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Manzo_a2hc_aamas

Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors

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Manzo_a2hc_aamas

  1. 1. Cohort and Trajectory Analysis in Multi-Agent Support Systems for Cancer Survivors Gaetano Manzo, Davide Calvaresi, Oscar Jimenez del Toro, Jean-Paul Calbimonte, and Michael Schumacher University of Applied Sciences Western Switzerland (HESSO) A2HC WORKSHOP – AAMAS 2021
  2. 2. 281,550 new cases of invasive breast cancer are estimated in the U.S. in 2021 G. Manzo A2HC 2021 2 all annual cancer cases Of patients are women Of cases are < 50 years old 50% Patients must cope with physical and psychological sequelae 12% 99%
  3. 3. assistive technologies for Biological Social Psychological Personalized support and assistance enhance patient’s quality of life G. Manzo A2HC 2021 3 HEALTH high-risk markers detection prognostic evaluation treatment adherence
  4. 4. EREBOTS is a Multi-Agent Personalized Chatbot System for e-Health applications A2HC 2021 For further details  https://doi.org/10.3390/electronics10060666 G. Manzo 4
  5. 5. G. Manzo A2HC 2021 5 Cohort and Trajectory Analysis Decision-support for clinicians
  6. 6. The CTA combines several model to extract patient’s risk G. Manzo A2HC 2021 6 Kaplan-Maier Cox Prop. Hazard (PH) Non-linear Cox PH Data Pre-processing Exploration Missing value Scaling 01 02 03 04 Survival Models Classification Decision Tree Random Forest Neural-Networks Cohort & Trajectory K-means Gaussian Mixture Hybrid
  7. 7. Tumor stage 2 patients: 80% of chance to survive more than 5 years
  8. 8. Hybrid machine learning models support patient's cohort and trajectory analysis
  9. 9. Conclusion and Future work G. Manzo A2HC 2021 9 Personalized Support for breast cancer patients EREBOTS, Cohort and trajectory analysis Hybrid models and risk markers detection Inject EHRs and Behavioural data Advanced CTA model selection HEMERA app

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