The purpose of the project is to develop "Hippocrates" expert system for the support of physician's decision-making, the system will be used at the stage of diagnosis, prevention and treatment of various diseases.
The introduction of "Hippocrates" system into clinical practice will allow to personalize the approach to each patient, reduce the risk of medical errors and clinical complications.
A feature of the basic technologies that underlie the project and determine its novelty is the combination of technology of deep machine learning while processing large amounts of data with the United Medical Knowledge Base (UMKB). The UMKB is based on the systems of classifiers, medical ontologies, a unique model of knowledge representation and algorithms similar to doctor's thinking.
The main advantage of "Hippocrates" is that the system retains the ability to operate with a lack of baseline data about the patient, and that is quite often encountered in clinical practice. In such cases, knowledge derived only from evidence-based medicine is often not applicable for decision- making. This requires fundamental knowledge in the field of medicine and the use of methods of deduction. This is what the doctor does when taking decisions with insufficient baseline data, he uses the deduction. Therefore, we have built the algorithms of our system similar to doctor's thinking. That's why we train the system step by step, we fill the knowledge base layer by layer, like a student is taught at a medical school. These are medical ontologies, classifiers, systematic knowledge in the field of anatomy, physiology, human pathophysiology, and, of course, clinical knowledge accumulated on the basis of evidence-based medicine.
How does it work? The system is integrated into the medical information system of a clinic (MIS) , it analyzes the electronic health records of patients (EHRs) and tracks the treatment process in the background mode. Based on the EHR the system forms a virtual image of a patient in a semantic representation. Depending on the amount of data received at a certain level, the reactivity and resistance of the organism are simulated. In this model, possible pathological processes are started in correspondence with the clinical picture described in the patient's EHR. After analyzing these data, the system returns a preliminary diagnosis with recommendations for additional research to clarify the diagnosis, as well as recommendations for managing the patient. With each arrival of new data about the patient (diaries, research results, etc.), his virtual image optimizes. Upon reaching a certain confidence, the system issues a clinical diagnosis with recommendations for treatment and management of the patient, taking into account his personal characteristics. On his workstation the doctor sees the recommendations of the system, as well as information about how the system generated this conclusion with a detailed visualization of the intermediate stages of solving the problem. The use of expert system in clinical practice will allow to personalize the approach to each patient and reduce the risk of medical errors and clinical complications. The special technology used to extract information from medical texts will allow the system to understand the EHR in any format and run it on the basis of any MIS. At different stages of project implementation and as the UMKB is being filled, the following individual innovative products have been developed and are being developed:
1. Electronic Clinical Pharmacologist (ECF) is a system for supporting the decision-making of a doctor for prescribing pharmacotherapy. The ECF is integrated into the medical information system of the medical institution, it keeps track of drug prescriptions in the background mode and issues recommendations on the doctor's workstation.With the use of ECF in clinics, the costs of the medical institution for the purchase of medicines are reduced through more rational prescriptions of the doctors, the risk of complications and side effects of medications are reduced, the duration of doctor's reception time is decreased, and the quality of medical care is increased. The ECF system has been developed, successfully clinically tested and is being used in medical institutions. More information about the product can be found at: ecp.umkb.com
2. The system of "smart" electronic prescriptions and control of the distribution of medicines (PHARMTAXI) - a unified network that unites various participants in the pharmaceutical industry (doctors, patients, pharmacies, pharmaceutical companies and medical institutions) into a special logistics and allows you to control information and material flows simultaneously. On the one hand, electronic prescriptions are exchanged between the links of the chain "doctor ---> patient ---> pharmacy", and on the other hand, the distribution of medicines through the supply chain "pharmaceutical producer ---> pharmacy ---> patient" is controlled. This makes it possible to check the efficiency and safety of the drug therapy prescribed to the patient, as well as the authenticity of the medications which the patient is taking.The system has been developed and is being piloted in the regions of the Russian Federation. More details about the product can be found at: pharmtaxi.com
3. Electronic Therapist (ET) isa decision support system for diagnosing diseases. Based on the patient's complaints, anamnesis and the results of laboratory research, the system determines the probable pathology, makes a preliminary diagnosis and forms an epicrisis in order to refer the patient to a relevant specialist. The "Electronic Therapist" system will function in polyclinics and can be integrated into user interface of the Unified Medical Information and Analytical System. This will allow a patient to undergo primary diagnostics and make an appointment with the necessary specialist, thus relieving the district therapists. This product is being developed.
4. The system for predicting the risks of diseases and complications (RPS). The system will determine the risks of the development of diseases and complications in the user, taking into account the individual characteristics of his organism.In case of increased risk, the system notifies the user about the likelihood of a dangerous disease and issues certain recommendations. For example, recommendations for the research to exclude the likelihood of a dangerous disease and recommendations on the methods of prevention to reduce the risk of developing the disease. The integration of the system into social networks will allow to identify users with increased risk of cardiovascular diseases and cancer in the background mode. The users leave a huge amount of information in social networks, some of which is helpful for this screening, for example, gender, age, habits, region of residence, lifestyle, etc. After asking a few more leading questions, the system will perform a full screening to identify a risk group for the particular user. Regular mass screening of the population will increase the likelihood of identifying patients at the early stages of the disease.Thus, timely prevention and early treatment will reduce mortality from cancer and cardiovascular diseases.
5. The expert system for personal medical tracking of a user- "Personal doctor." The system will provide services of a personal physician to any user, and if necessary, accompany the person from birth. Remembering the individual characteristics of its user, the system evaluates the risks of developing the most common diseases (cancer, heart attacks and strokes, sudden cardiac death, allergic reactions, infectious diseases, etc.). At elevated risk, the system alerts the user and/or the attending physician of the likelihood of dangerous diseases and gives specific recommendations for prevention. On the basis of complaints of the patient, the system determines the likely pathology and makes an appointment for the patient with a relevant specialist or the attending doctor. Tracking the performance of prescriptions of the attending doctor (taking medications, regimen, examinations) the system notifies both sides about violations.