Machine learning solutions

Medical diagnosis

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 clinicians, ranging from details of clinical symptoms to various types of biochemical assays and outputs of imaging devices:

But 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 treat them.

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.

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.

DISCOVER DISEASES IN THE PAST CLINICAL DATA

Learn about the clinical data of the patients, discovering the diseases, relation between characteristics, severity...

FIND THE SYMPTHONS ASSOCIATED TO PAST DISEASES

Discover the strength of the relations between symptoms and diseases.

PREDICT POTENTIAL FUTURE DISEASES

Develop a predictive diagnostic model from the historical data collected from the patients.

PREVENT THE APPEARANCE OF FUTURE DISEASES

Use the predictive model to help in the diagnostic decisions and to improve early diagnostic.

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 analyzes 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.

In conclusion, studying genetic factors, environmental influences and physiological data allows practitioners to prevent, diagnose and treat diseases more effectively to improve people's welfare.

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