scholarly journals A web-based, branching logic questionnaire for the automated classification of migraine

2018 ◽  
Author(s):  
Eric A. Kaiser ◽  
Aleksandra Igdalova ◽  
Geoffrey K. Aguirre ◽  
Brett Cucchiara
Keyword(s):  
Migraine Without Aura ◽  
Large Scale ◽  
Automated Analysis ◽  
Automated Classification ◽  
Online Questionnaire ◽  
Demographic Differences ◽  
Web Based ◽  
Online Evaluation ◽  
Automated Algorithm

AbstractObjectiveTo identify migraineurs and headache-free individuals with an online questionnaire and automated analysis algorithm.MethodsWe created a branching-logic, web-based questionnaire—the Penn Online Evaluation of Migraine (POEM)—to obtain standardized headache history from a previously studied cohort. Responses were analyzed with an automated algorithm to assign subjects to one of several categories based on ICHD-3 (beta) criteria. Following a pre-registered protocol, this result was compared to prior diagnostic classification by a neurologist following a direct interview.ResultsOf 118 subjects contacted, 90 (76%) completed the questionnaire; of these 31 were headache-free, 29 migraine without aura (MwoA), and 30 migraine with aura (MwA). Mean age was 41 ± 6 years and 76% were female. There were no significant demographic differences between groups. The median time to complete the questionnaire was 2.5 minutes. Sensitivity of the POEM tool was 42%, 59%, and 70%, and specificity was 100%, 84%, and 94% for headache-free, MwoA, and MwA, respectively. Sensitivity and specificity of the POEM tool for migraine overall (with or without aura), was 83% and 90%, respectively.ConclusionsThe POEM web-based questionnaire, and associated analysis routines, identifies headache-free and migraine subjects with good specificity. It may be useful for classifying subjects for large-scale research studies.Trial Registration:https://osf.io/sq9ef

scholarly journals A web-based, branching logic questionnaire for the automated classification of migraine

Cephalalgia ◽  
2019 ◽  
Vol 39 (10) ◽  
pp. 1257-1266 ◽  
Author(s):  
Eric A Kaiser ◽  
Aleksandra Igdalova ◽  
Geoffrey K Aguirre ◽  
Brett Cucchiara
Keyword(s):  
Migraine With Aura ◽  
Migraine Without Aura ◽  
Large Scale ◽  
Research Study ◽  
Automated Analysis ◽  
Automated Classification ◽  
Online Questionnaire ◽  
Web Based ◽  
Online Evaluation ◽  
Automated Algorithm

Objective To identify migraineurs and headache-free individuals with an online questionnaire and automated analysis algorithm. Methods We created a branching-logic, web-based questionnaire – the Penn Online Evaluation of Migraine – to obtain standardized headache history from a previously studied cohort. Responses were analyzed with an automated algorithm to assign subjects to one of several categories based on ICHD-3 (beta) criteria. Following a pre-registered protocol, the primary outcome was sensitivity and specificity for assignment of headache-free, migraine without aura, and migraine with aura labels, as compared to a prior classification by neurologist interview. Results Of 118 subjects contacted, 90 (76%) completed the questionnaire; of these 31 were headache-free controls, 29 migraine without aura, and 30 migraine with aura. Mean age was 41 ± 6 years and 76% were female. There were no significant demographic differences between groups. The median time to complete the questionnaire was 2.5 minutes (IQR: 1.5–3.4 minutes). Sensitivity of the Penn Online Evaluation of Migraine tool was 42%, 59%, 70%, and 83%, and specificity was 100%, 84%, 93%, and 90% for headache-free controls, migraine without aura, migraine with aura, and migraine overall, respectively. Conclusions The Penn Online Evaluation of Migraine web-based questionnaire, and associated analysis routine, identifies headache-free and migraine subjects with good specificity. It may be useful for classifying subjects for large-scale research studies. Research study pre-registration: https://osf.io/sq9ef The following research study is a not a clinical trial.

scholarly journals Distributed Biomedical Terminology Development: From Experiments to Open Process

