Use case
Artificial Intelligence applied to observational studies to improve patient outcomes
Real-world data is available in a wide variety of sources, from clinical data (i.e., from EMRs) to medical devices.
The integration of this Real-World Data can leverage insights to help with disease prevention, management of chronic diseases and to improve the decision-making process.
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 involved in their treatment.
How do we solve it?
dezzai Semantic AI platform will:
Describe the clinical profile of patients with a specific pathology who are treated with the product under study.
![](https://dezzai.com/es/wp-content/uploads/2021/12/number2.png")
Identify risk profiles that allow designing individualized therapeutic interventions to prevent the development of the disease.
Identify uncertainty factors associated with diagnostic procedures. Determine the real distribution of the procedures used throughout the different phases of the disease, as well as the associated pharmacological spectrum and the reasons that lead to the change of medication.
Predict how often patients visit the clinic or are readmitted to the hospital, as well as 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:
Process datasets
(structured and unstructured data, EMR, scientific papers) to identify trends and patterns, risk profiles, costs in a pathology with specific treatments and extract relevant characteristics.
Structures the information
in a graph-based 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.
Structures the information
in a graph-based 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.
Describes the clinical profile
of target patients and patterns.
Generate reports
with data and knowledge which helps scientists or researchers determine patterns in pathologies, costs derived from treatments, etc… which can 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, etc… which can be used in a potential scientific publication.
Benefits of applying Artificial Intelligence
The development of a retrospective observational study, starting from the data of the EMR of hospitals, by means of NLP techniques, on the medical records allows:
SPEED
A reduction of patient recruitment time by 90% with an exponential increase of the number of patients included.
DATA VOLUME
Generate non-structured databases, allowing to increase data volume processing.
COSTS
A reduction of more than 50% of costs resulting from the reduction of the time spent to recruit patients and the exploitation of big data.
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