Ai applied in healthcare
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This document discusses the application of machine learning in healthcare. It begins with an introduction of the author and their background in machine learning engineering. It then discusses the UN Sustainable Development Goals around health and highlights non-communicable and infectious diseases as areas machine learning could help address. The document outlines how machine learning can help expand medical knowledge, disseminate information, enable personalized medicine, and increase patient engagement. It also discusses best practices for business understanding, data modeling, and feature engineering when applying machine learning in healthcare.
Ai applied in healthcare
- 2. WHO AM I? Javier Samir Rey Rodríguez ● Systems engineer. ● Postgraduate studies software building. ● Mooc-learner (40 courses finished). ● Machine learning engineer - Direktio. ● Co-organizer meetup ML and data science Bogota (COL). ● Currently master degree data science (UOC). [email protected] javier-samir-rey-7104195 github/jasam jasam
- 5. “If there is no health, there is nothing.” Dr. Tedros Adhanom Ghebreyesus Director-general of the World Health Organization.
- 6. “Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondegaard, Senior Vice President and Global Head of Research for Gartner.
- 7. Source: United Nations - Sustainable Development Goals SUSTAINABLE DEVELOPMENT GOALS
- 8. 3 - GOOD HEALTH AND WELL-BEING “Ensure healthy lives and promoting well-being for all at all ages.” ● Reproductive maternal and child health. ● Communicable, non-communicable and environmental diseases. ● Health risk reduction and management. ● Universal health coverage.
- 9. NO AND COMMUNICABLE DISEASES The incidence of major infectious diseases: HIV, tuberculosis and malaria. Almost half the world’s population is at risk of malaria. 889,000 people died from infectious diseases caused largely by faecal contamination of water. 40 millions global death were due NCDs. 48% deaths were premature. 75% of premature deaths were caused by cardiovascular disease, cancer, diabetes and chronic respiratory disease. 80% of heart disease, stroke and diabetes can be prevented. Source: United Nations CDs NCDs
- 16. Hypertension and Diabetes Mellitus SPAIN NON COMMUNICABLE DISEASES major precursors of - Ischemic cardiovascular disease - Cerebrovascular events - End-stage renal disease - Death prevalence - Hypertension: 25 % - Diabetes: 7.17 ~20% of the population consumes 80%of the resources. Source: who
- 18. “I know that 50% of my advertising is wasted, I just don’t know which half.” WANAMAKER’S QUESTION Source: wikipedia
- 19. “Health care is the most difficult, chaotic, and complex industry to manage today. Peter Drucker - 2002”
- 21. THE POTENTIAL OF AI IN HEALTHCARE ● Expand capacity to generate new knowledge –the effectiveness of treatments. –the prediction of outcomes. ● Knowledge dissemination. ● Using AI(analytics) to combine EHR and genomic data to translate personalized medicine to clinical practice. ● Deliver information directly to patients and increase patient participation in their health care.
- 23. AGILE AI MANIFIESTO Source: agile data science 2.0 Iterate, iterate, iterate: tables, charts, reports, predictions - roadmap projects. 1 Integrate the tyrannical opinion of data in product management. 4 Ship intermediate output. Even failed experiments have output. 2 Prototype experiments over implementing tasks. 3
- 24. AGILE DATA SCIENCE MANIFIESTO Source: agile data science 2.0 Climb up and down the data-value pyramid as we work. 5 Discover and pursue the critical path to a killer product. 6 Get meta. Describe the process, not just the end-state. 7
- 26. CRISP-DM METHODOLOGY Source: Wikipedia Before define your framework (agile is a possibility), first define your culture and team. Cross Industry Standard Process for Data Mining
- 27. BUSINESS UNDERSTANDING It is one of the most important concepts of data science! It is vital to understand the problem to be solved and context. 1 Often recasting the problem and designing a solution is an iterative process of discovery. 2 The Business Understanding stage represents a part of the craft where the analysts’ creativity plays a large role. 3
- 28. BUSINESS UNDERSTANDING It is one of the most important concepts of data science! The key to a great success is a creative problem formulation how to cast the business problem as one or more data science problems (subproblems). 4 What is the expected value. 5 Team’s help is really important, we are not alone. 6
- 29. BUSINESS UNDERSTANDING - HEALTH ACTORS ● Clinicians, domain experts and financial analysts ● Managers, IT developers, consultants and vendors ● Policy makers ● Patients and consumers ● Executives and lines-of-business leaders ● Researches and academia ● Health institutions ● Society Build your strategy together!
