Related solutions:> Drug design.
> Microarray analysis.
> Medical diagnosis.
Although medical prognosis typically receives less attention than medical diagnosis, many clinical decisions are not fully informed unless medical prognosis is considered. Medical prognosis must be especially careful for older patients because they can be more sensitive to aggressive medical treatment. For that reason, studying in depth all possible treatment scenarios will allow doctor to choose the most appropriate treatment for each patients.
Advanced analytics, and especially neural networks, are capable of learning from medical data of all the patients and make accurate predictions. The main advantage of artificial neural networks algorithms is that they are able to generalize the knowledge learned from individuals but, at the same time, this allows them to make appropriate decisions for each of the patients based on their particular characteristics.
Advanced analysis of patients data allow doctors to perform more accurate forecasts of diseases. These forecasts could keep older people active and independent for longer and help health and care systems to remain sustainable.
Neural Designer is and advanced analytics software that uses artificial neural networks to extract useful information about the clients that will help to calculate a predictive model that can be used to help doctors to choose the best possible treatment for each patient.
The predictive model will be calculated from a data set that could contain as input variables the age of the patient, diseases that the patient had in the past, environmental characteristics,... The neural network, based on this particular variables of each patient will be able to predict their evolution and will help the doctor to decide the best treatment.
As a summary of the process that would be followed to obtain a predictive model for this case. Firstly, the data about the historical clinical data of patients are arranged in a database. This database is analyzed by Neural Designer to find patterns in the evolution of the patients depending on the followed treatment. As a result, we obtain a predictive model capable of helping us in knowing the evolution that a patient will have in the future.