Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology

2018 ◽  
Vol 15 (3) ◽  
pp. 569-576 ◽  
Author(s):  
Tanveer Syeda-Mahmood
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Diagnostic Decision

scholarly journals Prospects and Challenges for Clinical Decision Support in the Era of Big Data

2018 ◽  
pp. 1-12 ◽  
Author(s):  
Issam El Naqa ◽  
Michael R. Kosorok ◽  
Judy Jin ◽  
Michelle Mierzwa ◽  
Randall K. Ten Haken
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Current Status ◽  
Analytical Approaches

Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called big data (BD), an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data, patient privacy, transformation of current analytical approaches to handle such noisy and heterogeneous data, and expanded use of advanced statistical learning methods on the basis of confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical end points, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the use and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

Role of Machine Learning in Big Data Peregrination

2021 ◽  
pp. 235-276
Author(s):  
Aradhana Behura ◽  
Sanjaya Kumar Panda
Keyword(s):  
Machine Learning ◽  
Big Data

scholarly journals Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

2021 ◽  
Vol 10 (6) ◽  
pp. 424
Author(s):  
Behrouz Pirouz ◽  
Aldo Pedro Ferrante ◽  
Behzad Pirouz ◽  
Patrizia Piro
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Bayesian Network ◽  
Negative Impact ◽  
Virus Infections ◽  
The Third ◽  
Complex Problems ◽  
Probabilistic Technique ◽  
The World

Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals.

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