Ishleen Kaur1, M N Doja2, Tanvir Ahmad3. 1. Jamia Millia Islamia, New Delhi, India. Electronic address: kaur.ishleen20@gmail.com. 2. Indian Institute of Information Technology, Sonepat, India. 3. Jamia Millia Islamia, New Delhi, India. Electronic address: tahmad2@jmi.ac.in.
Abstract
BACKGROUND AND OBJECTIVE: Metastatic prostate cancer has a higher mortality rate than localized cancers. There is a need to investigate the survival outcome of metastatic prostate cancers separately. Also, the treatments undertaken by the patients affect their overall survival. The present study tries to analyze the sequence of treatments given to the patients, along with the time intervals between each set of treatments. The time when medication needs to be changed may provide some useful insights into the survival outcome of the patients. MATERIALS AND METHODS: A total of 407 metastatic prostate cancer patients' data was collected and analyzed from an Indian tertiary care center. Popular sequence mining algorithms with exact order constraint have been applied to the treatment data. Appropriate time intervals were added in the resulted frequent sequences and fed to machine learning techniques along with other clinical data. RESULTS: The study suggests that the proposed methodology of the time range based sequence mining approach gave better results than the existing methods with 84.5% accuracy and 0.89 AUC. The time intervals in the existing sequence mining algorithms can give the clinicians some useful insights into the survival analysis and in determining the best lines of treatments for a particular patient.
BACKGROUND AND OBJECTIVE:Metastatic prostate cancer has a higher mortality rate than localized cancers. There is a need to investigate the survival outcome of metastatic prostate cancers separately. Also, the treatments undertaken by the patients affect their overall survival. The present study tries to analyze the sequence of treatments given to the patients, along with the time intervals between each set of treatments. The time when medication needs to be changed may provide some useful insights into the survival outcome of the patients. MATERIALS AND METHODS: A total of 407 metastatic prostate cancerpatients' data was collected and analyzed from an Indian tertiary care center. Popular sequence mining algorithms with exact order constraint have been applied to the treatment data. Appropriate time intervals were added in the resulted frequent sequences and fed to machine learning techniques along with other clinical data. RESULTS: The study suggests that the proposed methodology of the time range based sequence mining approach gave better results than the existing methods with 84.5% accuracy and 0.89 AUC. The time intervals in the existing sequence mining algorithms can give the clinicians some useful insights into the survival analysis and in determining the best lines of treatments for a particular patient.
Authors: Ishleen Kaur; M N Doja; Tanvir Ahmad; Musheer Ahmad; Amir Hussain; Ahmed Nadeem; Ahmed A Abd El-Latif Journal: Comput Intell Neurosci Date: 2021-12-28