scholarly journals A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2439
Author(s):  
Talal A. A. Abdullah ◽  
Mohd Soperi Mohd Zahid ◽  
Waleed Ali
Keyword(s):  
Disease Diagnosis ◽  
Black Box ◽  
Data Driven ◽  
Quality Service ◽  
Future Directions ◽  
Comprehensive Survey ◽  
Data Driven Decisions ◽  
The Impact ◽  
Machine Learning Models ◽  
Made In

We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Furthermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed.

The Oxford Handbook of Hope

2017 ◽  
Keyword(s):  
Current Knowledge ◽  
Physical And Mental Health ◽  
Comprehensive Overview ◽  
Future Directions ◽  
Theoretical Perspectives ◽  
The Many ◽  
The Impact ◽  
Made In

The Oxford Handbook of Hope provides a comprehensive overview of current knowledge regarding the science and practice of hope. Hope has long been a topic of interest to philosophers and the general public, but it was only in recent decades that hope became a focus of psychological science. Rick Snyder defined hope as a cognitive trait that helps individuals to identify and pursue goals and consists of two components: pathways, the perceived capacity to identify strategies necessary to achieve goals, and agency, the willpower or motivation to pursue those pathways to achieve goals. Hope has become one of most robust and promising topics in the burgeoning field of positive psychology. This book reviews the progress that has been made in the past 25 years regarding the origins and influence of hope. Topics covered include current theoretical perspectives on how best to define hope and how it is distinct from related constructs, current best practices for measuring and quantifying hope, interventions and strategies for promoting hope across different settings and the lifespan, the impact that hope has on many dimensions and domains of physical and mental health, and the many ways and contexts in which hope promotes resilience and positive functioning. Experts in the field both review what is currently known about the role of hope in different domains and identify topics and questions that can help to guide the next decade of research. The handbook concludes with a collaborative vision on the future directions of the science of hope.

Data-driven decisions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Keyword(s):  
Reading Time ◽  
Data Driven ◽  
Social Media Data ◽  
Content Type ◽  
Pertinent Information ◽  
Hospitality Sector ◽  
Data Driven Decisions ◽  
Media Data ◽  
Practical Implications ◽  
Made In

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Social media data has the ability to drastically improve the strategic outlooks and planning of SMEs in the hospitality sector, is sufficient investment is made in the collection and analysis of this data. Originality The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Big Data

2015 ◽  
Vol 6 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Ashok Kumar Wahi ◽  
Yajulu Medury ◽  
Rajnish Kumar Misra
Keyword(s):  
Big Data ◽  
Unstructured Data ◽  
Data Driven ◽  
Enterprise 2.0 ◽  
Huge Amount ◽  
The World ◽  
Data Driven Decisions ◽  
The Impact

Big data has taken the world by storm. Everyone from every industry is not only talking about the impact of big data but is looking for ways to effectively leverage the power of big data. This challenge has heightened with the huge amount of unstructured data flowing from every direction, bringing along with it the increasing pressure to make data driven decisions rather than the gut-driven decisions. This article sheds light on how big data can be an enabler for smart enterprises if the organization is able to address the challenges posed by big data. Enterprises need to equip themselves with relevant technology, desired skills and a supporting managerial attitude to swim through the challenges of big data. It also highlights the need for all enterprises making the journey from 1.0 stage to Enterprise 2.0 to master the art of Big Data if they have to make the transition successful.

scholarly journals The Future of PHM Could be Tiny under Cloud: Exploring Potential Application Patterns of TinyML in PHM Scenarios

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xingyu Zhou ◽  
Zhuangwei Kang ◽  
Robert Canady ◽  
Shunxing Bao ◽  
Daniel Allen Balasubramanian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Learning Models ◽  
Level Data ◽  
Data Source ◽  
Single Data ◽  
The Impact ◽  
Machine Learning Models

