The analysis of clinical data enables to understand the biological mechanisms that underlie diseases, and how risk factors influence their developent.
In this regard, machine learning allows to bring together different types of data into a single model to better diagnose diseases.
A big amount of data is currently available to clinicalists, ranging from details of clinical symptoms to various types of biochemical assays and outputs of imaging devices.
To streamline the diagnostic process in daily routine and avoid misdiagnosis, predictive analytics is being widely employed. Neural networks can handle diverse types of medical data and successfully integrate them into categorized outputs.
Advanced analytics are being widely used in the field of health.
The main challenge for predictive analytics is to use artificial intelligence to analyze clinical data to take up new models of care and new technologies promoting health and wellbeing.
Studying genetic factors, environmental influences and physiological data allows practitioners to prevent, diagnose and treat diseases more effectively to improve people's welfare.
Some diagnostic methods require invasive techniques that may cause some harm to the patient. These advanced analytics methods can help to develop diagnostic methods that are less invasive based on historic clinical data. In addition, they can be very useful to detect diseases in the first phases so doctors can act rapidly to prevent or cure it.
Neural Designer is a software that implements neural networks with a user interface. It allows to easily build predictive models from a data set.
Neural Designer is able to load a data set containing the historical clinical data, find relations between the diagnostic variables, design the best predictive model according to the complexity of the information and to test this model against new cases. If the model is decided to work well, it will be prepared to be used for preventive medical diagnose.