Deploying an interactive machine learning system in an evidence-based practice center

2012 ◽  
Author(s):  
Byron C. Wallace ◽  
Kevin Small ◽  
Carla E. Brodley ◽  
Joseph Lau ◽  
Thomas A. Trikalinos
Keyword(s):  
Machine Learning ◽  
Evidence Based Practice ◽  
Learning System ◽  
Evidence Based ◽  
Interactive Machine Learning

An Interactive Machine Learning System for Image Advertisements

2021 ◽  
Author(s):  
Markus Foerste ◽  
Mario Nadj ◽  
Merlin Knaeble ◽  
Alexander Maedche ◽  
Leonie Gehrmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Interactive Machine Learning

scholarly journals Using Machine Intelligence to Uncover Alzheimer’s Disease Progression Heterogeneity

2020 ◽  
Author(s):  
Bessi Qorri ◽  
MIke Tsay ◽  
Abhishek Agrawal ◽  
Rhoda Au ◽  
Joseph Geraci
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Learning Technologies ◽  
Machine Intelligence ◽  
Learning System ◽  
Small Data ◽  
Biological Mechanisms ◽  
Interactive Machine Learning ◽  
Learning Frameworks

Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Several existing and proprietary machine learning frameworks were applied to transcriptomic data. Through a proprietary interactive machine learning system, we were able to uncover several underlying biological mechanisms associated with AD pathology. These results, in turn, informed new hypotheses and identified novel targets for treatment. This paper reviews how the use of explainable machine learning technologies that are capable of extracting insights from small data can potentially provide a new taxonomy of disease. This is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. We share results from such an effort involving a set of AD subject transcriptomic samples where we provide a combinatorial view of how the pathology associated with what we call AD could emerge.

scholarly journals DrCaptcha: An interactive machine learning application

2020 ◽  
pp. 48-48
Author(s):  
Rafael Glikis ◽  
Christos Makris ◽  
Nikos Tsirakis
Keyword(s):  
Machine Learning ◽  
Human Factor ◽  
Learning System ◽  
Third Party ◽  
Data Preparation ◽  
Training Set ◽  
Massive Datasets ◽  
Interactive Machine Learning ◽  
System A ◽  
Typical Process

The creation of a Machine Learning system is a typical process that is mostly automated. However, we may address some problems in the during development, such as the over-training on the training set. A technique for eliminating this phenomenon is the assembling of ensembles of models that cooperate to make predictions. Another problem that almost always occurs is the necessity of the human factor in the data preparation process. In this paper, we present DrCaptcha [15], an interactive machine learning system that provides third-party applications with a CAPTCHA service and, at the same time, uses the user's input to train artificial neural networks that can be combined to create a powerful OCR system. A different way to tackle this problem is to use transfer learning, as we did in one of our experiments [33], to retrain models trained on massive datasets and retrain them in a smaller dataset.

What Does Evidence-Based Practice Mean to You? A Follow-Up Study Examining School-Based Speech-Language Pathologists' Perspectives on Evidence-Based Practice

2020 ◽  
Vol 29 (2) ◽  
pp. 688-704
Author(s):  
Katrina Fulcher-Rood ◽  
Anny Castilla-Earls ◽  
Jeff Higginbotham
Keyword(s):  
Clinical Practice ◽  
Research Evidence ◽  
Evidence Based Practice ◽  
Evidence Based ◽  
Clinical Expertise ◽  
Speech Language Pathologists ◽  
School Based ◽  
Research Findings ◽  
Ebp Implementation

