This document discusses using cohort and trajectory analysis in multi-agent support systems to provide personalized assistance to cancer survivors. It describes how combining several machine learning models can extract a patient's risk level and predict their likelihood of survival. For example, trajectory analysis of tumor stage 2 patients found an 80% chance of surviving over 5 years. Overall, the research aims to develop personalized chatbot and mobile app assistants that integrate a patient's electronic health records and behavior data with hybrid machine learning models to conduct advanced cohort and trajectory analysis for improved clinical decision support and quality of life support.

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. 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. 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. 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. G. Manzo A2HC 2021 5
Cohort and
Trajectory
Analysis
Decision-support for clinicians

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. Tumor stage 2 patients: 80% of chance to
survive more than 5 years

8. Hybrid machine learning models support
patient's cohort and trajectory analysis

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