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

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
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  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
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  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. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacological Reviews 2020;72(1):191 View
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  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
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  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
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  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 2025;74(3):617 View
  52. Gan Z, Zhou D, Rush E, Panickan V, Ho Y, Ostrouchovm G, Xu Z, Shen S, Xiong X, Greco K, Hong C, Bonzel C, Wen J, Costa L, Cai T, Begoli E, Xia Z, Gaziano J, Liao K, Cho K, Cai T, Lu J. ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis. Journal of Biomedical Informatics 2025;162:104761 View
  53. Yu D, Huang M, Kane M, Hobbs B. A patient similarity-embedded Bayesian approach to prognostic biomarker inference with application to thoracic cancer immunity. Journal of the Royal Statistical Society Series C: Applied Statistics 2025;74(3):800 View
  54. Li J, Zakka K, Booth J, Rigny L, Ray S, Cortina-Borja M, Barnaghi P, Sebire N. Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning. BMC Medical Informatics and Decision Making 2025;25(1) View
  55. Kacer E, Hariharasudan A. Evaluating AI-based breastfeeding chatbots: quality, readability, and reliability analysis. PLOS ONE 2025;20(3):e0319782 View
  56. Ong R, Ng C, Gunasekaran K, Liu H, Hsu W, Lee M, Tan N, Behnoush A. Utility of a patient similarity-based digital tool for risk communication to patients with type 2 diabetes mellitus: perspectives from primary care physicians in ambulatory care. PLOS ONE 2025;20(3):e0319992 View
  57. Roux M, Spear R, Fouard C, Haigron P. Retrieving similar cases for clinical decision support in the context of revascularization of lower limbs. International Journal of Medical Informatics 2025;201:105931 View
  58. Krikella T, Dubin J. A Personalized Predictive Model That Jointly Optimizes Discrimination and Calibration. Statistics in Medicine 2025;44(10-12) View
  59. Manuilova I, Bossenz J, Weise A, Boehm D, Döbel M, Werle S, Ustjanzew A, Reimer N, Strantz C, Unberath P, Metzger P, Pauli T, Schulze S, Hiemer S, Oguztürk I, Kamkar L, Kestler H, Busch H, Brors B, Christoph J. Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: A Scoping Review (Preprint). Journal of Medical Internet Research 2025 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

Conference Proceedings

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  2. Lei L, Zhou Y, Zhai J, Zhang L, Fang Z, He P, Gao J. 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). An Effective Patient Representation Learning for Time-series Prediction Tasks Based on EHRs View
  3. Garcia-Gallo J, Fonseca-Ruiz N, Celi L, Duitama-Munoz J. 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). One-Year Mortality Prediction in ICU Patients with Diagnosis of Sepsis Driven by Population Similarities View
  4. Kornaropoulos E, Efstathopoulos P. 2019 IEEE European Symposium on Security and Privacy (EuroS&P). The Case of Adversarial Inputs for Secure Similarity Approximation Protocols View
  5. Sebastian Y, Chew J, Tiong X, Raman V, Fong A, Then P. Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication. Anomaly Detection from Diabetes Similarity Graphs using Community Detection and Bayesian Techniques View
  6. Memarzadeh H, Ghadiri N, Zarmehr S. 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE). A Graph Database Approach for Temporal Modeling of Disease Progression View
  7. Kleinau A, Flügel S, Pryss R, Vogel C, Engelke M, Schlee W, Unnikrishnan V, Spiliopoulou M. 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). Predicting Patient-Based Time-Dependent Mobile Health Data View
  8. Liu Y, Wei X, Ng S, Zhang T, Chen M, Tang X. 2023 International Joint Conference on Neural Networks (IJCNN). SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding View
  9. Zhang X, Lei Y, Li G, Wang S, Li H. 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). Combining Reverse Temporal Attention Mechanism and Dynamic Similarity Analysis for Disease Prediction View
  10. Rao R, Shaik R. INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING & COMMUNICATION ENGINEERING SYSTEMS: SPACES-2021. Review: Transformation of health care records with big data analytics View
  11. Krastev I, Vetova S, Budakova D. 12TH INTERNATIONAL SCIENTIFIC CONFERENCE “TECHSYS 2023” – ENGINEERING, TECHNOLOGIES AND SYSTEMS. Protein sequence alignment analysis using “biosequence alignments” database and bioinformatics system View
  12. Ponciano J, Cazzolato M, Gutierrez M, Traina C, Traina A. 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS). A symptom-based community-weighted similarity approach for inpatient health condition monitoring View
  13. Janjua J, Ghazal T, Abushiba W, Abbas S. 2024 IEEE 65th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). Optimizing Patient Outcomes with AI and Predictive Analytics in Healthcare View