Biomedical subjects

M Kayaalp

Publications and source records attributed to M Kayaalp.

3 recordsLinked to original sources

PubMed2000

Predicting ICU mortality: a comparison of stationary and nonstationary temporal models.

OBJECTIVE: This study evaluates the effectiveness of the stationarity assumption in predicting the mortality of intensive care unit (ICU) patients at the ICU discharge. DESIGN: This is a comparative study. A stationary temporal Bayesian network learned from data was compared to a set of (33) nonstationary temporal Bayesian networks learned from data. A process observed as a sequence of events is stationary if its stochastic properties stay the same when the sequence is shifted in a positive or negative direction by a constant time parameter. The temporal Bayesian networks forecast mortalities of patients, where each patient has one record per day. The predictive performance of the stationary model is compared with nonstationary models using the area under the receiver operating characteristics (ROC) curves. RESULTS: The stationary model usually performed best. However, one nonstationary model using large data sets performed significantly better than the stationary model. CONCLUSION: Results suggest that using a combination of stationary and nonstationary models may predict better than using either alone.

Artificial Intelligence↗

PubMed1999

Identifying patient subgroups with simple Bayes'.

Medical records can form the basis of retrospective studies, be used to evaluate hospital practices and guidelines, and provide examples for teaching medicine. Each of these tasks presumes the ability to accurately identify patient subgroups. We describe a method for selecting patient subgroups based on the text of their medical records and demonstrate its effectiveness. We also describe a modification of the basic system that does not assume the existence of a preclassified training set, and illustrate its effectiveness in one retrieval task.

Artificial Intelligence↗

PubMed1998

Using computer modeling to help identify patient subgroups in clinical data repositories.

OBJECTIVE: The ability to accurately and efficiently identify patient cases of interest in a hospital information system has many important clinical, research, educational and administrative uses. The identification of cases of interest sometimes can be difficult. This paper describes a two-stage method for searching for cases of interest. DESIGN: First, a Boolean search is performed using coded database variables. The user classifies the retrieved cases as being of interest or not. Second, based on the user-classified cases, a computer model of the patient cases of interest is constructed. The model is then used to help locate additional cases. These cases provide an augmented training set for constructing a new computer model of the cases of interest. This cycle of modeling and user classification continues until halted by the user. MEASUREMENTS: This paper describes a pilot study in which this method is used to identify the records of patients who have venous thrombosis. RESULTS: The results indicate that computer modeling enhances the identification of patient cases of interest.

Bayes Theorem↗