RBANS and TMT: Improvement in Rate of Diagnostic Classification

2007 ◽  
Author(s):  
Michael L. Drexler ◽  
Elsie Cheng ◽  
Flora Yuger ◽  
Theresa Rizzo
Keyword(s):  
Diagnostic Classification

Factors of Impulsivity and Cluster B Personality Dimensions

2017 ◽  
Vol 38 (4) ◽  
pp. 203-210 ◽  
Author(s):  
Christopher M. Lootens ◽  
Christopher D. Robertson ◽  
John T. Mitchell ◽  
Nathan A. Kimbrel ◽  
Natalie E. Hundt ◽  
...  
Keyword(s):  
Personality Disorders ◽  
The Other ◽  
Diagnostic Classification ◽  
Personality Dimensions ◽  
Cluster B Personality Disorders ◽  
Classification And Treatment

Abstract. The goal of the present investigation was to expand the literature on impulsivity and Cluster B personality disorders (PDs) by conceptualizing impulsivity in a multidimensional manner. Two separate undergraduate samples (n = 223; n = 204) completed measures of impulsivity and Cluster B dimensions. Impulsivity was indeed predictive of Cluster B dimensions and, importantly, each PD scale exhibited a unique impulsivity profile. Findings for borderline PD scores were highly consistent across samples and strongly and positively associated with urgency and lack of perseverance, as expected. Findings for the other PD dimensions also exhibited a fair amount of consistency. Implications of these findings for diagnostic classification and treatment are discussed.

scholarly journals AB0210 ACREULAR: AN R PACKAGE FOR THE CALCULATION AND VISUALISATION OF ACR/EULAR RELATED RHEUMATOID ARTHRITIS MEASURES

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1405.1-1406
Author(s):  
F. Morton ◽  
J. Nijjar ◽  
C. Goodyear ◽  
D. Porter
Keyword(s):  
Rheumatoid Arthritis ◽  
Functional Status ◽  
Rheumatic Diseases ◽  
Web Application ◽  
R Package ◽  
Diagnostic Classification ◽  
Microsoft Excel ◽  
Link Type ◽  
Large Joint ◽  
Programming Skills

Background:The American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) individually and collaboratively have produced/recommended diagnostic classification, response and functional status criteria for a range of different rheumatic diseases. While there are a number of different resources available for performing these calculations individually, currently there are no tools available that we are aware of to easily calculate these values for whole patient cohorts.Objectives:To develop a new software tool, which will enable both data analysts and also researchers and clinicians without programming skills to calculate ACR/EULAR related measures for a number of different rheumatic diseases.Methods:Criteria that had been developed by ACR and/or EULAR that had been approved for the diagnostic classification, measurement of treatment response and functional status in patients with rheumatoid arthritis were identified. Methods were created using the R programming language to allow the calculation of these criteria, which were incorporated into an R package. Additionally, an R/Shiny web application was developed to enable the calculations to be performed via a web browser using data presented as CSV or Microsoft Excel files.Results:acreular is a freely available, open source R package (downloadable fromhttps://github.com/fragla/acreular) that facilitates the calculation of ACR/EULAR related RA measures for whole patient cohorts. Measures, such as the ACR/EULAR (2010) RA classification criteria, can be determined using precalculated values for each component (small/large joint counts, duration in days, normal/abnormal acute-phase reactants, negative/low/high serology classification) or by providing “raw” data (small/large joint counts, onset/assessment dates, ESR/CRP and CCP/RF laboratory values). Other measures, including EULAR response and ACR20/50/70 response, can also be calculated by providing the required information. The accompanying web application is included as part of the R package but is also externally hosted athttps://fragla.shinyapps.io/shiny-acreular. This enables researchers and clinicians without any programming skills to easily calculate these measures by uploading either a Microsoft Excel or CSV file containing their data. Furthermore, the web application allows the incorporation of additional study covariates, enabling the automatic calculation of multigroup comparative statistics and the visualisation of the data through a number of different plots, both of which can be downloaded.Figure 1.The Data tab following the upload of data. Criteria are calculated by the selecting the appropriate checkbox.Figure 2.A density plot of DAS28 scores grouped by ACR/EULAR 2010 RA classification. Statistical analysis has been performed and shows a significant difference in DAS28 score between the two groups.Conclusion:The acreular R package facilitates the easy calculation of ACR/EULAR RA related disease measures for whole patient cohorts. Calculations can be performed either from within R or by using the accompanying web application, which also enables the graphical visualisation of data and the calculation of comparative statistics. We plan to further develop the package by adding additional RA related criteria and by adding ACR/EULAR related measures for other rheumatic disorders.Disclosure of Interests:Fraser Morton: None declared, Jagtar Nijjar Shareholder of: GlaxoSmithKline plc, Consultant of: Janssen Pharmaceuticals UK, Employee of: GlaxoSmithKline plc, Paid instructor for: Janssen Pharmaceuticals UK, Speakers bureau: Janssen Pharmaceuticals UK, AbbVie, Carl Goodyear: None declared, Duncan Porter: None declared

Taxonomy and utility in the diagnostic classification of mental disorders

2021 ◽  
Author(s):  
Spencer C. Evans ◽  
Michael C. Roberts ◽  
Jessy Guler ◽  
Jared W. Keeley ◽  
Geoffrey M. Reed
Keyword(s):  
Mental Disorders ◽  
Diagnostic Classification

scholarly journals Robust diagnostic classification via Q-learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Victor Ardulov ◽  
Victor R. Martinez ◽  
Krishna Somandepalli ◽  
Shuting Zheng ◽  
Emma Salzman ◽  
...  
Keyword(s):  
Autism Spectrum ◽  
Decision Function ◽  
Diagnostic Classification ◽  
Decision Making Process ◽  
Domain Experts ◽  
Q Learning ◽  
School Aged Children ◽  
Hyperactivity Disorder ◽  
Clinical Diagnoses ◽  
Learning Frameworks

AbstractMachine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional insights toward complex clinical diagnoses. In this context these tools desire two additional properties: interpretability, being able to audit and understand the decision function, and robustness, being able to assign the correct label in spite of missing or noisy inputs. This work formulates diagnostic classification as a decision-making process and utilizes Q-learning to build classifiers that meet the aforementioned desired criteria. As an exemplary task, we simulate the process of differentiating Autism Spectrum Disorder from Attention Deficit-Hyperactivity Disorder in verbal school aged children. This application highlights how reinforcement learning frameworks can be utilized to train more robust classifiers by jointly learning to maximize diagnostic accuracy while minimizing the amount of information required.

Symptoms as latent variables

2010 ◽  
Vol 33 (2-3) ◽  
pp. 165-166 ◽  
Author(s):  
Dennis J. McFarland ◽  
Loretta S. Malta
Keyword(s):  
Anxiety Disorder ◽  
Generalized Anxiety Disorder ◽  
Latent Variables ◽  
Diagnosis And Treatment ◽  
Diagnostic Classification ◽  
Generalized Anxiety ◽  
Diagnostic Categories ◽  
Target Article ◽  
The Relationship

AbstractIn the target article, Cramer et al. suggest that diagnostic classification is improved by modeling the relationship between manifest variables (i.e., symptoms) rather than modeling unobservable latent variables (i.e., diagnostic categories such as Generalized Anxiety Disorder). This commentary discusses whether symptoms represent manifest or latent variables and the implications of this distinction for diagnosis and treatment.

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