Using data mining techniques to predict hospitalization of hemodialysis patients

Hemodialysis patients might suffer from unhealthy care behaviors or long-term dialysis treatments and need to be hospitalized. If the hospitalization rate of a hemodialysis center is high, its service quality will be low. Therefore, decreasing hospitalization rate is a crucial problem for health care centers. This study combines temporal abstraction with data mining techniques for analyzing dialysis patients' biochemical data to develop a decision support system. The mined temporal patterns are helpful for clinicians to predict hospitalization of hemodialysis patients and to suggest immediate treatments to avoid hospitalization.

[1]  Andrew Kusiak,et al.  Predicting survival time for kidney dialysis patients: a data mining approach , 2005, Comput. Biol. Medicine.

[2]  TuBaoHo,et al.  Mining Hepatitis Data with Temporal Abstraction (文部科学省科学研究費特定領域研究「情報洪水時代におけるアクティブマイニングの実現」公開シンポジウム) , 2003 .

[3]  Blaz Zupan,et al.  Feature mining and predictive model construction from severe trauma patient's data , 2001, Int. J. Medical Informatics.

[4]  Carlo Combi,et al.  Data mining with Temporal Abstractions: learning rules from time series , 2007, Data Mining and Knowledge Discovery.

[5]  Vili Podgorelec,et al.  Knowledge discovery with classification rules in a cardiovascular dataset , 2005, Comput. Methods Programs Biomed..

[6]  Carolyn McGregor,et al.  Temporal abstraction in intelligent clinical data analysis: A survey , 2007, Artif. Intell. Medicine.

[7]  Moshe Koppel,et al.  Authorship verification as a one-class classification problem , 2004, ICML.

[8]  Yuval Shahar,et al.  A Framework for Knowledge-Based Temporal Abstraction , 1997, Artif. Intell..

[9]  James F. Allen.  Towards a General Theory of Action and Time , 1984, Artif. Intell..

[10]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[11]  Mu-Yen Chen,et al.  Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis , 2007, Expert Syst. Appl..

[12]  Mehmed Kantardzic,et al.  Data-Mining Concepts , 2011 .

[13]  Blaz Zupan,et al.  Intelligent Data Analysis in Medicine , 2000 .

[14]  Elpida T. Keravnou,et al.  Temporal representation and reasoning in medicine: Research directions and challenges , 2006, Artif. Intell. Medicine.

[15]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

[16]  Tu Bao Ho,et al.  Temporal Abstraction and Data Mining with Visualization of Laboratory Data , 2007, MedInfo.

[17]  Riccardo Bellazzi,et al.  Mining Healthcare Data with Temporal Association Rules: Improvements and Assessment for a Practical Use , 2009, AIME.

[18]  Yen-Liang Chen,et al.  Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism , 2004, Decision Support Systems.

[19]  Mehmed Kantardzic,et al.  Data Mining: Concepts, Models, Methods, and Algorithms , 2002 .

[20]  Blaz Zupan,et al.  Predictive data mining in clinical medicine: Current issues and guidelines , 2008, Int. J. Medical Informatics.

[21]  Ian Witten,et al.  Data Mining , 2000 .

[22]  Riccardo Bellazzi,et al.  Temporal data mining for the quality assessment of hemodialysis services , 2005, Artif. Intell. Medicine.

[23]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[24]  Jack Belzer,et al.  Encyclopedia of Computer Science and Technology , 2002 .

[25]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[26]  Peter A. Flach,et al.  Subgroup Discovery with CN2-SD , 2004, J. Mach. Learn. Res..