This paper is in the following e-collection/theme issue:

Reviews in Medical Informatics (93) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Electronic Health Records (1068)

Published on in Vol 5, No 1 (2017): Jan-Mar

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

Authors of this article:

Anis Sharafoddini1 Author Orcid Image ;   Joel A Dubin1, 2 Author Orcid Image ;   Joon Lee1 Author Orcid Image
Article Authors Cited by (68) Tweetations (13) Metrics

Journals

  1. Parimbelli E, Marini S, Sacchi L, Bellazzi R. Patient similarity for precision medicine: A systematic review. Journal of Biomedical Informatics 2018;83:87 View
  2. Chen X, Garcelon N, Neuraz A, Billot K, Lelarge M, Bonald T, Garcia H, Martin Y, Benoit V, Vincent M, Faour H, Douillet M, Lyonnet S, Saunier S, Burgun A. Phenotypic similarity for rare disease: Ciliopathy diagnoses and subtyping. Journal of Biomedical Informatics 2019;100:103308 View
  3. Tokodi M, Shrestha S, Bianco C, Kagiyama N, Casaclang-Verzosa G, Narula J, Sengupta P. Interpatient Similarities in Cardiac Function. JACC: Cardiovascular Imaging 2020;13(5):1119 View
  4. Tashkandi A, Wiese I, Wiese L. Efficient In-Database Patient Similarity Analysis for Personalized Medical Decision Support Systems. Big Data Research 2018;13:52 View
  5. Hier D, Kopel J, Brint S, Wunsch D, Olbricht G, Azizi S, Allen B. Evaluation of standard and semantically-augmented distance metrics for neurology patients. BMC Medical Informatics and Decision Making 2020;20(1) View
  6. Kim B, Lee J. Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review. JMIR mHealth and uHealth 2017;5(5):e69 View
  7. Balikuddembe M, Tumwesigye N, Wakholi P, Tylleskär T. Computerized Childbirth Monitoring Tools for Health Care Providers Managing Labor: A Scoping Review. JMIR Medical Informatics 2017;5(2):e14 View
  8. Zhang H, Zhu F, Dodge H, Higgins G, Omenn G, Guan Y. A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease. GigaScience 2018;7(7) View
  9. Wang N, Huang Y, Liu H, Fei X, Wei L, Zhao X, Chen H. Measurement and application of patient similarity in personalized predictive modeling based on electronic medical records. BioMedical Engineering OnLine 2019;18(1) View
  10. Seligson N, Warner J, Dalton W, Martin D, Miller R, Patt D, Kehl K, Palchuk M, Alterovitz G, Wiley L, Huang M, Shen F, Wang Y, Nguyen K, Wong A, Meric-Bernstam F, Bernstam E, Chen J. Recommendations for patient similarity classes: results of the AMIA 2019 workshop on defining patient similarity. Journal of the American Medical Informatics Association 2020;27(11):1808 View
  11. Sharafoddini A, Dubin J, Maslove D, Lee J. A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study. JMIR Medical Informatics 2019;7(1):e11605 View
  12. Xu D, Sheng J, Hu P, Huang T, Lee W. Predicting hepatocellular carcinoma recurrences: A data-driven multiclass classification method incorporating latent variables. Journal of Biomedical Informatics 2019;96:103237 View
  13. Hendrickx J, van Gastel J, Leysen H, Martin B, Maudsley S, Michel M. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacological Reviews 2020;72(1):191 View
  14. Huang M, Shah N, Yao L. Evaluating global and local sequence alignment methods for comparing patient medical records. BMC Medical Informatics and Decision Making 2019;19(S6) View
  15. Cano I, Tenyi A, Vela E, Miralles F, Roca J. Perspectives on Big Data applications of health information. Current Opinion in Systems Biology 2017;3:36 View
  16. Wentzel A, Hanula P, Luciani T, Elgohari B, Elhalawani H, Canahuate G, Vock D, Fuller C, Marai G. Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration. IEEE Transactions on Visualization and Computer Graphics 2019:1 View
