Clinical and radiological septic joint analysis of spontaneous sternoclavicular joint infections: achieving the best outcomes-a systems engineering approach.

Objectives: Spontaneous sternoclavicular joint infection (SSCJI) is a rare and poorly understood disease process. This study aims to identify factors guiding effective management strategies for SSCJI by using data mining.

Methods: An Institutional Review Board-approved retrospective review of patients from 2 large hospitals (2010-2022) was conducted.

A data-mining approach to predict influent quality.

In wastewater treatment plants, predicting influent water quality is important for energy management. The influent water quality is measured by metrics such as carbonaceous biochemical oxygen demand (CBOD), potential of hydrogen, and total suspended solid. In this paper, a data-driven approach for time-ahead prediction of CBOD is presented.

A data-driven model for maximization of methane production in a wastewater treatment plant.

A data-driven approach for maximization of methane production in a wastewater treatment plant is presented. Industrial data collected on a daily basis was used to build the model. Temperature, total solids, volatile solids, detention time and pH value were selected as parameters for the model construction.

Relabeling algorithm for retrieval of noisy instances and improving prediction quality.

A relabeling algorithm for retrieval of noisy instances with binary outcomes is presented. The relabeling algorithm iteratively retrieves, selects, and re-labels data instances (i.e.

Multiobjective optimization of temporal processes.

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data.

Cancer gene search with data-mining and genetic algorithms.

Cancer leads to approximately 25% of all mortalities, making it the second leading cause of death in the United States. Early and accurate detection of cancer is critical to the well being of patients. Analysis of gene expression data leads to cancer identification and classification, which will facilitate proper treatment selection and drug development.

Hypoplastic left heart syndrome: knowledge discovery with a data mining approach.

Hypoplastic left heart syndrome (HLHS) affects infants and is uniformly fatal without surgical palliation. Post-surgery mortality rates are highly variable and dependent on postoperative management. A data acquisition system was developed for collection of 73 physiologic, laboratory, and nurse-assessed parameters.

Patient-recognition data-mining model for BCG-plus interferon immunotherapy bladder cancer treatment.

Bladder cancer is the fifth most common malignant disease in the United States with an annual incidence of around 63,210 new cases and 13,180 deaths. The cost for providing care for patients with bladder cancer disease is high. Bladder cancer treatment options such as immunotherapy, chemotherapy, radiation therapy, transurethral resection, and cystectomy, are used with varying success rates.

Predicting survival time for kidney dialysis patients: a data mining approach.

The cost for providing care for patients on hemodialysis due to end stage kidney disease is high. Finding ways to improve patient outcomes and reduce the cost of dialysis is important. Dialysis care is intricate and multiple factors may influence patient survival.

Data mining and genetic algorithm based gene/SNP selection.

Objective: Genomic studies provide large volumes of data with the number of single nucleotide polymorphisms (SNPs) ranging into thousands. The analysis of SNPs permits determining relationships between genotypic and phenotypic information as well as the identification of SNPs related to a disease. The growing wealth of information and advances in biology call for the development of approaches for discovery of new knowledge.