Name
Title | ||
|---|---|---|
Integrated Data Management and Analysis Environment for Medical Longitudinal Research with Machine Learning Based Prediction Models |
Abstract | ||
|---|---|---|
In a longitudinal medical study, various types of data and biomaterial samples are collected in frequent intervals, and are used to analyze factors and pathways leading to a defined disease or a group of diseases. Large volume of complex data consisting of medical history and biochemical analysis results are typically collected in such studies.Our research shows that if the appropriate data model is co-designed with the research goals, it is possible to create a generalized model which allows integrating six tasks of the study: collecting, storing, managing, and analyzing data and samples, and introducing machine learning tools to create predictive models, which in turn assist in the previous tasks. We have implemented this model as a system, which integrates individual processes, minimizes human error, and conforms to changes in the study. This clearly improves the quality, interpretability, reliability and efficiency in understanding the development of the disease. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CSIE.2009.987 | CSIE |
Keywords | Field | DocType |
diseases,learning (artificial intelligence),medical administrative data processing,biochemical analysis,data analysis,integrated data management,machine learning tool,medical history,medical longitudinal research,prediction model,Disease prediction,Expert system | Data modeling,Data mining,Interpretability,Computer science,Expert system,Human error,Data type,Artificial intelligence,Predictive modelling,Data model,Data management,Machine learning | Conference |
Volume | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mika Laaksonen | 1 | 36 | 2.22 |
| Barbara Simell | 2 | 0 | 0.34 |
| Tapio Salakoski | 3 | 1513 | 106.70 |
| Olli Simell | 4 | 2 | 0.73 |