  17. Tényi Á, Vela E, Cano I, Cleries M, Monterde D, Gomez-Cabrero D, Roca J. Risk and temporal order of disease diagnosis of comorbidities in patients with COPD: a population health perspective. BMJ Open Respiratory Research 2018;5(1):e000302 View
  18. Wiese I, Sarna N, Wiese L, Tashkandi A, Sax U. Concept acquisition and improved in-database similarity analysis for medical data. Distributed and Parallel Databases 2019;37(2):297 View
  19. Suo Q, Ma F, Yuan Y, Huai M, Zhong W, Gao J, Zhang A. Deep Patient Similarity Learning for Personalized Healthcare. IEEE Transactions on NanoBioscience 2018;17(3):219 View
  20. Yu W, Wang K, Hu B, Huang Y. An improved clinical data similarity algorithm based on ICD10. IOP Conference Series: Earth and Environmental Science 2019;332(3):032024 View
  21. Cho J, Shrestha S, Kagiyama N, Hu L, Ghaffar Y, Casaclang-Verzosa G, Zeb I, Sengupta P. A Network-Based “Phenomics” Approach for Discovering Patient Subtypes From High-Throughput Cardiac Imaging Data. JACC: Cardiovascular Imaging 2020;13(8):1655 View
  22. Tsaneva-Atanasova K, Diaz-Zuccarini V. Editorial: Mathematics for Healthcare as Part of Computational Medicine. Frontiers in Physiology 2018;9 View
  23. Ruan T, Lei L, Zhou Y, Zhai J, Zhang L, He P, Gao J. Representation learning for clinical time series prediction tasks in electronic health records. BMC Medical Informatics and Decision Making 2019;19(S8) View
  24. Saad M, Lee I. Leveraging hybrid biomarkers in clinical endpoint prediction. BMC Medical Informatics and Decision Making 2020;20(1) View
  25. Yong Z, Luo L, Gu Y, Li C. Implication of excessive length of stay of asthma patient with heterogenous status attributed to air pollution. Journal of Environmental Health Science and Engineering 2021;19(1):95 View
  26. Yu W, Wang K, Hu B, Huang Y. Similarity study of clinical data. Journal of Physics: Conference Series 2021;1732(1):012013 View
  27. Sharafoddini A, Dubin J, Lee J. Identifying subpopulations of septic patients: A temporal data-driven approach. Computers in Biology and Medicine 2021;130:104182 View
  28. Sisk R, Lin L, Sperrin M, Barrett J, Tom B, Diaz-Ordaz K, Peek N, Martin G. Informative presence and observation in routine health data: A review of methodology for clinical risk prediction. Journal of the American Medical Informatics Association 2021;28(1):155 View
  29. Ng K, Kartoun U, Stavropoulos H, Zambrano J, Tang P. Personalized treatment options for chronic diseases using precision cohort analytics. Scientific Reports 2021;11(1) View
  30. Tayefi M, Ngo P, Chomutare T, Dalianis H, Salvi E, Budrionis A, Godtliebsen F. Challenges and opportunities beyond structured data in analysis of electronic health records. WIREs Computational Statistics 2021;13(6) View
  31. Xu D, Sheng J, Hu P, Huang T, Hsu C. A Deep Learning–Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients. IEEE Journal of Biomedical and Health Informatics 2021;25(6):2260 View
  32. Oei R, Fang H, Tan W, Hsu W, Lee M, Tan N. Using Domain Knowledge and Data-Driven Insights for Patient Similarity Analytics. Journal of Personalized Medicine 2021;11(8):699 View
  33. Wang N, Huang Y, Liu H, Zhang Z, Wei L, Fei X, Chen H. Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records. BMC Medical Informatics and Decision Making 2021;21(S2) View
  34. Gliozzo J, Mesiti M, Notaro M, Petrini A, Patak A, Puertas-Gallardo A, Paccanaro A, Valentini G, Casiraghi E. Heterogeneous data integration methods for patient similarity networks. Briefings in Bioinformatics 2022;23(4) View
  35. Huang H, Lu X, Guo W, Jiang X, Yan Z, Wang S. Heterogeneous Information Network-Based Patient Similarity Search. Frontiers in Cell and Developmental Biology 2021;9 View