2010 ◽  
Vol 19 (01) ◽  
pp. 58-63 ◽  
Author(s):  
C. G. Chute
Keyword(s):  
Group Process ◽  
Large Scale ◽  
Description Logic ◽  
Social Computing ◽  
Historical Review ◽  
Web Based ◽  
International Classification Of Disease ◽  
Biomedical Terminology ◽  
Computing Models

Summary Objective: Can social computing efforts materially alter the distributed creation and maintenance of complex biomedical terminologies and ontologies; a review of distributed authoring history and status. Background: Social computing projects, such as Wikipedia, have dramatically altered the perception and reality of large-scale content projects and the labor required to create and maintain them. Health terminologies have become large, complex, interdependent content artifacts of increasing importance to biomedical research and the communities understanding of biology, medicine, and optimal healthcare practices. The question naturally arises as to whether social computing models and distributed authoring platforms can be applied to the voluntary, distributed authoring of high-quality terminologies and ontologies. Methods: An historical review of distributed authoring developments. Results: The trajectory of description logic-driven authoring tools, group process, and web-based platforms suggests that public distributed authoring is likely feasible and practical; however, no compelling example on the order of Wikipedia is yet extant. Nevertheless, several projects, including the Gene Ontology and the new revision of the International Classification of Disease (ICD-11) hold promise.

Abstract 2597: Centralized and Study-specific Phenotyping in a Large-scale Stroke Genetics Study: An Analysis from the NINDS Stroke Genetics Network (SiGN)

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Bradford B Worrall ◽  
Alejandro Rabinstein ◽  
Dale M Gamble ◽  
Kevin M Barrett ◽  
Shaneela Malik ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Large Scale ◽  
Association Studies ◽  
Web Based ◽  
Stroke Treatment ◽  
Automated Algorithm ◽  
Genome Association ◽  
Whole Genome Association ◽  
Toast Classification ◽  
User Friendly

Background: The Stroke Genetics Network (SiGN) funded by the NINDS aims to identify genetic risk factors in ischemic stroke using whole-genome association studies (GWAS). High quality phenotyping is crucial to successful application of GWAS. As a heterogenous disorder, stroke poses specific challenges. The Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification is a broadly used, but its validity is challenged especially when performed by multiple investigators with differing interpretations of the system. The Causative Classification System for Ischemic Stroke (CCS) system is a new, web-based, and computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner ( ccs.mgh.harvard.edu ) to generate subtypes. Methods: In planning the SiGN proposal, a sample of 20 coded charts were collected from a subset of participating studies to assess feasibility of central adjudication and comparability to study-specific TOAST. Two central adjudicators reviewed all records and generated TOAST and CCS subtypes. These were compared to study-specific TOAST subtype and the CCS phenotype generated for SiGN by local trained adjudicators. CCS data is now available for 7134 included cases using both a 5 and a 7 category system as defined in the table . Results: All 4 phenotypes were available for 115 ischemic stroke cases from 6 studies in SiGN. Basic demographics were 54% women, 63% white, and median age between 65-74. Table 1 provides the agreement between the various subtypes. Table 2 describes the types of disagreement. Conclusions: Central adjudication with only two adjudicators and curated medical records yielded more consistent subtyping independent of phenotyping system. The agreement for TOAST was higher than published rates by independent groups (∼0.50). In contrast, the agreement for CCS was lower than previously published (0.85-0.95). Site adjudicators' familiarity with TOAST and inexperience with CCS may contribute. Although CCS is an automated algorithm and has a number of user friendly features, our findings suggest that formal training and certification process before starting to use CCS may be worthwhile to achieve optimal benefit from the system.

scholarly journals Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): A Cloud-based Platform for Curating and Classifying Germline Variants

2018 ◽  
Author(s):  
Michael N. Edmonson ◽  
Aman N. Patel ◽  
Dale J. Hedges ◽  
Zhaoming Wang ◽  
Evadnie Rampersaud ◽  
...  
Keyword(s):  
Pediatric Cancer ◽  
Information Exchange ◽  
Large Scale ◽  
Disease Genes ◽  
Single Nucleotide Variants ◽  
Web Based ◽  
Pediatric Cancer Patients ◽  
Predisposition Genes ◽  
Next Generation Sequencing Ngs

