Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service

Mi Jung Rho, Jihwan Park, Hyong Woo Moon, Chanjung Lee, Sejin Nam, Dongbum Kim, Choung-Soo Kim, Seong Soo Jeon, Minyong Kang and Ji Youl Lee

PLOS ONE, 2020, vol. 15, issue 8, 1-12

Abstract: Objectives: The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, we proposed a “Dr. Answer” AI software based on the clinical outcome prediction model for prostate cancer treated with radical prostatectomy. Methods: The Dr. Answer AI was developed based on a clinical outcome prediction model, with a user-friendly interface. We used 7,128 clinical data of prostate cancer treated with radical prostatectomy from three hospitals. An outcome prediction model was developed to calculate the probability of occurrence of 1) tumor, node, and metastasis (TNM) staging, 2) extracapsular extension, 3) seminal vesicle invasion, and 4) lymph node metastasis. Random forest and k-nearest neighbors algorithms were used, and the proposed system was compared with previous algorithms. Results: Random forest exhibited good performance for TNM staging (recall value: 76.98%), while k-nearest neighbors exhibited good performance for extracapsular extension, seminal vesicle invasion, and lymph node metastasis (80.24%, 98.67%, and 95.45%, respectively). The Dr. Answer AI software consisted of three primary service structures: 1) patient information, 2) clinical outcome prediction, and outcomes according to the National Comprehensive Cancer Network guideline. Conclusion: The proposed clinical outcome prediction model could function as an effective CDSS, supporting the decisions of the physicians, while enabling the patients to understand their treatment outcomes. The Dr. Answer AI software for prostate cancer helps the doctors to explain the treatment outcomes to the patients, allowing the patients to be more confident about their treatment plans.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0236553

DOI: 10.1371/journal.pone.0236553

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