Published on in Vol 17, No 9 (2015): September

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study

Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study

Journals

  1. Hussain W, Hussain F, Saberi M, Hussain O, Chang E. Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs. Future Generation Computer Systems 2018;89:464 View
  2. Rumsfeld J, Joynt K, Maddox T. Big data analytics to improve cardiovascular care: promise and challenges. Nature Reviews Cardiology 2016;13(6):350 View
  3. Rosella L, Kornas K, Yao Z, Manuel D, Bornbaum C, Fransoo R, Stukel T. Predicting High Health Care Resource Utilization in a Single-payer Public Health Care System. Medical Care 2018;56(10):e61 View
  4. Jödicke A, Zellweger U, Tomka I, Neuer T, Curkovic I, Roos M, Kullak-Ublick G, Sargsyan H, Egbring M. Prediction of health care expenditure increase: how does pharmacotherapy contribute?. BMC Health Services Research 2019;19(1) View
  5. Dendrou C, McVean G, Fugger L. Neuroinflammation — using big data to inform clinical practice. Nature Reviews Neurology 2016;12(12):685 View
  6. Agarwal V, Zhang L, Zhu J, Fang S, Cheng T, Hong C, Shah N. Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis. Journal of Medical Internet Research 2016;18(9):e251 View
  7. Coleman B, Fodeh S, Lisi A, Goulet J, Corcoran K, Bathulapalli H, Brandt C. Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization. Chiropractic & Manual Therapies 2020;28(1) View
  8. Wu P, Gifford A, Meng X, Li X, Campbell H, Varley T, Zhao J, Carroll R, Bastarache L, Denny J, Theodoratou E, Wei W. Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation. JMIR Medical Informatics 2019;7(4):e14325 View
  9. Baltaxe E, Czypionka T, Kraus M, Reiss M, Askildsen J, Grenkovic R, Lindén T, Pitter J, Rutten-van Molken M, Solans O, Stokes J, Struckmann V, Roca J, Cano I. Digital Health Transformation of Integrated Care in Europe: Overarching Analysis of 17 Integrated Care Programs. Journal of Medical Internet Research 2019;21(9):e14956 View
  10. Hao S, Fu T, Wu Q, Jin B, Zhu C, Hu Z, Guo Y, Zhang Y, Yu Y, Fouts T, Ng P, Culver D, Alfreds S, Stearns F, Sylvester K, Widen E, McElhinney D, Ling X. Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine. JMIR Medical Informatics 2017;5(3):e21 View
  11. Jin B, Liu R, Hao S, Li Z, Zhu C, Zhou X, Chen P, Fu T, Hu Z, Wu Q, Liu W, Liu D, Yu Y, Zhang Y, McElhinney D, Li Y, Culver D, Alfreds S, Stearns F, Sylvester K, Widen E, Ling X, Hu C. Defining and characterizing the critical transition state prior to the type 2 diabetes disease. PLOS ONE 2017;12(7):e0180937 View
  12. Jeffery A, Hewner S, Pruinelli L, Lekan D, Lee M, Gao G, Holbrook L, Sylvia M. Risk prediction and segmentation models used in the United States for assessing risk in whole populations: a critical literature review with implications for nurses’ role in population health management. JAMIA Open 2019;2(1):205 View
  13. Wiens K, Rosella L, Kurdyak P, Hwang S. Patterns and predictors of high-cost users of the health system: a data linkage protocol to combine a cohort study and randomised controlled trial of adults with a history of homelessness. BMJ Open 2020;10(12):e039966 View