Artificial Intelligence in CardioThoracic Sciences (AICTS)
Organisational unit: Research group
Research areas and keywords
UKÄ subject classification
Artificial Intelligence in Cardiothoracic Science was funded 2006. Traditionally, medical decisions are based on the combined strength of clinical facts and the experience of the clinician. A growing understanding of the molecular, genetic and biochemical basis of diseases have greatly increased the degree of complexity in medical decision-making. To identify risk factors in medical registers, a non-linear method such as artificial neural network (ANN) may better describe the correlations between different health risk factors. A research area with this high degree of complexity; to identify, optimize and simulate outcomes is strongly dependent on scientific computing using large scalable high-speed computing systems.
The general aim for our research group, using large and unique global medical databases, to bring the use of artificial neural networks (ANNs) and simulation techniques in risk stratification research a step further, in achieve a higher quality of treatment and improve the outcome for patients with cardiothoracic diseases.
Recent research outputs
Accelerating Biomarker Discovery Through Electronic Health Records, Automated Biobanking, and ProteomicsWells, Q. S., Gupta, D. K., J. Gustav Smith, Collins, S. P., Storrow, A. B., Ferguson, J., Maya Landenhed Smith, Pulley, J. M., Collier, S., Wang, X., Roden, D. M., Gerszten, R. E. & Wang, T. J., 2019, In : Journal of the American College of Cardiology. 73, 17, p. 2195-2205 11 p.
Research output: Contribution to journal › Article
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