Updating risk prediction tools a case study in prostate cancer

A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs.The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals.All members volunteer their time to serve on the USPSTF, and most are practicing clinicians.The Guide to Clinical Preventive Services includes U. Preventive Services Task Force (USPSTF) recommendations on screening, counseling, and preventive medication topics and includes clinical considerations for each topic.Before making a decision regarding treatment for prostate cancer it is important to estimate the likelihood that a given tumor will recur after treatment, progress, and pose a threat to life.Risk Assessment systems are not intended to replace individualized clinician-patient decision making, but rather to provide a straightforward instrument for facilitating disease risk classification in clinical decision making and in future research.Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry.Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields.

Internet Citation: Clinical Guidelines and Recommendations. The relationship between the possibility of prostate cancer and the following variables were evaluated including: age, PSA level, prostate volume, DRE finding and family history.By using chi-square analysis, multiple logistic regressions, receiver operating characteristic (ROC) curve were drawn based on the predictive scoring equation to predict the possibility of prostate cancer.Using the predictive equation, we design a normogram for predicting prostate cancer risk called prostate cancer risk calculator.All analyses were performed with SPSS, version 18.0.This system uses PSA level (blood test), Gleason grade (microscopic appearance of the cancer cells), and T stage (size of the tumor on rectal exam and/or ultrasound) to group men as low, intermediate, or high-risk.PSA more than 20, Gleason score equal or larger than 8, or clinical stage T2c-3a Limitations: Does not account for multiple risk factors For example, Patient one: Gleason 3 4, PSA 3.2, stage T1c cancer in one biopsy core Patient two: Gleason 4 3, PSA 19.2, stage T2b cancer involving eight cores • Both patients are classified as intermediate risk, althought patient two would have much higher disease risk.Results: We analyzed 92 patients with PSA (0.523 [length (cm) × width (cm) × height (cm)]) by TAUS/TRUS.DRE was classified as normal or abnormal (any prostatic nodule or induration).This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields.A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented.

Updating risk prediction tools a case study in prostate cancer