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.
, V. Jernryd
, G. Qin
, Paskevicius, A., Metzsch, C., D. Medved
, T. Sjöberg
& S. Steen
, 2020 apr
, I : The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation. 39
, s. S135 1 s.
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Galozy, A., Nowaczyk, S., Sant'Anna, A., Mattias Ohlsson
& Lingman, M., 2020 apr
, I : International Journal of Medical Informatics. 136
Forskningsoutput: Tidskriftsbidrag › Artikel i vetenskaplig tidskrift