A Videogame-Based Approach to Measuring Information Processing Speed in Multiple Sclerosis Patients.

Objective: Slowing information processing speed (IPS) is a biomarker of neuronal damage in patients with multiple sclerosis (pwMS). A focus on IPS might be the ideal solution in the perspective of promptly detecting cognitive changes over time. We developed a tablet-based home-made videogame to test the sensitivity of this device in measuring subclinical IPS in pwMS. Materials and Methods: Forty-three pwMS without cognitive impairment and 20 healthy controls (HCs) were administered the videogame task with a tablet. Response times (RTs) and accuracy were recorded. Results: PwMS (mean RTs = 505.5 ± 73.9 ms) were significantly slower than HCs (mean RTs = 462.3 ± 40.3 ms, P = 0.014) on the videogame task. A moderate but significant correlation (r = -0.35, P = 0.03) between mean RTs and the Symbol Digit Modalities Test was observed. Conclusion: Our videogame showed good sensitivity in measuring IPS in apparently cognitive normal pwMS. Computerized testing might be useful in screening initial cognitive dysfunction that should be monitored as a marker of underlying disease progression. IRB approval Number is 2332CESC.

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

BACKGROUND: Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. METHODOLOGY/PRINCIPAL FINDINGS: Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. CONCLUSIONS/SIGNIFICANCE: The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

A neural network architecture for data classification.

This article aims at showing an architecture of neural networks designed for the classification of data distributed among a high number of classes. A significant gain in the global classification rate can be obtained by using our architecture. This latter is based on a set of several little neural networks, each one discriminating only two classes. The specialization of each neural network simplifies their structure and improves the classification. Moreover, the learning step automatically determines the number of hidden neurons. The discussion is illustrated by tests on databases from the UCI machine learning database repository. The experimental results show that this architecture can achieve a faster learning, simpler neural networks and an improved performance in classification.

Working smarter with APCs.

Are HIM professionals ready to make the most of APCs? This article suggests some strategies to improve the way we work with APCs, focus on complete and accurate coding, and improve our expertise.

¿Historia clínica electrónica? Una realidad

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Health insurance reform: national standard employer identifier--HCFA. Proposed rule.

This rule proposes a standard for a national employer identifier and requirements concerning its use by health plans, health care clearinghouses, and health care providers. The health plans, health care clearinghouses, and health care providers would use the identifier, among other uses, in connection with certain electronic transactions. The use of this identifier would improve the Medicare and Medicaid programs, and other Federal health programs and private health programs, and the effectiveness and efficiency of the health care industry in general, by simplifying the administration of the system and enabling the efficient electronic transmission of certain health information. It would implement some of the requirements of the Administrative Simplification subtitle of the Health Insurance Portability and Accountability Act of 1996.

National standard health care provider identifier--HCFA. Proposed rule.

This rule proposes a standard for a national health care provider identifier and requirements concerning its use by health plans, health care clearinghouses, and health care providers. The health plans, health care clearinghouses, and health care providers would use the identifier, among other uses, in connection with certain electronic transactions. The use of this identifier would improve the Medicare and Medicaid programs, and other Federal health programs and private health programs, and the effectiveness and efficiency of the health care industry in general, by simplifying the administration of the system and enabling the efficient electronic transmission of certain health information. It would implement some of the requirements of the Administrative Simplification subtitle of the Health Insurance Portability and Accountability Act of 1996.

Underlying causes of death in Down syndrome: accuracy of British Columbia death certificate data.

British Columbia (B.C.) provides an excellent opportunity to study the accuracy of assigning Underlying Cause of Death (UCOD) in Down syndrome. The study was designed to evaluate the effect of inappropriate interpretation of guidelines for completing the death certificate. All B.C. livebirths identified as having Down syndrome during the period 1952-1981 were collected from the records of the B.C. Health Surveillance Registry. Of the 1,337 affected individuals identified, 324 (24.2%) had died. The death certificates on these individuals were reviewed to assess from the information on the death certificate whether the underlying cause of death appeared to have been assigned in accordance to World Health Organization coding guidelines. The UCOD appeared to be assigned appropriately for 165/315 cases (52.4%). It is possible that in the situation where an easily recognizable condition such as Down syndrome is present, the need to identify the actual UCOD may be viewed as less urgent compared with cases where an apparently "normal" individual dies.