AbstractVariant interpretation in the era of next-generation sequencing (NGS) is challenging. While many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here we present “PeCanPIE” – the Pediatric Cancer Variant Pathogenicity Information Exchange, a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in Variant Call Format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4,000 pediatric cancer patients, and serves as a repository for the expert-reviewed results. While PeCanPIE’s web-based interface was designed to be accessible to non-bioinformaticians, its back end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

Simulation Based Analysis of Automated Classification of Medical Images

2004 ◽  
Vol 43 (02) ◽  
pp. 150-155 ◽  
Author(s):  
W. Adler ◽  
T. Hothorn ◽  
B. Lausen
Keyword(s):  
Laser Scanning ◽  
Case Control Study ◽  
Real Data ◽  
Automated Analysis ◽  
Misclassification Error ◽  
Automated Classification ◽  
Analysis Method ◽  
Simulation Based ◽  
Control Study

Summary Objectives: The ability of various classifiers to discriminate between normal and glaucomatous eyes based on features derived from automated analysis of laser scanning images of the eye background is investigated. Methods: To compare the classifiers without over-optimization for a given dataset, we use a simulation model to create topography images. We designed three different simulation setups as model of extreme situations and medical subgroups. Results: Neither linear nor tree-based classifiers are ideal for all setups. The most robust performance is obtained by a combination of both, so-called Double-Bagging. Classification of real data from a case-control study shows best results with Double-Bagging. All results obtained with the analysis method extracting features automatically are worse than those obtained by the same classifiers but with features derived from an analysis method that requires intervention of a physician. Conclusions: Robust classification results for classification of laser scanning images obtained with the Heidelberg Retina Tomograph are achieved by combined classifiers. The examined automated procedure causes an increased misclassification error compared to the established clinical routine requiring an expert physician’s intervention.

Using Web-based Testing for Large-Scale Assessment

2013 ◽  
Author(s):  
Laura S. Hamilton ◽  
Stephen P. Klein ◽  
William Lorie
Keyword(s):  
Large Scale ◽  
Web Based ◽  
Large Scale Assessment ◽  
Scale Assessment

Impact of COVID-19 on Nuclear Medicine in Germany, Austria and Switzerland: An International Survey in April 2020

2020 ◽  
Vol 59 (04) ◽  
pp. 294-299 ◽  
Author(s):  
Lutz S. Freudenberg ◽  
Ulf Dittmer ◽  
Ken Herrmann
Keyword(s):  
Nuclear Medicine ◽  
Health Systems ◽  
Personal Protective Equipment ◽  
Large Scale ◽  
Diagnostic Procedures ◽  
Protective Equipment ◽  
Current Crisis ◽  
Web Based ◽  
Public Versus Private ◽  
The Impact

Abstract Introduction Preparations of health systems to accommodate large number of severely ill COVID-19 patients in March/April 2020 has a significant impact on nuclear medicine departments. Materials and Methods A web-based questionnaire was designed to differentiate the impact of the pandemic on inpatient and outpatient nuclear medicine operations and on public versus private health systems, respectively. Questions were addressing the following issues: impact on nuclear medicine diagnostics and therapy, use of recommendations, personal protective equipment, and organizational adaptations. The survey was available for 6 days and closed on April 20, 2020. Results 113 complete responses were recorded. Nearly all participants (97 %) report a decline of nuclear medicine diagnostic procedures. The mean reduction in the last three weeks for PET/CT, scintigraphies of bone, myocardium, lung thyroid, sentinel lymph-node are –14.4 %, –47.2 %, –47.5 %, –40.7 %, –58.4 %, and –25.2 % respectively. Furthermore, 76 % of the participants report a reduction in therapies especially for benign thyroid disease (-41.8 %) and radiosynoviorthesis (–53.8 %) while tumor therapies remained mainly stable. 48 % of the participants report a shortage of personal protective equipment. Conclusions Nuclear medicine services are notably reduced 3 weeks after the SARS-CoV-2 pandemic reached Germany, Austria and Switzerland on a large scale. We must be aware that the current crisis will also have a significant economic impact on the healthcare system. As the survey cannot adapt to daily dynamic changes in priorities, it serves as a first snapshot requiring follow-up studies and comparisons with other countries and regions.

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