WEBIOMED Company K -SkAI CLINICAL DECISION SUPPORT SYSTEM WITH - - PowerPoint PPT Presentation

WEBIOMED

Company «K-SkAI»

CLINICAL DECISION SUPPORT SYSTEM WITH MACHINE LEARNING

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PROBLEM

44%

22% 10% 4% 20% Proportional NCD mortality

World Health Organization, 2018. https://apps.who.int/iris/handle/10665/274512

Total NCD deaths 41 million people

Cardiovascular diseases

(every third death in the world)

Cancer diseases Respiratory diseases Diabetes Other diseases (Alzheimer's d., Parkimson’s d.)

NON-COMMUNICABLE DISEASES (NCD) ARE THE MOST IMPORTANT CAUSE OF MORTALITY IN ALL COUNTRIES:

71%

• f all deaths

in the world

National healthcare costs

$ Trillions -

Main GOAL is to reduce the total mortality rate of these diseases by 25% until 2025. DIRECTIONS:

• Comprehensive prevention
• Risk factors prediction
• Informing the population about

suspected diseases at an early stage

4 TYPES OF DISEASES

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Technology features:

• integration with Electronic health records

(EHR)

• extraction of full EHR documents and

features

• complexed approach to the analysis of

the text data (including NLP)

• producing Data Sets for ML
• analysis de-identified electronic health

records to determine diseases and clinical conditions

• risk factors prediction by machine

learning and deep learning models

Webiomed

http://webiomed.ai/

Webiomed is cloud-based Clinical Decision Support System Clinical advantages:

• various disease assessment
• integration of different approaches to

the clinical conditions

• own methods to the healthcare

management

• targeted recommendations to

physicians and patients

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ADVANTAGES & POINTS OF DIFFERENTIATION

WE USE CDSS Webiomed help physicians and healthcare providers to determine various diseases and clinical conditions. Webiomed is able to assess various risks, diseases and health related factors. MISSION WE OFFER

• to reduce medical errors
• to provide high speed processing of EHR big data
• to improve the quality of the diagnostic process
• to predict high risk diseases probability and clinical conditions
• clinical assessment scales
• clinical guidelines & scientific documents
• machine learning (neural networks)

We use machine learning methods to produce new healthcare knowledge

• the use of the unified ontological platform compatible with any other CDSS

FUTURE

HOW DOES WEBIOMED WORK?

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

EHR, EMR

Webiomed

USED METHODS

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Clicinal scale based analysis Group risk predictions of diseases:

very high, high, moderate, low

Prediction of the diseases suspicion Forecast of a critical event (complications)

INPUT: Analysis methods RESULTS: Health examinations Lab tests and diagnostics Instrumental data Clinical examination results Ambulance calls

Regulatory requirements analysis Clinical recommendation algorithms Extraction of additional risk factors Identification of hidden diseases Types of diseases

ML

EVIDENCE-BASED MEDICINE

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Clinical scales

RISK OF ATHEROSCLEROSIS RISK OF OTHER CARDIOVASCULAR DISEASES

• Guidelines & scientific documents

0,2 0,4 0,6 0,8 1 0,2 0,4 0,6 0,8 1

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 RESULTS

PATIENT COHORT

• Total – 3 652 (have all features:

vital signs ,diagnoses, medications)

• Average age – 49,4 (21-75)
• Female - 68,2%

 The machine learning outperformed a traditional clinically-used predictive model for CVD risk prediction.  This approach was used to create a CDSS. It uses both methods: traditional risk scales and models based on neural network. The system can calculate the CVD risks automatically and recalculate immediately after adding new information to the EHR.

ELECTRONIC HEALTH RECORD

UNSTRUCTURED DATA

NLP

DATA SET NEURAL NETWORKS

Individual risks prediction

31 517 patients 3 652 all features patients

MODEL CLINICAL DECISION SUPPORT SYSTEM (CDSS)

Deep Learning (ROC AUC = 0,75-0,76)

Logistic Regression (ROC AUC =0,74-0,76) Framingham (ROC AUC = 0,62-0,72) SCORE (ROC AUC = 0,66-0,73) PROCAM (ROC AUC = 0,60-0,69)

True Positive Rate False Positive Rate

CONCLUSION

• 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

DEMONSTRATION OF MVP SERVICE «WEBIOMED.CHECK-UP»

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When the physician working by EMR he can ask for artificial intelligence’s advice. It requires him to push the bottom in the workflow. EMR automatically analyzes the electronic record of the patient and sends to the Webiomed de-identified request for its analysis In response to this request Webiomed returns the identified risk factors and the appropriate assessment of group patient risk The results are displayed on the system website page WEBIOMED The answer contains detailed explanations and further recommendations for the doctor and the patient

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Social data Anamnesis and signaling information Medical documents

Webiomed extracts DATASET

MVP version screenshot

PHYSICIANS INSURANCE COMPANY SERVICES FOR PATIENTS

Who needs this service ?(costumers)

PUBLIC HEALTH SYSTEMS

Opportunities for PHARMA

• Drug therapy prescribing
• Drug therapy monitoring: effects, side

effects, complications, error reduction and etc.

