AIMC Topic: Decision Support Systems, Clinical

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Deep Learning Approach for Assessment of Bladder Cancer Treatment Response.

Tomography (Ann Arbor, Mich.)
We compared the performance of different Deep learning-convolutional neural network (DL-CNN) models for bladder cancer treatment response assessment based on transfer learning by freezing different DL-CNN layers and varying the DL-CNN structure. Pre-...

[Cold thinking in the boom of artificial intelligence].

Zhonghua wai ke za zhi [Chinese journal of surgery]
Artificial intelligence clinical decision-support system is an important direction of artificial intelligence in the medical field. Both international and domestic researchers are exploring the application value of intelligent decision-making system ...

Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases.

Technology and health care : official journal of the European Society for Engineering and Medicine
In 2005, global cardiovascular diseases caused 30% of deaths in Europe, which is 46% of total deaths for all death groups. Today, according to the International Adult Diabetes Federation, 20% to 25% of the adult population in the world has Metabolic ...

Introduction of a Pathophysiology-Based Diagnostic Decision Support System.

Studies in health technology and informatics
Many current Clinical Decision Support Systems which assist clinical diagnosis, are based on a causal condition-symptom relation. To reach more diagnostic precision Ada's Deep Reasoning is substituting this approach with the use of a model based on p...

Towards a Clinical Analytics Adoption Maturity Framework for Primary Care.

Studies in health technology and informatics
Clinical decision support systems are evolving with growing analytics capabilities towards pervasive use of artificial intelligence. Maturity models can guide the adoption of these new technologies in clinical practice to improve patient outcomes in ...

Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets.

Journal of the American Medical Informatics Association : JAMIA
Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integrating clinical knowledge with machine learning can facilitate effective development and maintenance...

[Applications of machine learning in clinical decision support in the omic era].

Yi chuan = Hereditas
With the development of the omic technologies, the acquisition approaches of various biological data on different levels and types are becoming more mature. As a large amount of data will be produced in the process of diagnosis and treatment of disea...