Impact of diagnosis code grouping method on clinical prediction model performance: A multi-site retrospective observational study
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Michael Gao | Aman Kansal | Marshall Nichols | Suresh Balu | Mark Sendak | Kristin M Corey | Sehj Kashyap | Kristin Corey | M. Sendak | S. Balu | M. Gao | M. Nichols | Aman Kansal | Sehj Kashyap
[1] Peter Szolovits,et al. MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.
[2] J. Coselli,et al. Transcatheter aortic valve replacement using a self-expanding bioprosthesis in patients with severe aortic stenosis at extreme risk for surgery. , 2014, Journal of the American College of Cardiology.
[3] Carl van Walraven,et al. Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada , 2011, Medical care.
[4] Mehdi Jamei,et al. Predicting all-cause risk of 30-day hospital readmission using artificial neural networks , 2017, PloS one.
[5] Mário J. Silva,et al. Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text , 2018, J. Biomed. Informatics.
[6] Yan Liu,et al. Deep Learning Solutions for Classifying Patients on Opioid Use , 2017, AMIA.
[7] Ianita Zlateva,et al. Using electronic health records data to identify patients with chronic pain in a primary care setting. , 2013, Journal of the American Medical Informatics Association : JAMIA.
[8] Anna Goldenberg,et al. Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks , 2019, MLHC.
[9] Frank D. Wood,et al. Diagnosis code assignment: models and evaluation metrics , 2013, J. Am. Medical Informatics Assoc..
[10] C. Mackenzie,et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.
[11] H. Quan,et al. Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data , 2005, Medical care.
[12] C. Steiner,et al. Comorbidity measures for use with administrative data. , 1998, Medical care.
[13] John F. Hurdle,et al. Measuring diagnoses: ICD code accuracy. , 2005, Health services research.
[14] Shelley A. Rusincovitch,et al. A comparison of phenotype definitions for diabetes mellitus. , 2013, Journal of the American Medical Informatics Association : JAMIA.
[15] H. Krumholz,et al. Transition to the ICD-10 in the United States: An Emerging Data Chasm. , 2018, JAMA.
[16] Elizabeth C. Lorenzi,et al. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study , 2018, PLoS medicine.
[17] Li Li,et al. Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort , 2017, PSB.
[18] Matthew D. Lakoma,et al. Impact of ICD-10-CM Transition on Mental Health Diagnoses Recording , 2019, EGEMS.
[19] Girish N. Nadkarni,et al. Leveraging hierarchy in medical codes for predictive modeling , 2014, BCB.
[20] Lisa I. Iezzoni,et al. Risk Adjustment of Medicare Capitation Payments Using the CMS-HCC Model , 2004, Health care financing review.
[21] P. Austin,et al. The Mortality Risk Score and the ADG Score: Two Points-Based Scoring Systems for the Johns Hopkins Aggregated Diagnosis Groups to Predict Mortality in a General Adult Population Cohort in Ontario, Canada , 2011, Medical care.
[22] R. Deyo,et al. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.
[23] L. Schneider,et al. Antipsychotics, other psychotropics, and the risk of death in patients with dementia: number needed to harm. , 2015, JAMA psychiatry.