Medical and Psychiatric Diagnosis

Neural Networks for Medical and Psychiatric Diagnosis

Neural networks are established analytical tools for bio-medical research. They have repeatedly outperformed traditional methods for pattern recognition and clinical outcome prediction, and have demonstrated numerous benefits, such as:

>trained by examples instead of rules.
>do not suffer from human fatigue and habituation.
>can identify problems quickly.
>enable analysis of conditions and diagnosis in real time.

In the field of psychiatry, this technology has been used to help diagnose patients with epilepsy, Alzheimer’s disease and other neuro-psychological disorders.

Advanced Technology for Psychiatric Diagnosis

NeuroXL Clusterizer is an add-in to Microsoft Excel that harnesses the power of artificial intelligence for clustering tasks. Patient data stored in Microsoft Excel form can be quickly analyzed and sorted into appropriate categories, facilitating diagnosis.

Due to the complexity of neural networks, many analysts have been reluctant to adopt them for medical diagnosis. NeuroXL Clusterizer removes this barrier - users do not require any prior knowledge of the technology, and since users use the familiar Excel interface, learning time is minimal, greatly reducing the interval between loading the software and performing useful clusterings.


NeuroXL Clusterizer is an affordable and easy-to-use solution for the clustering of medical and psychiatric data. It is an effective tool for diagnosis, harnessing the power of artificial intelligence to discover patterns and relationships in data that traditional methods overlook.

More information

For more information on NeuroXL Clusterizer, please visit our home page .

Useful Links

>Library of the National Medical Society - Medical Diagnosis
>CiteSeer - Neural Networks in Medical Diagnosis



New versions of NeuroXL Predictor, NeuroXL Clusterizer and NeuroXL Package released: 4.0.6

July 18, 2016


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I can definitely recommend NeuroXL software to any individual or business that would like to take advantage of the power of artificial neural networks in analyzing complex data.

Dr. Jean-Michel Jaquet