Purpose The current investigation is a follow-up from a previous study examining child language diagnostic decision making in school-based speech-language pathologists (SLPs). The purpose of this study was to examine the SLPs' perspectives regarding the use of evidence-based practice (EBP) in their clinical work. Method Semistructured phone interviews were conducted with 25 school-based SLPs who previously participated in an earlier study by Fulcher-Rood et al. 2018). SLPs were asked questions regarding their definition of EBP, the value of research evidence, contexts in which they implement scientific literature in clinical practice, and the barriers to implementing EBP. Results SLPs' definitions of EBP differed from current definitions, in that SLPs only included the use of research findings. SLPs seem to discuss EBP as it relates to treatment and not assessment. Reported barriers to EBP implementation were insufficient time, limited funding, and restrictions from their employment setting. SLPs found it difficult to translate research findings to clinical practice. SLPs implemented external research evidence when they did not have enough clinical expertise regarding a specific client or when they needed scientific evidence to support a strategy they used. Conclusions SLPs appear to use EBP for specific reasons and not for every clinical decision they make. In addition, SLPs rely on EBP for treatment decisions and not for assessment decisions. Educational systems potentially present other challenges that need to be considered for EBP implementation. Considerations for implementation science and the research-to-practice gap are discussed.

Current Issues: Evidence-Based Practice for the Real World: Thoughts on Clinical Education in Communication Disorders

2010 ◽  
Vol 20 (3) ◽  
pp. 100-105 ◽  
Author(s):  
Anne K. Bothe
Keyword(s):  
Clinical Education ◽  
Real World ◽  
Evidence Based Practice ◽  
Communication Disorders ◽  
University Faculty ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Basic Principles ◽  
The Real ◽  
Language Pathology

This article presents some streamlined and intentionally oversimplified ideas about educating future communication disorders professionals to use some of the most basic principles of evidence-based practice. Working from a popular five-step approach, modifications are suggested that may make the ideas more accessible, and therefore more useful, for university faculty, other supervisors, and future professionals in speech-language pathology, audiology, and related fields.

Training Methods With Clinicians and Students—A Personal Story and Change: Narrative Therapy for Stuttering

2008 ◽  
Vol 18 (1) ◽  
pp. 37-42 ◽  
Author(s):  
Margaret Leahy
Keyword(s):  
Narrative Therapy ◽  
Evidence Based Practice ◽  
Theoretical Background ◽  
Evidence Based ◽  
Training Methods ◽  
Sense Of Control ◽  
Case Description ◽  
General Outline ◽  
Personal Story ◽  
Fluency Disorders

Abstract Educating students and informing clinicians regarding developments in therapy approaches and in evidence-based practice are important elements of the responsibility of specialist academic posts in universities. In this article, the development of narrative therapy and its theoretical background are outlined (preceded by a general outline of how the topic of fluency disorders is introduced to students at an Irish university). An example of implementing narrative therapy with a 12-year-old boy is presented. The brief case description demonstrates how narrative therapy facilitated this 12-year-old make sense of his dysfluency and his phonological disorder, leading to his improved understanding and management of the problems, fostering a sense of control that led ultimately to their resolution.

Evidence-Based Practice & Practice-Based Evidence Applied to Adult, Medical Speech-Language Pathology

2013 ◽  
Vol 18 (1) ◽  
pp. 14-26 ◽  
Author(s):  
Rik Lemoncello ◽  
Bryan Ness
Keyword(s):  
Evidence Based Practice ◽  
Single Case ◽  
Quality Data ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Clinical Practices ◽  
High Quality ◽  
Randomized Controlled Clinical Trials ◽  
Relative Paucity ◽  
Language Pathology

In this paper, we review concepts of evidence-based practice (EBP), and provide a discussion of the current limitations of EBP in terms of a relative paucity of efficacy evidence and the limitations of applying findings from randomized controlled clinical trials to individual clinical decisions. We will offer a complementary model of practice-based evidence (PBE) to encourage clinical scientists to design, implement, and evaluate our own clinical practices with high-quality evidence. We will describe two models for conducting PBE: the multiple baseline single-case experimental design and a clinical case study enhanced with generalization and control data probes. Gathering, analyzing, and sharing high-quality data can offer additional support through PBE to support EBP in speech-language pathology. It is our hope that these EBP and PBE strategies will empower clinical scientists to persevere in the quest for best practices.

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