  36. Lopez Pineda A, Pourshafeie A, Ioannidis A, Leibold C, Chan A, Bustamante C, Frankovich J, Wojcik G. Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patients. Journal of Biomedical Informatics 2021;113:103664 View
  37. Wang N, Wang M, Zhou Y, Liu H, Wei L, Fei X, Chen H. Sequential Data–Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development. Journal of Medical Internet Research 2022;24(1):e30720 View
  38. Gim J. A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research. International Journal of Molecular Sciences 2022;23(11):5963 View
  39. F. de Carvalho D, Kaymak U, Van Gorp P, van Riel N. A Markov model for inferring event types on diabetes patients data. Healthcare Analytics 2022;2:100024 View
  40. Oh S, Back S, Park J. Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network. Sensors 2021;22(1):131 View
  41. Omar N, Nazirun N, Vijayam B, Wahab A, Bahuri H. Diabetes subtypes classification for personalized health care: A review. Artificial Intelligence Review 2023;56(3):2697 View
  42. Sinnige A, Kittelson A, Van der Wees P, Teijink J, Hoogeboom T. Personalised Outcomes Forecasts of Supervised Exercise Therapy in Intermittent Claudication: An Application of Neighbours Based Prediction Methods with Routinely Collected Clinical Data. European Journal of Vascular and Endovascular Surgery 2022;63(4):594 View
  43. Jo H, Jun C. A personalized classification model using similarity learning via supervised autoencoder. Applied Soft Computing 2022;131:109773 View
  44. Rigdon J, Ostasiewski B, Woelfel K, Wiseman K, Hetherington T, Downs S, Kowalkowski M. Automated generation of comparator patients in the electronic medical record. Learning Health Systems 2024;8(1) View
  45. Jo H, Jun C. A Personalized Classification Model Using Similarity Learning Via Supervised Autoencoder. SSRN Electronic Journal 2022 View
  46. Ma M, Sun P, Li Y, Huo W. Predicting the risk of mortality in ICU patients based on dynamic graph attention network of patient similarity. Mathematical Biosciences and Engineering 2023;20(8):15326 View
  47. Pikoula M, Kallis C, Madjiheurem S, Quint J, Bafadhel M, Denaxas S, Le N. Evaluation of data processing pipelines on real-world electronic health records data for the purpose of measuring patient similarity. PLOS ONE 2023;18(6):e0287264 View
  48. Liu Q, Ostinelli E, De Crescenzo F, Li Z, Tomlinson A, Salanti G, Cipriani A, Efthimiou O. Predicting outcomes at the individual patient level: what is the best method?. BMJ Mental Health 2023;26(1):e300701 View
  49. Pinho X, Meijer W, de Graaf A. Deriving Treatment Decision Support From Dutch Electronic Health Records by Exploring the Applicability of a Precision Cohort–Based Procedure for Patients With Type 2 Diabetes Mellitus: Precision Cohort Study. Online Journal of Public Health Informatics 2024;16:e51092 View
  50. Ahmed M, Hasan T, Islam S, Ahmed N. Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study. JMIR Research Protocols 2024;13:e51540 View
  51. Chatton A, Bally M, Lévesque R, Malenica I, Platt R, Schnitzer M. Personalized dynamic super learning: an application in predicting hemodiafiltration convection volumes. Journal of the Royal Statistical Society Series C: Applied Statistics 2024 View

Books/Policy Documents

  1. Sekar B, Wang H. Knowledge Science, Engineering and Management. View
  2. Cinaroglu S. Analytics, Operations, and Strategic Decision Making in the Public Sector. View
  3. Wang K, Xia E, Zhao S, Huang Z, Huang S, Mei J, Li S. Explainable AI in Healthcare and Medicine. View
  4. Cinaroglu S. Research Anthology on Public Health Services, Policies, and Education. View
  5. Bolton W, Georgiou P, Holmes A, Rawson T. AI for Health Equity and Fairness. View