Healthcare

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Treatment recommendation

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Medical Assistants

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Support system for optimization and personalization of drug therapy

For:

Public and private healthcare system, pharmaceutical companies

Scope:

This system is a full range of integrated solutions for the selection of the optimal type of drug, its dose, and its combination with other drugs.

Goal:

Other

AI solution to predict post-operative visual acuity for LASIK surgeries

For:

Hospitals, patients undergoing LASIK surgeries.

Scope:

Predicting post-operative visual acuity for laser-assisted in SItu keratomileusis (LASIK) surgeries from retrospective LASIK surgery data with patient follow- ups.

Goal:

Automate a Business Process

Improving the knowledge base of prescriptions for drug and non-drug therapy and its use as a tool in support of medical professionals

For:

Doctors and patients

Scope:

Providing the medical professional with methods and means that would allow, within the time allotted for the appointment of 0 patient with a known nosology, to make a high-quality choice of drugs and to formulate a prescription corresponding to good medical practices

Goal:

Improved Employee Efficiency

AI-based mapping of optical to multi-electrode catheter recordings for atrial fibrillation treatment

For:

Hospitals, cardiologists

Scope:

Predicting possible targets for atrial fibrillation ablation based on explanted human heart data of two modalities (multi-electrode mapping and near-infrared optical imaging)

Goal:

Improved Product Development / R&D

WebioMed clinical decision support system

For:

End-users (physician, nurse, laboratory technologist, pharmacist, patient) Sales and marketing team CDSS product development and maintenance team (system administrator, system developer, system architect, project manager, and system maintenance)

Scope:

Screening for cardiovascular disease risk prediction with machine and deep learning methods

Goal:

Other

Integrated recommendation solution for prosthodontic treatments

For:

Dentist Hospital

Scope:

In order to support complicated prosthetic treatments according to the patient's condition, the artificial intelligence technology provides a comprehensive analysis of the given information and situations to recommend various prosthetic treatment methods and visualize them to support doctors and patients.

Goal:

Improved Employee Efficiency

Support system for optimization and personalization of drug therapy

For:

Public and private healthcare system, pharmaceutical companies

Goal:

Other

Problem addressed

Support system for optimization of the patient's medical therapy, taking into

account their individual physiological features, type, and disease severity.

Scope of use case

This system is a full range of integrated solutions for the selection of the optimal

type of drug, its dose, and its combination with other drugs.

Description

For doctors at the present time it may be difficult to choose a

specific drug and select its optimal dosage in the treatment

of a disease. There are, however, a number of more

experienced therapists who, in their practices, may have

repeatedly experienced cases of atypical courses of disease

and characteristics of patients, and the combined

administration of several drugs.

A thorough analysis of documented cases would provide

recommendations and generalizations for these patient

groups. However, clusters of case histories for each patient

history group is desirable to be first created. It is expected

that the number of cases would be very unevenly distributed

among groups.

Although for the most typical cases recommendations are

also typical and can readily be given, including by

inexperienced (novice) doctors, for cases of diseases falling

into clusters with a small amount of data, in the presence of

individual physiological characteristics of the patient and the

presence of other drugs, the accumulation of data and

training of the AI system based on the recommendations of

doctors is of particular importance.

The main body of the analysed data is text data, namely

transcripts of the results of the analysis of patients and

doctors' appointments. However, input data can also contain

images (snapshots), which implies a more complex data

analysis based on deep and full entanglement of neural

networks.

Knowledge graph

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