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Peredicting heart failures using Multi-Party Computation

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The health crisis due to COVID-19 is shaping a new reality in which the exchange and access to health data in a secure way will be more and more necessary. In this complex challenge converge both the respect for the individual rights as well as the interests of the patients and the need to promote the research in pursuit of the public interest. To face this challenge, we can find different approaches across Europe. In this webinar, we will present the experiences of three EU-funded projects (BigMedilytics, BodyPass, and DeepHealth), besides an overview of the legal framework and recommendations to enforce both national regulations and GDPR by an expert in data privacy and security.

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Peredicting heart failures using Multi-Party Computation

  1. 1. Predicting heart failures using Multi-Party Computation 001010010100101 1010010010001001001 0100101001 01000110 001011111100010 0000101010010010001001001 001001001000010001 01001000101001 01001 01001010010 01010010010100100100010101001 010100010100010 01010 10010010010001010 01010010010010010010010 0100100100010001001001001 0100101001010010 0101001001 H2020 BigMedilytics Project with Erasmus MC, Achmea and TNO Pilot Topic Heart Failure Alex Sangers (alex.sangers@tno.nl)
  2. 2. BIGMEDILYTICS HEART FAILURE PILOT: DOES MY HEART FAILURE PATIENT REQUIRE ADDITIONAL CARE? 1. Heart failure patient arrives at the hospital 2. Doctor measures and requests data about this patient 3. Doctor uses the patients data and a prediction model to determine whether this patient requires additional health interventions However: • For accurate predictions, the prediction model is trained based on many patient data of multiple organizations • And getting consent from the large group of patients is time consuming.
  3. 3. COMBINING PATIENT INFORMATION OF MULTIPLE ORGANIZATIONS RESULTS IN BETTER PREDICTIONS But there are reasons not to share data • Privacy concerns / bad publicity • Laws and regulations (e.g. GDPR) • Business sensitive information • Revealing suboptimal business processes • … Smoking behaviour Claims data w.r.t. heart failure Alcohol behaviour Claim data w.r.t. other diseases Exercising … … … Table: available data at different organizations for (partially) the same persons
  4. 4. Goal: Identify heart failure patients with a high risk of early hospitalization or mortality, so they can receive additional health care interventions How: Develop accurate hospitalization prediction model based on combination of insurance data and hospital data Problem: Sensitive data that cannot be shared! DATA SHARING CHALLENGE: HOW TO DEVELOP AN ACCURATE PREDICTION MODEL? ?Hospital data Insurance data
  5. 5. BREAKING THE PARADOX: WE WANT A MODEL, NOT DATA Privacy & Confidentiality Information sharing & Cooperation Exercise (hours/week) [Erasmus MC] = input data on heart failure patients (sensitive!) = resulting prediction model (not sensitive)
  6. 6. MULTI-PARTY COMPUTATION (MPC): COOPERATIVELY ANALYZING DATA, WITHOUT SHARING DATA MPC uses cryptographic protocols to: • Cooperatively perform a computation • Without sharing sensitive data • And receive only the outcome of the computation 𝑥4 𝑥2𝑥1 𝑥3 𝑓(𝑥1, 𝑥2, 𝑥3, 𝑥4)
  7. 7. Based on advanced cryptography No one-size-fits-all solution: • Toolbox with different cryptografic techniques Trade-off between: • Functionality • Computation time • Communication / bandwidth • Security Last years: improved practical applicability, including software libraries MULTI-PARTY COMPUTATION IS CHALLENGING
  8. 8. A PREDICTION MODEL CAN BE TRAINED BY APPLYING DATABASES OF MULTIPLE ORGANIZATIONS THAT ARE COUPLED VIA MULTI-PARTY COMPUTATION Laptop/ server/VM Encrypted data Encrypted data Laptop/ server/VM MPC MPC MPC Laptop/ server/VM Developed by:
  9. 9. Big Impact all together with

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