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Kyle Perkins

Publications and source records attributed to Kyle Perkins.

2 recordsLinked to original sources

Using a Rasch scale to characterize the clinical features of patients with a clinical diagnosis of uncertain, probable, or possible Alzheimer disease at intake.

OBJECTIVE: This study examined the clinical features of patients with clinical diagnoses of probable Alzheimer disease (AD), possible AD, and uncertain. DESIGN: Case study comparing three groups of AD patients diagnosed at their initial visit to an Alzheimer outpatient clinic. SETTING: Southern Illinois University School of Medicine's Center for Alzheimer Disease and Related Disorders (CADRD) assessment sites (20) in rural Illinois. PARTICIPANTS: 300 patients assessed at CADRD between January 1, 1994 and July 1, 2000. MEASUREMENTS: Patients were given an extensive clinical battery consisting of physical and neurologic examination, mental status testing including the Mini-Mental State Exam (MMSE), Short Blessed Dementia (SBD) and Blessed Dementia Scale (ADL), medical history evaluation, and laboratory tests. Other data included age at visit, gender, and medical history variables. RESULTS: Mean MMSE, SBD, and ADL scores differed significantly between groups (p's < 0.01). In all three cognitive tests, the uncertain group was the least impaired while the probable AD group was the most impaired. A Rasch model indicated that only the cognitive measures were useful in discriminating between the three diagnostic groups. CONCLUSION: In general, probable AD patients were distinguished from possible AD patients by the severity of their dementia as measured by the MMSE, ADL and SBD as well as Hachinski-Ischemic Score (HIS) scores. A Rasch model did well at predicting group membership based upon dementia measures only. The uncertain group differed from the AD groups in age and dementia severity as measured by the MMSE, ADL and SBD. Noting differences between this and previous studies, we speculate disparity may be related to differences in population ethnicity.

Activities of Daily Living↗

Risk-factor fusion for predicting multifactorial diseases.

A generalized classification methodology is developed to predict the presence or absence of a multifactorial disease from a set of risk factors thought to be correlated with the disease. The methodology includes fusion to combine risk factors into a single feature vector, normalization to overcome the problems associated with fusing features which have different formats and ranges, discrete Karhunen-Loeve transform (DKLT)-based transformation to facilitate parametric classifier development, the selection of features with high interclass separations, and the design of parametric classifiers. The validity of the method is demonstrated by applying it to predict the occurrence of gout from 14 risk factors. Cross-validation evaluations on 96 patients, 48 clinically diagnosed to have gout and 48 diagnosed to not have gout, showed that an average classification accuracy of 75.7% can be obtained. Even more promising is that higher classification accuracies can be achieved through the careful selection of the DKLT transformation matrix which in turn involves selecting design sets that are good representatives of the gout and nongout classes. It is concluded that the generalized methodology developed in this paper is quite effective in predicting multifactorial diseases and can, therefore, assist/support a physician in diagnosing a multifactorial disease.

Diagnosis, Differential↗