Improving risk grouping rules for prostate cancer patients with optimization

Data mining techniques provide a popular and powerful toolset to address both clinical and management issues in the area of health care. This paper describes the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. The cluster analysis based model is suggested and an application of non-smooth non-convex optimization techniques to solve this model is discussed. It is demonstrated that using the optimization based approach to data mining of a prostate cancer patients database can lead to generation of a significant amount of new knowledge that can be effectively utilized to enhance clinical decision making.E

Association Rules Mining Based Clinical Observations

Healthcare institutes enrich the repository of patients' disease related information in an increasing manner which could have been more useful by carrying out relational analysis. Data mining algorithms are proven to be quite useful in exploring useful correlations from larger data repositories. In this paper we have implemented Association Rules mining based a novel idea for finding co-occurrences of diseases carried by a patient using the healthcare repository. We have developed a system-prototype for Clinical State Correlation Prediction (CSCP) which extracts data from patients' healthcare database, transforms the OLTP data into a Data Warehouse by generating association rules. The CSCP system helps reveal relations among the diseases. The CSCP system predicts the correlation(s) among primary disease (the disease for which the patient visits the doctor) and secondary disease/s (which is/are other associated disease/s carried by the same patient having the primary disease).Comment: 5 pages, MEDINFO 2010, C. Safran et al. (Eds.), IOS Pres

APPLYING OPERATIONS RESEARCH MODELS TO PROBLEMS IN HEALTH CARE

Intensity- modulated radiation therapy is a form of cancer treatment that directs high energy x-rays to irradiate a tumor volume. In order to minimize the damage to surround-ing tissue the radiation is delivered from multiple angles. The selection of angles is an NP-hard problem and is currently done manually in most hospitals. We use previously evaluated treatment plans to train a machine learning model to sort potential treatment plans. By sorting potential treatment plans we can find better solutions while only evalu-ating a fifth as many plans. We then construct a genetic algorithm and use our machine learning models to search the space of all potential treatment plans to suggest a potential best plan. Using the genetic algorithm we are able to find plans 2% better on average than the previously best known plans. Proton therapy is a new form of radiation therapy. We simulated a proton therapy treatment center in order to optimize patient throughput and minimize patient wait time. We are able to schedule patients reducing wait times between 20% and 35% depending on patient tardiness and absenteeism. Finally, we analyzed the impact of operations research on the treatment of pros-tate cancer. We reviewed the work that has been published in both operations research and medical journals, seeing how it has impacted policy and doctor recommendations