Colorectal Cancer Registration: Data Approach to Knowledge.

For ensuring the quality of data and facilitating data exchange between healthcare providers and professional organizations, it is necessary to define a standard data set. The main aim of this study was to define a national minimum data set for colorectal cancer in Iran. To develop this data set, a combination of literature review and two rounds of a modified Delphi technique were used. An initial checklist was proposed based on a literature review and comparative studies. Based on the literature review, main categories, including: demographic information, diagnostic information, treatment information, clinical status assessment information, and clinical trial information were proposed. In this study, the national minimum data set of colorectal cancer was collected. Developing this data set through standard contents can improve effective health information exchange for both healthcare providers and health information systems.

Discovery of Hidden Patterns in Breast Cancer Patients, Using Data Mining on a Real Data Set.

The aim is to recognize the unknown atterns in a real breast cancer dataset using data mining algorithms as a new method in medicine. Due to excessive missing data in the collection only data on 665 of 809 patients were available. The other missing values were estimated using the EM algorithm in SPSS21 software. Fields have been converted into discrete fields and finally the APRIORI algorithm has been used to analyze and explore the unknown patterns. After the rule extraction, experts in the field of breast cancer eliminated redundant and meaningless relations. 100 association rules with a confidence value of more than 0.9 explored by the APRIORI algorithm and after the clinical expert feedback, 10 clinically meaningful relations have been detected and reported. Due to the high number of risk factors, the use of data mining is effective for cancer data. These patterns provide the future study hypotheses of specific clinical studies.

Intensive Care Decision Making: Using Prognostic Models for Resource Allocation.

Accurate outcome prediction by the means of available clinical contributing factors will support researchers and administrators in realistic planning, workload determination, resource optimization, and evidence-based quality control process. This study is aimed to evaluate APACHE II and SAPS II prediction models in an Iranian population. A a prospective cross-sectional study was conducted in four tertiary care referral centers located in the top two most populated cities in Iran, from August 2013 to August 2015. The Brier score, Area under the Receiver Operating Characteristics Curve (AUC), and Hosmer-Lemeshow (H-L) goodness-of-fit test were employed to quantify models' performance. A total of 1799 patients (58.5% males and 41.5% females) were included for further score calculation. The overall observed mortality (24.4%) was more than international rates due to APACHE II categories. The Brier score for APACHE II and SAPS II were 0.17 and 0.196, respectively. Both scoring systems were associated with acceptable AUCs (APACHE II = 0.745 and SAPS II = 0.751). However, none of prediction models were fitted to dataset (H-L ρ value < 0.01). With regards to poor performance measures of APACHE II and SAPS II in this study, finding recalibrated version of current prediction models is considered as an obligatory research question before applying it as a clinical prioritization or quality control instrument.