Deep learning has shown impressive performance acrosshealth management and prognostics applications. Nowadays, an emerging trend of machine learning deployment on resource constraint hardware devices like micro-controllers(MCU) has aroused much attention. Given the distributed andresource constraint nature of many PHM applications, using tiny machine learning models close to data source sensors for on-device inferences would be beneficial to save both time andadditional hardware resources. Even though there has beenpast works that bring TinyML on MCUs for some PHM ap-plications, they are mainly targeting single data source usage without higher-level data incorporation with cloud computing.We study the impact of potential cooperation patterns betweenTinyML on edge and more powerful computation resources oncloud and how this would make an impact on the application patterns in data-driven prognostics. We introduce potential ap-plications where sensor readings are utilized for system health status prediction including status classification and remaining useful life regression. We find that MCUs and cloud com-puting can be adaptive to different kinds of machine learning models and combined in flexible ways for diverse requirement.Our work also shows limitations of current MCU-based deep learning in data-driven prognostics And we hope our work can

Value Orientation Inventory: Development, Applications, and Contributions

2018 ◽  
Vol 7 (3) ◽  
pp. 206-210 ◽  
Author(s):  
Weimo Zhu ◽  
Ang Chen
Keyword(s):  
Value Orientation ◽  
Value Orientations ◽  
Measurement Tool ◽  
Teaching Behaviors ◽  
Future Directions ◽  
Inventory Development ◽  
The Impact ◽  
Development Applications ◽  
Measurement Tool Development ◽  
Made In

This paper provides an overview of the long and vigorous efforts made in the development, applications, and contributions of the Value Orientation Inventory (VOI) by Dr. Catherine D. Ennis, her students, and her colleagues. After a brief review of the development, validation, and cross-validation of the VOI and corresponding applications, the authors describe the contributions the VOI made in pedagogy research and the impact of teachers’ value orientations on their teaching behaviors. They also discuss how a measurement tool should be developed and present Ennis’s work as a model of how a research line should be established. Finally, they reflect on the limitations in measurement tool development in kinesiology and outline future directions for VOI revision and application.

Translating Principles of Speech Science to Clinical Practice: Current and Future Trends in Craniofacial Disorders

2008 ◽  
Vol 18 (1) ◽  
pp. 31-40 ◽  
Author(s):  
David J. Zajac
Keyword(s):  
Clinical Practice ◽  
Speech Intelligibility ◽  
Current Practice ◽  
Computer Assisted ◽  
Behavioral Management ◽  
Single Word ◽  
Future Directions ◽  
Speech Science ◽  
The Impact ◽  
Opinion Article

Abstract The purpose of this opinion article is to review the impact of the principles and technology of speech science on clinical practice in the area of craniofacial disorders. Current practice relative to (a) speech aerodynamic assessment, (b) computer-assisted single-word speech intelligibility testing, and (c) behavioral management of hypernasal resonance are reviewed. Future directions and/or refinement of each area are also identified. It is suggested that both challenging and rewarding times are in store for clinical researchers in craniofacial disorders.

Inflammatory Markers Associated With Diabetes Mellitus – Old and New Players

2020 ◽  
Vol 26 ◽  
Author(s):  
Emir Muzurović ◽  
Zoja Stanković ◽  
Zlata Kovačević ◽  
Benida Šahmanović Škrijelj ◽  
Dimitri P Mikhailidis
Keyword(s):  
Diabetes Mellitus ◽  
Inflammatory Markers ◽  
High Specificity ◽  
Future Directions ◽  
Effective Strategies ◽  
Advantages And Disadvantages ◽  
Type 2 Dm ◽  
Increased Risk ◽  
Made In

: Diabetes mellitus (DM) is a chronic and complex metabolic disorder, and also an important cause of cardiovascular (CV) diseases (CVDs). Subclinical inflammation, observed in patients with type 2 DM (T2DM), cannot be considered the sole or primary cause of T2DM in the absence of classical risk factors, but it represents an important mechanism that serves as a bridge between primary causes of T2DM and its manifestation. Progress has been made in the identification of effective strategies to prevent or delay the onset of T2DM. It is important to identify those at increased risk for DM by using specific biomarkers. Inflammatory markers correlate with insulin resistance (IR) and glycoregulation in patients with DM. Also, several inflammatory markers have been shown to be useful in assessing the risk of developing DM and its complications. However, the intertwining of pathophysiological processes and the not-quite-specificity of inflammatory markers for certain clinical entities limits their practical use. In this review we consider the advantages and disadvantages of various inflammatory biomarkers of DM that have been investigated to date as well as possible future directions. Key features of such biomarkers should be high specificity, non-invasiveness and cost-effectiveness.

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