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Jan 08, 2023 •103 likes •259 views

WEBIOMED Company K -SkAI AI-enabled predictive analytics and risk score in healthcare PROBLEM NON-COMMUNICABLE DISEASES ( NCD ) ARE THE MOST IMPORTANT CAUSE OF MORTALITY IN ALL COUNTRIES: of all deaths 71% $ Trillions - in the world

  1. WEBIOMED Company «K -SkAI » AI-enabled predictive analytics and risk score in healthcare

  2. PROBLEM NON-COMMUNICABLE DISEASES ( NCD ) ARE THE MOST IMPORTANT CAUSE OF MORTALITY IN ALL COUNTRIES: of all deaths 71% $ Trillions - in the world National healthcare costs Proportional NCD mortality 4 TYPES OF DISEASES Cardiovascular diseases 20% ( every third death in the world ) 44% Main GOAL is to reduce the total mortality rate of these diseases by 25% until 2025. Cancer diseases 4% Total NCD deaths DIRECTIONS: 41 million people • Comprehensive prevention 10% Respiratory diseases • Risk factors prediction • Informing the population about Diabetes 22% suspected diseases at an early stage Other diseases (Alzheimer's d., Parkimson’s d.) World Health Organization, 2018. https://apps.who.int/iris/handle/10665/274512

  3. Webiomed 1 Webiomed is cloud-based service Webiomed is the first Russian software that was registrated as a medical device in the field of artificial intelligent Technology features: integration with Electronic health records • (EHR) extraction of full EHR documents and • features Clinical advantages: complexed approach to the analysis of • the text data (including NLP ) • various disease assessment producing Data Sets for ML • • integration of different approaches to analysis de-identified electronic health • the clinical conditions records to determine diseases and clinical • own methods to the healthcare conditions management risk factors prediction by machine • • targeted recommendations to learning and deep learning models physicians and patients http://webiomed.ai/

  4. WEBIOMED PRODUCTS Webiomed.CheckUp 1 Prediction of suspected disease based on data analysis, form recommendations for doctors and patients Webiomed.NLP 2 Extract features from unstructured formats in EHR Webiomed.DataSet 3 Accumulation of de-identified data from medical records, data set production 4 ACCUMULATED DATA 25 million 822 thousand 6 million The service provides risk assessments of various diseases, including the development of atherosclerosis and its complications, the risk of thromboembolic complications in patients with arrhythmias, the risk of cardiac arrest in hospitalized patients, the total risk of atherosclerosis and its complications, the risk of thromboembolic complications in cardiac arrhythmias. During the COVID-19 pandemic, a very relevant option is to detect the severity of patients with pneumonia. A model for the automatic detection of suspicion on COVID-19, as well as an assessment of the patient's risk group for coronavirus infection is being developed. Identification of high-risk patients allow us to predict the risks of severe COVID-19, the need for mechanical ventilation / hospitalization in the ICU, and death.

  5. HOW DOES WEBIOMED WORK? Data preprocessing (format-logical control, NLP) Data analysis (algorithms, scales, neural models) Extraction of risk factors De-identified Predictions of the group risks electronic health records for different diseases (JSON) Webiomed Creation of targeted recommendations to Results are sent to EHR physicians and patients EHR, (report, JSON, HTML) according to the clinical guidelines EMR

  6. DATA PROCESSING METHODS WEBIOMED RESULTS: INPUT: ANALYSIS METHODS : EHR Extraction of additional risk factors MACHINE Health examinations LEARNING Forecast of a critical event (complications) Lab tests and diagnostics Identification of hidden diseases Clinical recommendation Instrumental data Published risk assessment Prediction of the diseases suspicion Clinical examination results Regulatory requirements analysis Group risk predictions of diseases: very high, high, moderate, low Ambulance calls Clinical recommendation algorithms Types of diseases

  7. DEEP AND MACHINE LEARNING MODELS TO IMPROVE CARDIVASCULAR RISK PREDICTION GOAL: to compare both methods to CVD risk prediction based on extracted EHR data - machine learning and traditional risk scales ML TECHNOLOGY Accuracy: 78.84% ELECTRONIC HEALTH UNSTRUCTURED NLP DATA SET 1 True Positive Rate RECORD DATA 0,8 АМБУЛАТОРНАЯ КАРТА № 27916 0,6 Deep Learning ( ROC AUC = 0,75-0,76) 31 517 patients Logistic Regression ( ROC AUC =0,74-0,76) 3 652 all features patients 0,4 Framingham ( ROC AUC = 0,62-0,72) NEURAL NETWORKS MODEL CLINICAL DECISION SCORE ( ROC AUC = 0,66-0,73) SUPPORT SYSTEM (CDSS) 0,2 PROCAM ( ROC AUC = 0,60-0,69) 0 0,2 0,4 0,6 0,8 1 Individual risks False Positive Rate prediction CONCLUSION PATIENT COHORT  The machine learning outperformed a traditional clinically-used predictive model • Total – 3 652 (have all features: for CVD risk prediction. vital signs ,diagnoses, medications)  This approach was used to create a CDSS. It uses both methods: traditional risk scales and • Average age – 49,4 (21-75) models based on neural network. The system can calculate the CVD risks automatically and • Female - 68,2% recalculate immediately after adding new information to the EHR. EI. Korsakov, A. Gusev, T. Kuznetsova, D. Gavrilov, R. Novitskiy « Deep and machine learning models to improve risk prediction of cardiovascular disease using data extraction from electronic health record » /ESC Congress, Paris.2019

