W Nick Street | PubMed Health

PubMed2005

Predicting outcomes of hospitalization for heart failure using logistic regression and knowledge discovery methods.

The purpose of this study is to determine the best prediction of heart failure outcomes, resulting from two methods -- standard epidemiologic analysis with logistic regression and knowledge discovery with supervised learning/data mining. Heart failure was chosen for this study as it exhibits higher prevalence and cost of treatment than most other hospitalized diseases. The prevalence of heart failure has exceeded 4 million cases in the U.S.. Findings of this study should be useful for the design of quality improvement initiatives, as particular aspects of patient comorbidity and treatment are found to be associated with mortality. This is also a proof of concept study, considering the feasibility of emerging health informatics methods of data mining in conjunction with or in lieu of traditional logistic regression methods of prediction. Findings may also support the design of decision support systems and quality improvement programming for other diseases.

Databases as Topic↗

PubMed2002

Computer-generated nuclear features compared with axillary lymph node status and tumor size as indicators of breast cancer survival.

The extent to which malignant cells deviate from normal is generally accepted to be a prognostic indicator. However, assessing the degree of deviation has been subjective and poorly reproducible. Our goal is to develop a computer program for objectively measuring nuclear size, shape, and texture from histologic slides and to make the program available on the Internet. We used this program to analyze 353 histologic sections obtained from patients with invasive breast cancer who were diagnosed and treated from 1981 through 1995 and who had determinable outcomes. The median follow-up was 8.3 years. We compared the relationship of survival with our computer-derived nuclear features versus axillary lymph node status and tumor size. We believe that our results are generally applicable because our patient survival, when stratified by lymph node status, was similar to that of the 24,000 breast cancer patients in the National Cancer Institute's Surveillance, Epidemiology, and End Results program. In multivariate analysis, the strongest prognostic factor was the largest nuclear area, followed by tumor size and the extent of axillary lymph node involvement. The mean area of the 3 largest nuclei when combined with tumor size identified 30% of all breast cancer patients who had an 87% 15-year breast cancer-specific survival. Inclusion of lymph node status added little to this 2-factor model. Routine axillary lymph node surgery for prognostic purposes may become unnecessary, because nuclear features may provide sufficient information.

Axilla↗