Use case

Observational studies powered by Artificial Intelligence to improve patient outcomes

Real-world data is available across a wide variety of sources, from clinical data (e.g. EMRs) to medical devices.

Integrating Real-World Data can leverage insights to support disease prevention, manage chronic diseases, and improve decision-making.

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What do we solve?

Electronic medical records (EMR) hide medical knowledge that can be exploited (trends, patterns, risk profiles, etc. The application of Natural Language Processing and Machine Learning in clinical care has high potential value for pathology researchers and pharmaceutical companies.

How do we solve it?

The dezzai Semantic AI platform will:

Describe the clinical profile of patients with a specific pathology, who are treated with the product under study.

Identify risk profiles that allow the designing of individualized therapeutic interventions to prevent further development of the disease.

Identify uncertainty factors associated with diagnostic procedures. Determine the real distribution of procedures used throughout different phases of the disease. Also, the associated pharmacological spectrum, and the reasons that lead to the change of medication.

Predict how often patients visit the clinic or are readmitted and the average length of stay. Also, segmenting patients by group according to the different clinical categories of the disease.

What do we deliver?

A clinical data mining platform for observational studies which can:

Process datasets

(structured and unstructured data, EMR, scientific papers) to identify trends, patterns, risk profiles, and costs in a pathology with specific treatments and extract relevant characteristics.

Structure information

in a graph database to represent the medical records, the importance of the concepts detected in those records, the relationship between medical concepts detected in the set, and the similarity between patients.

Structure information

in a graph database to represent the medical records, the importance of the concepts detected in those records, the relationship between medical concepts detected in the set, and the similarity between patients.

Describe clinical profiles

for target patients and patterns.

Generate reports

with data and knowledge which helps scientists or researchers determine patterns in pathologies, costs derived from treatments, and more. This can later be used in a potential scientific publication.

Generate reports

with data and knowledge which helps scientists or researchers determine patterns in pathologies, costs derived from treatments, and more. This can later be used in a potential scientific publication.