain.

Objective: To develop an efficient clinical prediction model that includes postnatal weight gain to identify infants at risk of developing severe retinopathy of prematurity (ROP). Under current birth weight (BW) and gestational age (GA) screening criteria, <5% of infants examined in countries with advanced neonatal care require treatment.

Patients And Methods: This study was a secondary analysis of prospective data from the Premature Infants in Need of Transfusion Study, which enrolled 451 infants with a BW < 1000 g at 10 centers.

An evaluation of heuristics for rule ranking.

Objective: To evaluate and compare the performance of different rule-ranking algorithms for rule-based classifiers on biomedical datasets.

Methodology: Empirical evaluation of five rule ranking algorithms on two biomedical datasets, with performance evaluation based on ROC analysis and 5 × 2 cross-validation.

Results: On a lung cancer dataset, the area under the ROC curve (AUC) of, on average, 14267.

Computer versus human diagnosis of melanoma: evaluation of the feasibility of an automated diagnostic system in a prospective clinical trial.

The aim of this study was to evaluate the accuracy of a computer-based system for the automated diagnosis of melanoma in the hands of nonexpert physicians. We performed a prospective comparison between nonexperts using computer assistance and experts without assistance in the setting of a tertiary referral center at a University hospital. Between February and November 2004 we enrolled 511 consecutive patients.

Improving calibration of logistic regression models by local estimates.

Objective: To improve the calibration of logistic regression (LR) estimates using local information.

Background: Individualized risk assessment tools are increasingly being utilized. External validation of these tools often reveals poor model calibration.

A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry.

Motivation: Prostate cancer is the most prevalent tumor in males and its incidence is expected to increase as the population ages. Prostate cancer is treatable by excision if detected at an early enough stage. The challenges of early diagnosis require the discovery of novel biomarkers and tools for prostate cancer management.

Applying a decision support system in clinical practice: results from melanoma diagnosis.

The work reported in this paper investigates the use of a decision-support tool for the diagnosis of pigmented skin lesions in a real-world clinical trial with 511 patients and 3827 lesion evaluations. We analyzed a number of outcomes of the trial, such as direct comparison of system performance in laboratory and clinical setting, the performance of physicians using the system compared to a control dermatologist without the system, and repeatability of system recommendations. The results show that system performance was significantly less in the real-world setting compared to the laboratory setting (c-index of 0.

Approximation properties of haplotype tagging.

Background: Single nucleotide polymorphisms (SNPs) are locations at which the genomic sequences of population members differ. Since these differences are known to follow patterns, disease association studies are facilitated by identifying SNPs that allow the unique identification of such patterns. This process, known as haplotype tagging, is formulated as a combinatorial optimization problem and analyzed in terms of complexity and approximation properties.

Nomographic representation of logistic regression models: a case study using patient self-assessment data.

Logistic regression models are widely used in medicine, but difficult to apply without the aid of electronic devices. In this paper, we present a novel approach to represent logistic regression models as nomograms that can be evaluated by simple line drawings. As a case study, we show how data obtained from a questionnaire-based patient self-assessment study on the risks of developing melanoma can be used to first identify a subset of significant covariates, build a logistic regression model, and finally transform the model to a graphical format.

Do physicians value decision support? A look at the effect of decision support systems on physician opinion.

Objective: Clinical decision support systems are on the verge of becoming routine software tools in clinical settings. We investigate the question of how physicians react when faced with decision support suggestions that contradict their own diagnoses.

Methodology: We used a study design involving 52 volunteer dermatologists who each rated the malignancy of 25 lesion images on an ordinal scale and gave a dichotomous excise/no excise recommendation for each lesion image.

Logistic regression and artificial neural network classification models: a methodology review.

Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. We provide considerations useful for critically assessing the quality of the models and the results based on these models.