Biomedical subjects

Andrew Kusiak

Publications and source records attributed to Andrew Kusiak.

5 recordsLinked to original sources

PubMed2006

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. Gene expression data sets for ovarian, prostate, and lung cancer were analyzed in this research. An integrated gene-search algorithm for genetic expression data analysis was proposed. This integrated algorithm involves a genetic algorithm and correlation-based heuristics for data preprocessing (on partitioned data sets) and data mining (decision tree and support vector machines algorithms) for making predictions. Knowledge derived by the proposed algorithm has high classification accuracy with the ability to identify the most significant genes. Bagging and stacking algorithms were applied to further enhance the classification accuracy. The results were compared with that reported in the literature. Mapping of genotype information to the phenotype parameters will ultimately reduce the cost and complexity of cancer detection and classification.

Algorithms↗

PubMed2006

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. The acquisition system was designed for the collection on numerous patients. Data records were created at 30s intervals. An expert-validated wellness score was computed for each data record. To efficiently analyze the data, a new metric for assessment of data utility, the combined classification quality measure, was developed. This measure assesses the impact of a feature on classification accuracy without performing computationally expensive cross-validation. The proposed measure can be also used to derive new features that enhance classification accuracy. The knowledge discovery approach allows for instantaneous prediction of interventions for the patient in an intensive care unit. The discovered knowledge can improve care of complex to manage infants by the development of an intelligent bedside advisory system.

Algorithms↗

PubMed2005

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. In this research, data from a nationwide bacillus Calmette-Gue rin (BCG) plus interferon-alpha (IFN-alpha) immunotherapy clinical trial was considered. Data mining algorithms were used to analyze the effectiveness of immunotherapy treatment and to understand the prominent parameters and their interactions. The extracted knowledge was used to build a patient recognition model for prediction of treatment outcomes. The data was analyzed to understand the impact of various parameters on the treatment outcome. A list of significant parameters such as cumulative tumor size, presence of residual disease, stages of prior bladder cancer, current state of bladder cancer, and the presence of current bladder cancer (T1) is provided. The decision-making approach outlined in the paper supplemented with additional knowledge bases will lead to a comprehensive analytical road map of the BCG/IFN-alpha immunotherapy treatment. It will provide individualized guidelines for each stage of the treatment as well as measure the success of the treatment.

Adjuvants, Immunologic↗

PubMed2005

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. Over 50 parameters may be monitored on a regular basis in providing kidney dialysis treatments. Understanding the collective role of these parameters in determining outcomes for an individual patient and administering individualized treatments allowing specific interventions is a challenge. Individual patient survival may depend on a complex interrelationship between multiple demographic and clinical parameters, medications, medical interventions, and the dialysis treatment prescription. In this research, data preprocessing, data transformations, and a data mining approach are used to elicit knowledge about the interaction between many of these measured parameters and patient survival. Two different data mining algorithms were employed for extracting knowledge in the form of decision rules. These rules were used by a decision-making algorithm, which predicts survival of new unseen patients. Important parameters identified by data mining are interpreted for their medical significance. The concepts introduced in this research have been applied and tested using data collected at four dialysis sites. The computational results are reported in the paper.

Algorithms↗

PubMed2004

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. One such area is the identification of gene/SNP patterns impacting cure/drug development for various diseases. METHODS: A new approach for predicting drug effectiveness is presented. The approach is based on data mining and genetic algorithms. A global search mechanism, weighted decision tree, decision-tree-based wrapper, a correlation-based heuristic, and the identification of intersecting feature sets are employed for selecting significant genes. RESULTS: The feature selection approach has resulted in 85% reduction of number of features. The relative increase in cross-validation accuracy and specificity for the significant gene/SNP set was 10% and 3.2%, respectively. CONCLUSION: The feature selection approach was successfully applied to data sets for drug and placebo subjects. The number of features has been significantly reduced while the quality of knowledge was enhanced. The feature set intersection approach provided the most significant genes/SNPs. The results reported in the paper discuss associations among SNPs resulting in patient-specific treatment protocols.

Algorithms↗