title: Exploration of patterns predicting renal damage in patients with diabetes type II using a visual temporal analysis laboratory.
abstract: OBJECTIVE
  To analyze the longitudinal data of multiple patients and to discover new temporal knowledge, we designed and developed the Visual Temporal Analysis Laboratory (ViTA-Lab). In this study, we demonstrate several of the capabilities of the ViTA-Lab framework through the exploration of renal-damage risk factors in patients with diabetes type II.
  
  
  METHODS
  The ViTA-Lab framework combines data-driven temporal data mining techniques, with interactive, query-driven, visual analytical capabilities, to support, in an integrated fashion, an iterative investigation of time-oriented clinical data and of patterns discovered in them. Patterns discovered through the data mining mode can be explored visually, and vice versa. Both analysis modes are supported by a rich underlying ontology of clinical concepts, their relations, and their temporal properties. The knowledge enables us to apply a temporal-abstraction pre-processing phase that abstracts in a context-sensitive manner raw time-stamped data into interval-based clinically meaningful interpretations, increasing the results' significance. We demonstrate our approach through the exploration of risk factors associated with future renal damage (micro-albuminuria and macro-albuminuria) and their relationship to the hemoglobin A1C (HbA1C ) and creatinine level concepts, in the longitudinal records of 22 000 patients with diabetes type II followed for up to 5 years.
  
  
  RESULTS
  The iterative ViTA-Lab analysis process was highly feasible. Higher ranges of either normal albuminuria or normal creatinine values and their combination were shown to be significantly associated with future micro-albuminuria and macro-albuminuria. The risk increased given high HbA1C levels for women in the lower range of normal albuminuria, and for men in the higher range of albuminuria.
  
  
  CONCLUSIONS
  The ViTA-Lab framework can potentially serve as a virtual laboratory for investigations of large masses of longitudinal clinical databases, for discovery of new knowledge through interactive exploration, clustering, classification, and prediction.
authors.affiliation: Department of Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
authors.forename: Denis
authors.initials: D
authors.lastname: Klimov
authors.affiliation: Department of Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
authors.forename: Alexander
authors.initials: A
authors.lastname: Shknevsky
authors.affiliation: Department of Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
authors.forename: Yuval
authors.initials: Y
authors.lastname: Shahar
mesh_terms: D000419:Albuminuria / Q000150:complications*; D003404:Creatinine / Q000097:blood; D003626:Data Display; D057225:Data Mining; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000150:complications*; D003928:Diabetic Nephropathies / Q000209:etiology*; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis*; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated; D012307:Risk Factors; D014584:User-Computer Interface
chemical_list: D003404:Creatinine; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
keywords: Visual Analytics; data analysis; knowledge discovery; ontologies; temporal abstraction; temporal data mining