Machine learning solutions

Medical Prognosis

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Medical prognosis must be carefully performed for older patients because they can be more sensitive to aggressive medical treatment.

For that reason, studying in depth all possible treatment scenarios allows doctors to choose the most appropriate treatment for each patients.

Although medical prognosis typically receives less attention than medical diagnosis, many clinical decisions are not fully informed unless medical prognosis is considered.

The predictive model is calculated from a data set that contains as input variables the age of the patient, diseases that the patient had in the past, environmental 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.


Study the historical survival rates on patients according to patients circumstances and treatment.


Discover which are the treatments and environmental conditions that favor a faster recovery.


Forecast the disease evolution of the patients based on their medical history and previous experiences.


Prevent diseases, increase survival rate and improve early treatment.

Neural networks are capable of learning from clinical data and make predictions of new patients.

This allows us to make appropriate decisions for each patient based on their particular characteristics.

Neural Designer allows to build predictive models that can help doctors to choose the best possible treatment for each patient.

The neural network, based on this particular variables of each patient is able to predict their evolution and helps 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 dataset 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.

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