Published on 03.03.17 in Vol 5, No 1 (2017): Jan-Mar
Works citing "Patient Similarity in Prediction Models Based on Health Data: A Scoping Review"
According to Crossref, the following articles are citing this article (DOI 10.2196/medinform.6730):
(note that this is only a small subset of citations)
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Parimbelli E, Marini S, Sacchi L, Bellazzi R. Patient similarity for precision medicine: A systematic review. Journal of Biomedical Informatics 2018;83:87
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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
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Tokodi M, Shrestha S, Bianco C, Kagiyama N, Casaclang-Verzosa G, Narula J, Sengupta PP. Interpatient Similarities in Cardiac Function. JACC: Cardiovascular Imaging 2020;13(5):1119
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Tashkandi A, Wiese I, Wiese L. Efficient In-Database Patient Similarity Analysis for Personalized Medical Decision Support Systems. Big Data Research 2018;13:52
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Hier DB, Kopel J, Brint SU, Wunsch DC, Olbricht GR, Azizi S, Allen B. Evaluation of standard and semantically-augmented distance metrics for neurology patients. BMC Medical Informatics and Decision Making 2020;20(1)
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Kim BY, Lee J. Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review. JMIR mHealth and uHealth 2017;5(5):e69
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Balikuddembe MS, Tumwesigye NM, Wakholi PK, Tylleskär T. Computerized Childbirth Monitoring Tools for Health Care Providers Managing Labor: A Scoping Review. JMIR Medical Informatics 2017;5(2):e14
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Zhang H, Zhu F, Dodge HH, Higgins GA, Omenn GS, 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)
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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)
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Seligson ND, Warner JL, Dalton WS, Martin D, Miller RS, Patt D, Kehl KL, Palchuk MB, Alterovitz G, Wiley LK, Huang M, Shen F, Wang Y, Nguyen KA, Wong AF, Meric-Bernstam F, Bernstam EV, Chen JL. 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
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Sharafoddini A, Dubin JA, Maslove DM, Lee J. A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study. JMIR Medical Informatics 2019;7(1):e11605
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Xu D, Sheng JQ, Hu PJ, Huang TS, Lee W. Predicting hepatocellular carcinoma recurrences: A data-driven multiclass classification method incorporating latent variables. Journal of Biomedical Informatics 2019;96:103237
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S, Michel MC. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacological Reviews 2020;72(1):191
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Huang M, Shah ND, Yao L. Evaluating global and local sequence alignment methods for comparing patient medical records. BMC Medical Informatics and Decision Making 2019;19(S6)
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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
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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
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Tényi , 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
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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
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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
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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
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Cho JS, Shrestha S, Kagiyama N, Hu L, Ghaffar YA, Casaclang-Verzosa G, Zeb I, Sengupta PP. A Network-Based “Phenomics” Approach for Discovering Patient Subtypes From High-Throughput Cardiac Imaging Data. JACC: Cardiovascular Imaging 2020;13(8):1655
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Tsaneva-Atanasova K, Diaz-Zuccarini V. Editorial: Mathematics for Healthcare as Part of Computational Medicine. Frontiers in Physiology 2018;9
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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)
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Saad M, Lee IH. Leveraging hybrid biomarkers in clinical endpoint prediction. BMC Medical Informatics and Decision Making 2020;20(1)
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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
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Yu W, Wang K, Hu B, Huang Y. Similarity study of clinical data. Journal of Physics: Conference Series 2021;1732(1):012013
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Sharafoddini A, Dubin JA, Lee J. Identifying subpopulations of septic patients: A temporal data-driven approach. Computers in Biology and Medicine 2021;130:104182
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Sisk R, Lin L, Sperrin M, Barrett JK, Tom B, Diaz-Ordaz K, Peek N, Martin GP. 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
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Ng K, Kartoun U, Stavropoulos H, Zambrano JA, Tang PC. Personalized treatment options for chronic diseases using precision cohort analytics. Scientific Reports 2021;11(1)
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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)
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Xu D, Sheng JQ, Hu PJ, 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
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Oei RW, Fang HSA, 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
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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)
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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)
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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
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Lopez Pineda A, Pourshafeie A, Ioannidis A, Leibold CM, Chan AL, Bustamante CD, Frankovich J, Wojcik GL. Discovering prescription patterns in pediatric acute-onset neuropsychiatric syndrome patients. Journal of Biomedical Informatics 2021;113:103664
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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
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. 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
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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
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Oh SH, Back S, Park J. Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network. Sensors 2021;22(1):131
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Omar N, Nazirun NN, Vijayam B, Wahab AA, Bahuri HA. Diabetes subtypes classification for personalized health care: A review. Artificial Intelligence Review 2023;56(3):2697
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Sinnige A, Kittelson A, Van der Wees PJ, Teijink JA, Hoogeboom TJ. 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
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Jo H, Jun C. A personalized classification model using similarity learning via supervised autoencoder. Applied Soft Computing 2022;131:109773
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Rigdon J, Ostasiewski B, Woelfel K, Wiseman KD, Hetherington T, Downs S, Kowalkowski M. Automated generation of comparator patients in the electronic medical record. Learning Health Systems 2024;8(1)
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Jo H, Jun C. A Personalized Classification Model Using Similarity Learning Via Supervised Autoencoder. SSRN Electronic Journal 2022;
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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
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Pikoula M, Kallis C, Madjiheurem S, Quint JK, Bafadhel M, Denaxas S, Le NQK. 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
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Liu Q, Ostinelli EG, 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
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Pinho X, Meijer W, de Graaf A. Deriving Treatment Decision Support from Dutch EHR: exploring the applicability of a Precision Cohort-based Procedure for Type-2 Diabetes Mellitus Patients (Preprint). Online Journal of Public Health Informatics 2023;
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According to Crossref, the following books are citing this article (DOI 10.2196/medinform.6730):
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Sekar BD, Wang H. Knowledge Science, Engineering and Management. 2018. Chapter 37:426
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. Analytics, Operations, and Strategic Decision Making in the Public Sector. 2019. chapter 5:88
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Wang K, Xia E, Zhao S, Huang Z, Huang S, Mei J, Li S. Explainable AI in Healthcare and Medicine. 2021. Chapter 2:11
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. Research Anthology on Public Health Services, Policies, and Education. 2021. chapter 18:410
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