• Drug compatibility

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As a system for analysis and full health records extraction CDSS Webiomed will provide:

• Data Analysis: individual and general
• Data Sets implementation: important

diseases, locations, nationalities and etc.

Physicians’ support

Clinical trials, Research&Dev

Health Records Analysis:

• Diagnosis
• Conditions
• Drug therapy

PHARMACEUTICAL MANAGEMENT

Intellectual property

CERTIFICATES

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Certificate for Webiomed trademark

KEY PUBLICATIONS

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 Physician and information technologies, No. 2, 2017. pp. 60-72. Healthcare Manager. 2014. No. 1. P. 51-60. Physician and information technologies. No.5, 2011 pp. 60-76 Medical academic journal. Volume 5. No. 3. 2005. Supplement 7. pp. 64-67

Certificate for registering PC software “Clinical Decision Support System Webiomed”

Software is being registered as a medical device in Roszdravnadzor

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MEDIA ABOUT PROJECT

Artificial Intelligence helps physicians to Identify dangerous diseases https://www.rosminzdrav.ru/regional _news/11278-iskusstvennyy- intellekt-pomozhet-yamalskim- vracham-vyyavlyat-opasnye- zabolevaniya-na-rannih-stadiyah In Yamal, artificial intelligence examined 30 thousand patients https://rg.ru/2019/04/06/reg-urfo/na- iamale-iskusstvennyj-intellekt-obsledoval- 30-tysiach-pacientov.html Increase in the identification of heart disease risk factors https://www.kommersant.ru/doc/3984543

CDSS must be implemented in healthcare https://www.yanao.ru/presscenter/news/8918/

«Artificial Intelligence in medicine» Regional Scientific and Practical Conference was held in April, 2019. http://conf.nbmz.ru/

Artificial intelligence has increased the detection of cancer risk factors https://ntinews.ru/in_progress/likbez/kak-iskusstvennyy- intellekt-pomogaet-vracham-v-rabote-.html https://zdrav.fom.ru/post/zhitelej-muravlenko-kotorym- grozit-infarkt-nahodit-intellektualnaya-sistema AI finds those who have а risk of a heart attack. Will artificial intelligence replace doctors? http://www.topnews.ru/news_id_129732.h tml

PROJECTS IN RUSSIAN REGIONS

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Regional pilot projects:

• Industry projects :

OUR PARTNERS AWARDS and ACHIEVEMENTS

NATIONAL BASE OF MEDICAL KNOWLEDGE

The Association of Developers and Users of Artificial Intelligence Systems in Medicine

SKOLKOVO

The Skolkovo Innovation Center is a high technology business area in Russia

ASSOCIATION OF CLINICAL PHARMACOLOGISTS

This is the largest organization of clinical pharmacologists in Russia.Established in 2009

MEDICAL PREVENTION CENTER

YNAO Health Organization for prevent diseases

PETROZAVODSK STATE UNIVERSITY Petrozavodsk State University is the Flagship University of the Republic of Karelia

Digital Health Awards PROF-IT.2019 #IТМ 2019

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PROJECT TEAM

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Korsakov Igor

Expert in AI, Machine Learning and NLP Ph.D. in Math & Computer Sciences, Hirsch index – 2

Product development strategy and Market Launch. Consulting in the subject area. Ph.D. in Computer Sciences, Hirsch index – 10

LEADER, CO-FOUNDER

Gavrilov Denis

HealthCare Expert, Member of the Russian Society of Cardiology, Member and European Society of Cardiology, Chairman of the Karelian Republican Branch of the Russian Society of Cardiology. Hirsh index - 5

Guseva Anna

IT expert, project analysis,market researches, marketing

CO-FOUNDER, COMMERCIAL, FINANCE & OPERATIONS

Organization and legal issues, commercialization of results. Hirsch index – 1 Expert in IT, math methods . Project analysis, preparation of technical documentation and analytical reports. Ph.D. in Computer Sciences, Hirsch Index – 7

CHIEF MEDICAL OFFICER

Novitskiy Roman Gusev Alexander

ANALYSIS & RESEARCH

• Developers
• Testers

HealthCare Expert, Ph.D. in Medicine, Hirsch index – 27

Kuznetsova Tatyana Serova Larisa

We are a balanced team of experienced specialists in IT, AI and Healthcare medicine!

CONTACTS

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https://webiomed.ai 185031, RF, Karelia Republic, Petrozavodsk city, Varkausa street, 17 +7-814-228-08-18 info@webiomed.ai

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