Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Discourse-level Annotation over Europarl for Machine Translation: Connectives and Pronouns

This paper describes methods and results for the annotation of two discourse-level phenomena, connectives and pronouns, over a multilingual parallel corpus. Excerpts from Europarl in English and French have been annotated with disambiguation information for connectives and pronouns, for about 3600 tokens. This data is then used in several ways: for cross-linguistic studies, for training automatic disambiguation software, and ultimately for training and testing discourse-aware statistical machine translation systems. The paper presents the annotation procedures and their results in detail, and overviews the first systems trained on the annotated resources and their use for machine translation.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

Demographics of having a gun in the home in Columbia, SC as reported by women primary care patients

Objectives. To determine demographic correlates of having one or more guns in the household of women primary care patients in the southern USA. ^ Methods. All participants in this cross-sectional study were women aged 18-65 who were insured by either Medicaid or a managed care provider and had ever had an intimate sexual relationship with a male partner that lasted at least three months. Prevalence rate ratios and 95% confidence intervals were calculated using stratified analyses for having a gun in the home and the following demographic factors: age, race, educational attainment, marital status, employment status, and alcohol/drug use. ^ Results. Twenty six percent of households had at least one gun and 6.5% had 3 or more guns. The following demographic characteristics of women were associated with having a gun in the household: age (>40) (prevalence rate ratio [PRR] = 1.4; 95% confidence interval [CI] = 1.1–1.8); White race (PRR = 1.89; 95% CI = 1.61–2.27); currently being employed (PRR = 1.72; 95% CI = 1.22–2.44); higher education; and being insured by an HMO (PRR = 1.92; 95% CI = 1.47–2.50). Neither the partner's unemployment nor his substance use was associated with having a gun. While White households were more likely to have a gun, the same correlates of gun ownership held for both White and African-American households; being married or living as married and higher socio-economic status (i.e. HMO insurance and being employed) were strongly correlated with gun in the household. The following were correlated with having multiple guns in the household: White race (p < 0.0001); increased age (p = 0.005); being currently married or living as married (p < 0.0001); and HMO insured status (p < 0.0001). Among those households with at least one gun, White race and married or currently living as married were associated with having 2 or more guns relative to one gun in the household. ^ Conclusions. Currently living with a man and being of higher socio-economic status were strong correlates of household gun ownership among both Whites and African-Americans. Substance use was not associated with household gun ownership. ^

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.^