Applying clustering based on rules on WHO-DAS II for knowledge discovery on functional disabilities

The senior citizens represent a fast growing proportion of the population in Europe and other developed areas. This increases the proportion of persons with disability and reducing quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on the concept of disability, the World Health Organization (WHO) developed a clinical test WHO Disability Assessment Schedule, (WHO-DASII) that is understood to include physical, mental, and social well-being, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge on the performance of the test WHO-DASII on the basis of a sample of neurological patients from an Italian hospital. This Knowledge Discovery problem has been faced by using clustering based on rules, a technique stablished on 1994 by Gibert which combines some Inductive Learning (from AI) methods with Statistics to extract knowledge on ill-structured domains (that is complex domains where consensus is not achieved, like is the case). So, in this paper, the results of applying this technique to the WHO-DASII results is presented.Postprint (published version

Therapy development for the mucopolysaccharidoses : updated consensus recommendations for neuropsychological endpoints

Neurological dysfunction represents a significant clinical component of many of the mucopolysaccharidoses (also known as MPS disorders). The accurate and consistent assessment of neuropsychological function is essential to gain a greater understanding of the precise natural history of these conditions and to design effective clinical trials to evaluate the impact of therapies on the brain. In 2017, an International MPS Consensus Panel published recommendations for best practice in the design and conduct of clinical studies investigating the effects of therapies on cognitive function and adaptive behavior in patients with neuronopathic mucopolysaccharidoses. Based on an International MPS Consensus Conference held in February 2020, this article provides updated consensus recommendations and expands the objectives to include approaches for assessing behavioral and social-emotional state, caregiver burden and quality of life in patients with all mucopolysaccharidoses

BioMeT and algorithm challenges: A proposed digital standardized evaluation framework

Technology is advancing at an extraordinary rate. Continuous flows of novel data are being generated with the potential to revolutionize how we better identify, treat, manage, and prevent disease across therapeutic areas. However, lack of security of confidence in digital health technologies is hampering adoption, particularly for biometric monitoring technologies (BioMeTs) where frontline healthcare professionals are struggling to determine which BioMeTs are fit-for-purpose and in which context. Here, we discuss the challenges to adoption and offer pragmatic guidance regarding BioMeTs, cumulating in a proposed framework to advance their development and deployment in healthcare, health research, and health promotion. Furthermore, the framework proposes a process to establish an audit trail of BioMeTs (hardware and algorithms), to instill trust amongst multidisciplinary users

Focal Spot, Spring 1999

https://digitalcommons.wustl.edu/focal_spot_archives/1081/thumbnail.jp

Startle Distinguishes Task Expertise

abstract: Recently, it was demonstrated that startle-evoked-movements (SEMs) are present during individuated finger movements (index finger abduction), but only following intense training. This demonstrates that changes in motor planning, which occur through training (motor learning - a characteristic which can provide researchers and clinicians with information about overall rehabilitative effectiveness), can be analyzed with SEM. The objective here was to determine if SEM is a sensitive enough tool for differentiating expertise (task solidification) in a common everyday task (typing). If proven to be true, SEM may then be useful during rehabilitation for time-stamping when task-specific expertise has occurred, and possibly even when the sufficient dosage of motor training (although not tested here) has been delivered following impairment. It was hypothesized that SEM would be present for all fingers of an expert population, but no fingers of a non-expert population. A total of 9 expert (75.2 ± 9.8 WPM) and 8 non-expert typists, (41.6 ± 8.2 WPM) with right handed dominance and with no previous neurological or current upper extremity impairment were evaluated. SEM was robustly present (all p < 0.05) in all fingers of the experts (except the middle) and absent in all fingers of non-experts except the little (although less robust). Taken together, these results indicate that SEM is a measurable behavioral indicator of motor learning and that it is sensitive to task expertise, opening it for potential clinical utility.Dissertation/ThesisMasters Thesis Biomedical Engineering 201

