
|
Jie Xu(1) , Xiaolin Zhou(2) , Daping Yang (3) , Haiyan Ge(1) , Qi Wang (4) , Kang Tu (4) , Tiefang Guo (3) ‚
(1) Department of General Surgery, The Shanghai Tenth Peoples Hospital of Tongji University, Shanghai, P. R. China (2) Department of Neurology, The Shanghai First People ‚ s Hospital of Shanghai Jiao Tong University, Shanghai, P. R. China (
Objective: To facilitate tissue engineering strategies determination with informatics tools. Methods: Firstly, tissue engineering experimental data were standardized and integrated into a centralized database; secondly, we used data mining tools (e.g. artificial neural networks and decision trees) to predict the outcomes of tissue engineering strategies; thirdly, a strategy design algorithm was developed, and its efficacy was validated with animal experiments; lastly, we constructed an online database and a decision support system for tissue engineering. Results: The artificial neural networks and the decision trees respectively predicted the outcomes of tissue engineering strategies with the predictive accuracy of 95.14% and 85.26%. Following the strategies generated by computer, we cured 18 of the 20 experimental animals with a significantly lower cost than usual. Conclusion: Informatics is beneficial for realizing safe, effective and economical tissue engineering.
Machine Learning, Artificial Intelligence, informatics, Database, Tissue engineering
| 1. | ||
H.F. Marin1, R. Carr2 IMIA Yearbook 2008 2008 3 1: 25-28 | ||
| 2. | ||
J. Laurikkala (1) , M. Juhola (1), S. Lammi (2) , K. Viikki (1) Methods of Information in Medicine 1999 38 2: 125-131 | ||
| 3. | ||
T. Aper (1, 2), C. Puschmann (3), H. Mertsching (3), A. Haverich (3), O.E. Teebken (1, 2) Phlebologie 2004 33 5: 160-165 | ||