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

Secondary Use of Clinical Data for Research and Surveillance (366) Electronic Health Records (1053) Big Data (240) Machine Learning (1465) Public Health (197) Statistical Methods for Surveillance and Population Health (156)

Published on in Vol 10, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35422, first published .
Clustering Diagnoses From 58 Million Patient Visits in Finland Between 2015 and 2018

Clustering Diagnoses From 58 Million Patient Visits in Finland Between 2015 and 2018

Clustering Diagnoses From 58 Million Patient Visits in Finland Between 2015 and 2018

Authors of this article:

Pasi Fränti1 Author Orcid Image ;   Sami Sieranoja1 Author Orcid Image ;   Katja Wikström2, 3 Author Orcid Image ;   Tiina Laatikainen2, 3 Author Orcid Image
Article Authors Cited by (13) Tweetations (5) Metrics

Journals

  1. Rezaei M, Fränti P. K-sets and k-swaps algorithms for clustering sets. Pattern Recognition 2023;139:109454 View
  2. Esnault C, Rollot M, Guilmin P, Zucker J. Qluster: An easy-to-implement generic workflow for robust clustering of health data. Frontiers in Artificial Intelligence 2023;5 View
  3. Alkhalaf M, Yu P, Shen J, Deng C. A review of the application of machine learning in adult obesity studies. Applied Computing and Intelligence 2022;2(1):32 View
  4. Péalat C, Bouleux G, Cheutet V, Maignan M, Provoost L, Pillet S, Mory O. Using a manifold-based approach to extract clinical codes associated with winter respiratory viruses at an emergency department. Expert Systems with Applications 2023;230:120620 View
  5. Wikström K, Linna M, Reissell E, Laatikainen T. Multimorbidity transitions and the associated healthcare cost among the Finnish adult population during a two-year follow-up. Journal of Multimorbidity and Comorbidity 2023;13 View
  6. de Maeyer R, Sieranoja S, Fränti P. Balanced k-means revisited. Applied Computing and Intelligence 2023;3(2):145 View
  7. Dhafari T, Pate A, Azadbakht N, Bailey R, Rafferty J, Jalali-najafabadi F, Martin G, Hassaine A, Akbari A, Lyons J, Watkins A, Lyons R, Peek N. A scoping review finds a growing trend in studies validating multimorbidity patterns and identifies five broad types of validation methods. Journal of Clinical Epidemiology 2024;165:111214 View
  8. Chang T, Chen Y, Lu H, Wu J, Mak K, Yu C. Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning. Medicine 2024;103(7):e37112 View
  9. Griffith L, Brini A, Muniz-Terrera G, St. John P, Stirland L, Mayhew A, Oyarzún D, van den Heuvel E. A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging. Journal of Clinical Epidemiology 2024;172:111435 View
  10. Kozbagarov O, Mussabayev R. Distributed random swap: An efficient algorithm for minimum sum-of-squares clustering. Information Sciences 2024;681:121204 View
  11. Sreenivasan S, Fang C, Flood E, Markuzon N, Sze J. Insights Into the Patient Experience of Hormone Therapy for Early Breast Cancer Treatment Using Patient Forum Discussions and Natural Language Processing. JCO Clinical Cancer Informatics 2024;(8) View
  12. Mutai I, Van Laerhoven K, Karuri N, Tewo R. Using multiple linear regression for biochemical oxygen demand prediction in water. Applied Computing and Intelligence 2024;4(2):125 View

Books/Policy Documents

  1. Ribeiro B, Curioso I, Santos R, Guede-Fernández F, Coelho P, Santos J, Fragata J, Londral A, Sousa I. Progress in Artificial Intelligence. View