Effective Strategies for Recognition and Treatment of In-Hospital Strokes

In-hospital onset strokes represent 4% to 20% of all reported strokes in the United States. The variability of treatment protocols and workflows as well as the complex etiology and multiple comorbidities of the in-hospital stroke subpopulation often result in unfavorable outcomes and higher mortality rates compared to those who experience strokes outside of the hospital setting. The purpose of this project was to conduct a systematic review to identify and summarize effective strategies and practices for prompt recognition and treatment of in-hospital strokes. The results of the literature review with leading-edge guidelines for stroke care were corelated to formulate recommendations at an organizational level for improving care delivery and workflow. Peer-reviewed publications and literature not controlled by publishers were analyzed. An appraisal of 24 articles was conducted, using the guide for classification of level of evidence by Fineout-Overholt, Melnyk, Stillwell, and Williamson. The results of this systematic review revealed that the most effective strategies and practices for prompt recognition and treatment of in-hospital strokes included: staff education, creating a dedicated responder team, analysis and improvement of internal processes to shorten the time from discovery to diagnosis, and offering appropriate evidence-based treatments according to acute stroke guidelines. Creating organizational protocols and quality metrics to promote timely and evidence-based care for in-hospital strokes may result in a positive social change by eliminating the existing care disparities between community and in-hospital strokes and improving the health outcomes of this subpopulation of strokes

Automatic classification of registered clinical trials towards the Global Burden of Diseases taxonomy of diseases and injuries

Includes details on the implementation of MetaMap and IntraMap, prioritization rules, the test set of clinical trials and the classification of the external test set according to the 171 GBD categories. Dataset S1: Expert-based enrichment database for the classification according to the 28 GBD categories. Manual classification of 503 UMLS concepts that could not be mapped to any of the 28 GBD categories. Dataset S2: Expert-based enrichment database for the classification according to the 171 GBD categories. Manual classification of 655 UMLS concepts that could not be mapped to any of the 171 GBD categories, among which 108 could be projected to candidate GBD categories. Table S1: Excluded residual GBD categories for the grouping of the GBD cause list in 171 GBD categories. A grouping of 193 GBD categories was defined during the GBD 2010 study to inform policy makers about the main health problems per country. From these 193 GBD categories, we excluded the 22 residual categories listed in the Table. We developed a classifier for the remaining 171 GBD categories. Among these residual categories, the unique excluded categories in the grouping of 28 GBD categories were “Other infectious diseases” and “Other endocrine, nutritional, blood, and immune disorders”. Table S2: Per-category evaluation of performance of the classifier for the 171 GBD categories plus the “No GBD” category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities, specificities (in %) and likelihood ratios for each of the 171 GBD categories plus the “No GBD” category for the classifier using the Word Sense Disambiguation server, the expert-based enrichment database and the priority to the health condition field. Table S3: Performance of the 8 versions of the classifier for the 171 GBD categories. Exact-matching and weighted averaged sensitivities and specificities for 8 versions of the classifier for the 171 GBD categories. Exact-matching corresponds to the proportion (in %) of trials for which the automatic GBD classification is correct. Exact-matching was estimated over all trials (N = 2,763), trials concerning a unique GBD category (N = 2,092), trials concerning 2 or more GBD categories (N = 187), and trials not relevant for the GBD (N = 484). The weighted averaged sensitivity and specificity corresponds to the weighted average across GBD categories of the sensitivities and specificities for each GBD category plus the “No GBD” category (in %). The 8 versions correspond to the combinations of the use or not of the Word Sense Disambiguation server during the text annotation, the expert-based enrichment database, and the priority to the health condition field as a prioritization rule. Table S4: Per-category evaluation of the performance of the baseline for the 28 GBD categories plus the “No GBD” category. Number of trials per GBD category from the test set of 2,763 clinical trials. Sensitivities and specificities (in %) of the 28 GBD categories plus the “No GBD” category for the classification of clinical trial records towards GBD categories without using the UMLS knowledge source but based on the recognition in free text of the names of diseases defining in each GBD category only. For the baseline a clinical trial records was classified with a GBD category if at least one of the 291 disease names from the GBD cause list defining that GBD category appeared verbatim in the condition field, the public or scientific titles, separately, or in at least one of these three text fields. (DOCX 84 kb