|
1
|
Walczak S, Permuth JB, Velanovich V. Analyzing Intraductal Papillary Mucinous Neoplasms Using Artificial Neural Network Methodologic Triangulation. INTERNATIONAL JOURNAL OF HEALTHCARE INFORMATION SYSTEMS AND INFORMATICS 2019. [DOI: 10.4018/ijhisi.2019100102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Intraductal papillary mucinous neoplasms (IPMN) are a type of mucinous pancreatic cyst. IPMN have been shown to be pre-malignant precursors to pancreatic cancer, which has an extremely high mortality rate with average survival less than 1 year. The purpose of this analysis is to utilize methodological triangulation using artificial neural networks and regression to examine the impact and effectiveness of a collection of variables believed to be predictive of malignant IPMN pathology. Results indicate that the triangulation is effective in both finding a new predictive variable and possibly reducing the number of variables needed for predicting if an IPMN is malignant or benign.
Collapse
Affiliation(s)
- Steven Walczak
- School of Information, University of South Florida, Tampa, USA
| | - Jennifer B. Permuth
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and R, Tampa, USA
| | - Vic Velanovich
- Department of Surgery, College of Medicine, University of South Florida, Tampa, USA
| |
Collapse
|
|
2
|
Walczak S. Artificial Neural Networks. ADVANCED METHODOLOGIES AND TECHNOLOGIES IN ARTIFICIAL INTELLIGENCE, COMPUTER SIMULATION, AND HUMAN-COMPUTER INTERACTION 2019. [DOI: 10.4018/978-1-5225-7368-5.ch004] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
This chapter examines the history of artificial neural networks research through the present day. The components of artificial neural network architectures and both unsupervised and supervised learning methods are discussed. Although a step-by-step tutorial of how to develop artificial neural networks is not included, additional reading suggestions covering artificial neural network development are provided. The advantages and disadvantages of artificial neural networks for research and real-world applications are presented as well as potential solutions to many of the disadvantages. Future research directions for the field of artificial neural networks are presented.
Collapse
|
|
3
|
Walczak S. The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijccp.2018070103] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.
Collapse
|