Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer progn
- PDF / 2,613,056 Bytes
- 31 Pages / 439.37 x 666.142 pts Page_size
- 15 Downloads / 205 Views
Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis Marina Johnson1 · Abdullah Albizri1
· Serhat Simsek1
Accepted: 8 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Artificial Intelligence (AI) is critical for data-driven decision making to increase resource utilization, operational performance, and service quality in various industry domains, particularly in healthcare. Using AI in healthcare operations can significantly improve treatment outcomes and enhance patient satisfaction while reducing costs. In this paper, we propose a multi-stage framework to build an AI-based decision support tool that can predict the 5year survivability of lung cancer patients. We evaluate the proposed framework using the Surveillance, Epidemiology, and End Results dataset pertaining to the 1973–2015 period obtained from the National Institutes of Health. The first stage entails data preprocessing and target creation. The second stage applies six AI algorithms with feature selection through Particle Swarm Optimization and hyperparameter tuning with Cross-Validation. These Algorithms include Logistic Regression, Decision Trees, Random Forests (RF), Adaptive Boosting (AdaBoost), Artificial Neural Network, and Naïve Bayes. The results show that RF and AdaBoost models yield an AUC rate of 0.94 and outperform the other models. Stage 3 utilizes permutation importance to interpret the RF and AdaBoost models and applies Treebased Augmented Naïve Bayes to gain insights regarding the interrelations among important features. The results of Stage 3 delineate that the number of lymph nodes containing metastases), the number of tumors that patients have had in their lifetime, the patient’s age, and the microscopic composition of cells rank among the topmost important features and can significantly impact patient survivability. We think this study has significant practical implications in helping physicians predict prognosis and develop treatment plans for lung cancer patients. Keywords Artificial intelligence · Machine learning · Healthcare operations · Cancer survival prediction · Healthcare analytics
B 1
Abdullah Albizri [email protected] Feliciano School of Business, Montclair State University, Montclair, NJ, USA
123
Annals of Operations Research
1 Introduction Artificial Intelligence (AI) has recently been the center of attention among academics, industry professionals, governments, and societal institutions. A survey—regarding AI adoption—of 2360 firm executives shows that more than 62% of executives stated that their revenues improved due to the AI applications’ adoption (Cam et al. 2018). Academic studies are highlighting the significance of studying AI-based applications across all domains as well as focusing on AI impact on the operational level (Bawack et al. 2019). Specifically, AI applications are considered a game-changer and a revolutionizing transformation driver in healthcare operations (Akter et al. 202
Data Loading...
