Browsing by Author "Mane, KK"

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    Predictive model of the treatment effect for patients with major depressive disorder
    (2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013, 2013-11-28) Akushevich, I; Kravchenko, J; Gersing, K; Mane, KK
    The model to evaluate and predict the effectiveness of treatment of the Major Depressive Disorder (MDD) was developed and estimated using MindLinc data. The clinical global impression (CGI) scale with seven categories was used to measure the patient's state. The proportional odds model was selected because of ordinal nature of the outcome. The set of predictors included i) CGI score measured at preceded visit, ii) three groups of medications (antidepressants, atypical medicine, and augmentation medicine), all categorized for appropriate number of strata (from six to nine) and their daily doses, iii) psychiatric comorbidities, iv) type of the therapy used (talk vs. medications), v) demographic variables (e.g., age group, sex), and vi) the history of the efficiency of prior treatment. More than a half of a million records with measured CGI scores and their predictors were identified in the MindLinc database and used for model estimation. The predicted model of future CGI scales was developed and evaluated for single and recurrent episodes of MDD. Significant estimates were obtained for demographic factors, history of previous SGI scales, and for comorbidity and treatment indices. The methods of causal inferences based on the inverse probability weighting approach were applied to evaluate the treatment effects. The model extensions allowing for addressing the limitations of the proportional odds model are discussed. Copyright © 2007 by the Association for Computing Machinery.
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    Visual analytics to optimize patient-population evidence delivery for personalized care
    (2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013, 2013-11-28) Mane, KK; Owen, P; Schmitt, C; Wilhelmsen, K; Gersing, K; Pietrobon, R; Akushevich, I
    Electronic medical records (EMR) can be used to identify cohorts of patients who are clinically comparable to an individual patient. In this paper, we describe an approach that applies visual analytics to EMR data to describe the clinical course for an individual patient, display outcomes for a comparable cohort stratified by treatment, and generate predictions regarding a patient's clinical course based on treatment options. The visual display of information is designed to help clinicians choose among alternative therapies based on the EMR-derived outcomes of the cohort. Copyright © 2007 by the Association for Computing Machinery.