A novel approach selected small sets of diagnosis codes with high prediction performance in large healthcare datasets.

[1]  C. Ritchie,et al.  Measuring multimorbidity beyond counting diseases: systematic review of community and population studies and guide to index choice , 2020, BMJ.

[2]  Willi Sauerbrei,et al.  State of the art in selection of variables and functional forms in multivariable analysis—outstanding issues , 2019, Diagnostic and Prognostic Research.

[3]  L. Sharples,et al.  Protocol for an observational study evaluating new approaches to modelling diagnostic information from large administrative hospital datasets , 2019 .

[4]  P. Austin,et al.  The Integrated Calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models , 2019, Statistics in medicine.

[5]  S. Normand,et al.  Comparative Effectiveness of New Approaches to Improve Mortality Risk Models From Medicare Claims Data , 2019, JAMA network open.

[6]  K. Moons,et al.  Electronic healthcare records and prognosis research , 2019, Prognosis Research in Health Care.

[7]  Richard D Riley,et al.  Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes , 2018, Statistics in medicine.

[8]  G. Prescott,et al.  Defining and measuring multimorbidity: a systematic review of systematic reviews , 2018, European journal of public health.

[9]  T. Hastie,et al.  Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data. , 2018, Journal of the National Cancer Institute.

[10]  M. Simard,et al.  Validation of the Combined Comorbidity Index of Charlson and Elixhauser to Predict 30-Day Mortality Across ICD-9 and ICD-10 , 2018, Medical care.

[11]  Georg Heinze,et al.  Variable selection – A review and recommendations for the practicing statistician , 2018, Biometrical journal. Biometrische Zeitschrift.

[12]  Alan D. Lopez,et al.  Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study , 2017, JAMA oncology.

[13]  Pia Hardelid,et al.  Data Resource Profile: Hospital Episode Statistics Admitted Patient Care (HES APC) , 2017, International journal of epidemiology.

[14]  John P. A. Ioannidis,et al.  Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review , 2017, J. Am. Medical Informatics Assoc..

[15]  M. M. Rahman,et al.  Worldwide trends in diabetes since 1980 : pooled analysis of 751 population-based measurement studies with over 4 . 4 million participants , 2016 .

[16]  David Moher,et al.  The REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement: Methods for Arriving at Consensus and Developing Reporting Guidelines , 2015, PloS one.

[17]  Sowmya R. Rao,et al.  A Clinical Prediction Model to Assess Risk for Chemotherapy-Related Hospitalization in Patients Initiating Palliative Chemotherapy. , 2015, JAMA oncology.

[18]  B. Toson,et al.  The ICD-10 Charlson Comorbidity Index predicted mortality but not resource utilization following hip fracture. , 2015, Journal of clinical epidemiology.

[19]  Diane Lacaille,et al.  A systematic review identifies valid comorbidity indices derived from administrative health data. , 2015, Journal of clinical epidemiology.

[20]  Amber E Barnato,et al.  A randomized trial of protocol-based care for early septic shock. , 2014, The New England journal of medicine.

[21]  Diane Lacaille,et al.  Validity of Myocardial Infarction Diagnoses in Administrative Databases: A Systematic Review , 2014, PloS one.

[22]  Richard M. Martin,et al.  The association of time between diagnosis and major resection with poorer colorectal cancer survival: a retrospective cohort study , 2014, BMC Cancer.

[23]  R. Mamidanna,et al.  Population-based cohort study comparing 30- and 90-day institutional mortality rates after colorectal surgery , 2013, The British journal of surgery.

[24]  H. Quan,et al.  Case definitions for acute myocardial infarction in administrative databases and their impact on in-hospital mortality rates. , 2013, Health services research.

[25]  A. Bottle,et al.  Systematic Review of Comorbidity Indices for Administrative Data , 2012, Medical care.

[26]  Ewout W Steyerberg,et al.  Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable , 2012, BMC Medical Research Methodology.

[27]  C. Salisbury,et al.  Measures of Multimorbidity and Morbidity Burden for Use in Primary Care and Community Settings: A Systematic Review and Guide , 2012, The Annals of Family Medicine.

[28]  Ara Darzi,et al.  Variation in reoperation after colorectal surgery in England as an indicator of surgical performance: retrospective analysis of Hospital Episode Statistics , 2011, BMJ : British Medical Journal.

[29]  Philip Quirke,et al.  Thirty-day postoperative mortality after colorectal cancer surgery in England , 2011, Gut.

[30]  N. Obuchowski,et al.  Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures , 2010, Epidemiology.

[31]  B. Starfield,et al.  Defining Comorbidity: Implications for Understanding Health and Health Services , 2009, The Annals of Family Medicine.

[32]  E. Steyerberg.  Clinical Prediction Models , 2008, Statistics for Biology and Health.

[33]  W. Sanderson,et al.  The coming acceleration of global population ageing , 2008, Nature.

[34]  P. Royston,et al.  Selection of important variables and determination of functional form for continuous predictors in multivariable model building , 2007, Statistics in medicine.

[35]  Eric T. Bradlow,et al.  Relationship between Medicare's hospital compare performance measures and mortality rates. , 2006, JAMA.

[36]  C. Holman,et al.  A multipurpose comorbidity scoring system performed better than the Charlson index. , 2005, Journal of clinical epidemiology.

[37]  Martin Fortin,et al.  Multimorbidity and quality of life in primary care: a systematic review , 2004, Health and quality of life outcomes.

[38]  L. Ferrucci,et al.  Untangling the concepts of disability, frailty, and comorbidity: implications for improved targeting and care. , 2004, The journals of gerontology. Series A, Biological sciences and medical sciences.

[39]  Patrick Royston,et al.  Simplifying a prognostic model: a simulation study based on clinical data , 2002, Statistics in medicine.

[40]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[41]  J. Habbema,et al.  Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.

[42]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[43]  F. Harrell,et al.  Development of a clinical prediction model for an ordinal outcome: the World Health Organization Multicentre Study of Clinical Signs and Etiological agents of Pneumonia, Sepsis and Meningitis in Young Infants. WHO/ARI Young Infant Multicentre Study Group. , 1998, Statistics in medicine.

[44]  C. Steiner,et al.  Comorbidity measures for use with administrative data. , 1998, Medical care.

[45]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[46]  J. Wyatt,et al.  Commentary: Prognostic models: clinically useful or quickly forgotten? , 1995 .

[47]  P. J. Verweij,et al.  Penalized likelihood in Cox regression. , 1994, Statistics in medicine.

[48]  R. Tibshirani,et al.  An Introduction to the Bootstrap , 1995 .

[49]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[50]  C. Mackenzie,et al.  A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. , 1987, Journal of chronic diseases.

[51]  J. Lawless,et al.  Efficient Screening of Nonnormal Regression Models , 1978 .

[52]  H. Akaike.  A new look at the statistical model identification , 1974 .

[53]  N. Mantel.  Why Stepdown Procedures in Variable Selection , 1970 .

[54]  R. R. Hocking,et al.  Selection of the Best Subset in Regression Analysis , 1967 .

[55]  D. Cox.  Two further applications of a model for binary regression , 1958 .

[56]  G. Brier.  VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .