Machine-Learning Modeling of Asphalt Crack Treatment Effectiveness

Big data is defined as being large, varied or frequently updated, and usually generated from real-world interaction. With the unprecedented availability of big data, comes an obligation to maximise its potential for healthcare improvements in treatment effectiveness, disease prevention and healthcare delivery. We review the opportunities and challenges that big data brings to gastroenterology. We review its sources for healthcare improvement in gastroenterology, including electronic medical records, patient registries and patient-generated data. Big data can complement traditional research methods in hypothesis generation, supporting studies and disseminating findings; and in some cases holds distinct advantages where traditional trials are unfeasible. There is great potential power in patient-level linkage of datasets to help quantify inequalities, identify best practice and improve patient outcomes. We exemplify this with the UK colorectal cancer repository and the potential of linkage using the National Endoscopy Database, the inflammatory bowel disease registry and the National Health Service bowel cancer screening programme. Artificial intelligence and machine learning are increasingly being used to improve diagnostics in gastroenterology, with image analysis entering clinical practice, and the potential of machine learning to improve outcome prediction and diagnostics in other clinical areas. Big data brings issues with large sample sizes, real-world biases, data curation, keeping clinical context at analysis and General Data Protection Regulation compliance. There is a tension between our obligation to use data for the common good and protecting individual patient’s data. We emphasise the importance of engaging with our patients to enable them to understand their data usage as fully as they wish.

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Information-Based Medicine in Glioma Patients: A Clinical Perspective

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/8572058 ◽

2018 ◽

Vol 2018 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Joeky Tamba Senders ◽

Maya Harary ◽

Brittany Morgan Stopa ◽

Patrick Staples ◽

Marike Lianne Daphne Broekman ◽

...

Keyword(s):

Machine Learning ◽

Treatment Effectiveness ◽

Primary Brain Tumor ◽

Clinical Expertise ◽

Patient Harm ◽

Automated Inference ◽

Future Challenges ◽

The Individual ◽

Based Medicine ◽

Evidence Based Decision Making

Glioma constitutes the most common type of primary brain tumor with a dismal survival, often measured in terms of months or years. The thin line between treatment effectiveness and patient harm underpins the importance of tailoring clinical management to the individual patient. Randomized trials have laid the foundation for many neuro-oncological guidelines. Despite this, their findings focus on group-level estimates. Given our current tools, we are limited in our ability to guide patients on what therapy is best for them as individuals, or even how long they should expect to survive. Machine learning, however, promises to provide the analytical support for personalizing treatment decisions, and deep learning allows clinicians to unlock insight from the vast amount of unstructured data that is collected on glioma patients. Although these novel techniques have achieved astonishing results across a variety of clinical applications, significant hurdles remain associated with the implementation of them in clinical practice. Future challenges include the assembly of well-curated cross-institutional datasets, improvement of the interpretability of machine learning models, and balancing novel evidence-based decision-making with the associated liability of automated inference. Although artificial intelligence already exceeds clinical expertise in a variety of applications, clinicians remain responsible for interpreting the implications of, and acting upon, each prediction.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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Preliminary Findings on the Use of Session Questionnaires to Improve Clinician-Client Alliance With Special Consideration to Clinical Education

Perspectives on Administration and Supervision ◽

10.1044/aas25.1.50 ◽

2015 ◽

Vol 25 (1) ◽

pp. 50-60

Author(s):

Anu Subramanian

Keyword(s):

Graduate Students ◽

Clinical Education ◽

Clinical Decision Making ◽

Rating Scale ◽

Treatment Effectiveness ◽

Clinical Decision ◽

Added Value ◽

Therapeutic Outcomes ◽

Specific Feedback ◽

Stakeholder Perspective

ASHA's focus on evidence-based practice (EBP) includes the family/stakeholder perspective as an important tenet in clinical decision making. The common factors model for treatment effectiveness postulates that clinician-client alliance positively impacts therapeutic outcomes and may be the most important factor for success. One strategy to improve alliance between a client and clinician is the use of outcome questionnaires. In the current study, eight parents of toddlers who attended therapy sessions at a university clinic responded to a session outcome questionnaire that included both rating scale and descriptive questions. Six graduate students completed a survey that included a question about the utility of the questionnaire. Results indicated that the descriptive questions added value and information compared to using only the rating scale. The students were varied in their responses regarding the effectiveness of the questionnaire to increase their comfort with parents. Information gathered from the questionnaire allowed for specific feedback to graduate students to change behaviors and created opportunities for general discussions regarding effective therapy techniques. In addition, the responses generated conversations between the client and clinician focused on clients' concerns. Involving the stakeholder in identifying both effective and ineffective aspects of therapy has advantages for clinical practice and education.

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Establishing the Validity of Stuttering Treatment Effectiveness: The Fallibility of Clinical Experience

Perspectives on Fluency and Fluency Disorders ◽

10.1044/ffd14.2.9 ◽

2004 ◽

Vol 14 (2) ◽

pp. 9-12 ◽

Cited By ~ 3

Author(s):

Patrick Finn

Keyword(s):

Clinical Experience ◽

Treatment Effectiveness

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Research Consumerism 101: Evaluating External Evidence of Treatment Effectiveness

Perspectives on Swallowing and Swallowing Disorders (Dysphagia) ◽

10.1044/sasd17.2.43 ◽

2008 ◽

Vol 17 (2) ◽

pp. 43-49

Author(s):

James L. Coyle

Keyword(s):

Treatment Effectiveness ◽

Code Of Ethics ◽

Oropharyngeal Dysphagia ◽

Clinical Importance ◽

Professional Services ◽

Adverse Outcomes ◽

Professional Judgment ◽

Evidence Based ◽

Intervention Selection ◽

Published Research

Abstract The modern clinician is a research consumer. Rehabilitation of oropharyngeal impairments, and prevention of the adverse outcomes of dysphagia, requires the clinician to select interventions for which evidence of a reasonable likelihood of a successful, important outcome exists. The purpose of this paper is to provide strategies for evaluation of published research regarding treatment of oropharyngeal dysphagia. This article utilizes tutorial and examples to inform and educate practitioners in methods of appraising published research. It provides and encourages the use of methods of efficiently evaluating the validity and clinical importance of published research. Additionally, it discusses the importance of the ethical obligation we, as practitioners, have to use evidence-based treatment selection methods and measurement of patient performance during therapy. The reader is provided with tactics for evaluating treatment studies to establish a study's validity and, thereby, objectively select interventions. The importance of avoiding subjective or unsubstantiated claims and using objective methods of generating empirical clinical evidence is emphasized. The ability to evaluate the quality of research provides clinicians with objective intervention selection as an important, essential component of evidence-based clinical practice. ASHA Code of Ethics (2003): Principle I, Rule F: “Individuals shall fully inform the persons they serve of the nature and possible effects of services rendered and products dispensed…” (p. 2) Principle I, Rule G: “Individuals shall evaluate the effectiveness of services rendered and of products dispensed and shall provide services or dispense products only when benefit can reasonably be expected.” (p. 2) Principle IV, Rule G: “Individuals shall not provide professional services without exercising independent professional judgment, regardless of referral source or prescription.” (p. 4)

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