Discussing Health IT, AI & Machine Learning Above Helsinki & Boston!


[P.S. Also consider joining us Thursday, July 18, above Cleveland, to discuss Healthcare, Marketing & AI: Synergies & Opportunities]

How can you be at two great conferences on two different continents at the same time? Through the magic of social virtual reality! Please join us above Helsinki AND (!!) Boston during HIMSS & Health 2.0 European and Machine Learning & AI for Healthcare to discuss health IT, AI and related themes.

About HIMSS & Health 2.0 European:

  1. INTEGRATING HEALTH & SOCIAL CARE Tackling social determinants of health Redesigning care pathways to include social care for improved clinical and financial outcomes Health starts with mental health Shift of responsibilities to nurses and social workers Aging graphic
  2. ENABLING THE SHIFT FROM ACUTE TO COMMUNITY-BASED CARE Homecare: healing and ageing in place Integrating and coordinating care to enable community-based and people-centred health Remote monitoring and telemedicine solutions and initiatives: IoT, pervasive technologies Data graphic
  3. PROMOTING A SECURE, ETHICAL AND ACTIONABLE FLOW OF DATA Protecting privacy and securing data Structuring data for decision-making Unleasing the power of data for prevention, population health management, personalized health and research AI graphic
  4. WHAT CAN AI DELIVER TODAY & WHAT ARE WE REALLY READY FOR? Ethical use of AI –we can’t verify the ethics of algorithms, but we can verify the ethics of data handling Beyond the buzz, real-world evidence and case studies on the use of AI in clinical setting Health 2.0 and startup solutions using AI Innovation graphic
  5. OPPORTUNITIES AND CHALLENGES OF OPENING INNOVATION Open innovation: Combining opening EMRs and cybersecurity Using large cohorts for population health management Using genomics and multi-nomics data to personalise health, care and wellness


About Machine Learning & AI for Healthcare:

Actionable steps and practical approaches to implementing analytics, AI and machine learning and integrating these technologies into their clinical workflow. (Workflow sighting!)

  • Data governance and quality
  • Developing an analytics team
  • Scaling analytics into clinical operations
  • How to maximize impact
  • Common misconceptions of AI in Healthcare
  • Integrating machine learning and AI into clinical workflows
  • Making an AI Investment Decision
  • Case studies on using machine learning to detect social determinants of health, address rural health disparities, and detect suicidal thinking

What is machine learning? We drew you another flowchart - MIT Technology Review

A Healthcare Example: Flip The Model On Advanced Data Analytics

A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

Data Science vs Machine Learning vs Data Analytics vs Business Analytics

"Machine learning (ML) is the ability of machines to predict outcomes without being explicitly programmed to do so"

Analytics in Healthcare: A Practical Introduction

Prediction Models: Traditional versus Machine Learning

AutoML — A Tool to Improve Your Workflow – Towards Data Science

No Stories Without Data, No Data Without Stories in Health Care Policy

The 10 Steps of Automated Machine Learning

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