- 31. DAMA - DMBOK GUIDE Fuente: The DAMA Guide to the Data Management Body of Knowledge" (DAMA-DMBOK Guide) Culture and empowerment Education
- 32. DATA UNDERSTANDING - HEALTH SOURCES FOR DATA IN HEALTHCARE Healthcare data Examples Images Radiographic, Images, MRIs, Ultrasounds and Nuclear imaging Un-/semi-structured Clinical narratives, Physician notes, Level 2,3 OMICS, Summaries, Pathology reports Streaming Bedside, remote monitors, Implants, fitness bands, smart watches and smart phones Social media Facebook, Twitter, Web forums and communities Structure data All claims, EHR, ERP and other information systems Dark data Server logs, application error logs, account information, emails and documents
- 33. DATA UNDERSTANDING - HEALTH Source: mckinsey and company
- 34. DATA MODELING The creation of models from data is known as model induction. Induction is a term from philosophy that refers to generalizing from specific cases to general rules (or laws, or truths). Source: Data science for business Generally speaking, a model is a simplified representation of reality created to serve a purpose. In ML (Artificial Intelligence), a predictive model is a formula for estimating the unknown value of interest: the target. The formula could be mathematical, or it could be a logical statement such as a rule. Often it is a hybrid of the two. Many Names for the Same Things!.
- 35. DATA MODELING - BEST PRACTICES Ask a specific question, Remember you are solving a business problem, not a math problem. 1 Start simple, start with the minimal set of data. 2 Try many algorithms but remember that data is more important than the exact algorithm, better your features. 3 Treat your data with suspicion, understand its idiosyncrasy. 4 Normalize your inputs 5
- 36. DATA MODELING - BEST PRACTICES ● Validate your model (test validation and clinical) ● Do the benchmark attempt, don’t be afraid to launch your product without AI. ● Operationalize their models. ● Set up a feedback loop. ● Healthcare doesn’t trust black boxes. ● Correlation is not causation. ● Monitor ongoing performance. ● Don’t be fooled by “accuracy”. ● Use medical support libraries eg: pubmed, cochrane, American Heart Association, Diabetes UK and so on.
- 38. DATA MODELING - TRADE OFF - NO FREE LUNCH Source: oreilly strata 2013
- 41. RISK MANAGEMENT - INFARCTION PREDICTING
- 43. DATA PREPARATION - TABULAR FORM - THE GOAL Primary care Secondary care Medication Other data… a lot of types ID age med height weight BMI diet 1 15 Y 168 60 21.3 Y 2 20 Y 185 80 23.4 Y 3 65 N 192 90 24.4 N 4 48 N 172 85 28.7 N 5 45 Y 185 79 23.1 N 6 79 N 182 71 21.4 Y 7 22 Y 186 79 22.8 Y Feature engineering Data points this is the key (N*M)! After a very expensive process To put data together is challenging Data engineering N features Mobservations TARGET
- 44. FEATURE ENGINEERING Dealing with outliers, binarization, categorical vars are managed by algorithm.1 Dealing with duplicates, Log transform, design thinking in terms of TIME (Generate Lag Features). 2 Creating key (id, date), creating lag and lead features. AUTOML3 “Coming up with features is difficult, time-consuming, requires expert knowledge. ‘Applied machine learning’ is basically feature engineering.” — Prof. Andrew Ng.
- 46. How is prediction and risk management going, 6 months after?
- 47. TRENDS AND CHALLENGES IN AI AND DIGITAL HEALTH
- 50. ● Data quality. ● Lack of access to data and tools. ● Insufficient training in data science methods. ● Interoperability. ● Operational clinical data may be: ❏ Inaccurate. ❏ Incomplete. ❏ Of unknown provenance. ❏ Of insufficient granularity. ● There are many idiosyncrasies about data. ● Business models (regulations, interdisciplinary, monetization, investment, and so on). CHALLENGES
- 52. SOME PEOPLE TO FOLLOW
- 53. THANKS!