  8. WEBIOMED.CHECK-UP Webiomed returns the identified risk factors and the appropriate assessment of group During work with EMR physicians he can ask for artificial intelligence’s advice . They patient risk push the bottom in a workflow. EMR automatically analyzes the electronic and sends to The results shows on the system website page Webiomed de-identified data set The answer contains of detailed explanations and further recommendations for doctors and patients

  9. Webiomed extracts DATASET Social data Anamnesis and signaling information MVP version screenshot Medical documents

  10. CONSUMERS WEBIOMED B2 В B2 С B2G SERVICES FOR HEALTHCARE INSURANCE PHYCISIANCE PATIENTS MANAGERS COMPANY EHR PHARMA

  11. Intellectual property CERTIFICATES KEY PUBLICATIONS http://jtelemed.ru/article/iskusstvennyj-intellekt-v-ocenke-riskovrazvitija-serdechno-sosudistyh-zabolevanij Physician and information technologies. 2018. No. 3. pp. 45-60. Physician and information technologies, No. 3, 2017. pp. 92-105. Information society. No. 4-5, 2017, pp. 78-93 Certificate for registering Certificate for Webiomed Physician and information technologies, No. 2, 2017. pp. 60-72. PC software trademark “Clinical Decision Support System Webiomed” Healthcare Manager. 2014. No. 1. P. 51-60. Software is being registered as a medical device in Roszdravnadzor Physician and information technologies. No.5, 2011 pp. 60-76 Medical academic journal. Volume 5. No. 3. 2005. Supplement 7. pp. 64-67

  12. PROJECTS Regional pilot projects: • • • Industry projects : • • •

  13. OUR PARTNER S AWARDS and ACHIEVEMENTS SKOLKOVO The Skolkovo Innovation Center is a high technology business area in Russia Digital Health Awards PETROZAVODSK STATE UNIVERSITY Petrozavodsk State University is the Flagship University of the Republic of Karelia FEDERAL STATE BUDGET ORGANIZATION NATIONAL MEDICAL RESEARCH CENTER OF CARDIOLOGY OF THE MINISTRY OF HEALTHCARE (Russia) ПРОФ -IT.2019 NATIONAL BASE OF MEDICAL KNOWLEDGE The Association of Developers and Users of Artificial Intelligence Systems in Medicine MEDICAL PREVENTION CENTER # ИТМ YNAO Health Organization for prevent diseases 2019 ASSOCIATION OF CLINICAL PHARMACOLOGISTS This is the largest organization of clinical pharmacologists in Russia.Established in 2009 13

  14. PROJECT TEAM Novitskiy Roman Serova Larisa ANALYSIS & RESEARCH CO-FOUNDER, COMMERCIAL, FINANCE & OPERATIONS Expert in IT, math methods . Project analysis, preparation of technical documentation and Organization and legal issues, commercialization of results. analytical reports. Hirsch index – 1 Ph.D. in Computer Sciences, Hirsch Index – 7 Gusev Alexander Guseva Anna LEADER, CO-FOUNDER IT expert, project analysis,market researches, marketing Product development strategy and Market Launch. Consulting in the subject area. Ph.D. in Computer Sciences , Hirsch index – 10 Kuznetsova Tatyana Gavrilov Denis CHIEF MEDICAL OFFICER HealthCare Expert, Member of the Russian HealthCare Expert, Society of Cardiology, Member and European Ph.D. in Medicine, Hirsch index – 27 Society of Cardiology, Chairman of the Karelian Republican Branch of the Russian Society of Cardiology. Hirsh index - 5 • Developers • Korsakov Igor Testers Expert in AI, Machine Learning and NLP Ph.D. in Math & Computer Sciences, Hirsch index – 2 We are a balanced team of experienced specialists in IT, AI and Healthcare medicine!

  15. CONTACTS WEBIOMED Join us on Social networks for news and events: Vk https://webiomed.ai https://vk.com/webiomed Facebook 185031, RF, Karelia Republic, Petrozavodsk city, Varkausa street, 17 https://www.facebook.com/webiomed/ Twitter +7-814-228-08-18 https://twitter.com/webiomed Telegram https://t.me/webiomed info@webiomed.ai YouTube https://www.youtube.com/

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