Download - Applying Machine Learning Techniques to Breast Cancer Research - by Benjamin Habert - PAPIs Connect
Context:
Hacking Health Camp 2015 • Most innovative solution
• BPI award
Applying Machine Learning
Techniques to Breast Cancer
Research
Today
Under-exploited
medical data
Tomorrow
Data-driven
prognosis
Personal
medical
file
Medical Staff Data Scientists
We have
unstructured but
rich medical data
that is under-
exploited
We provide tools and
techniques to structure and
analyze data
Cancer Patients
We want to better
understand the prognosis
provided by physicians
Invasive Cancers
8%
tubular
cancer
Other
cancer
No
relapse
No
metastasis
Evolution of tubular cancer
100% 99,5%
0,5%
normal
cells
non-
invasive
cancer
invasive
cancer
Anonymization,
extraction and
aggregation
Natural Language
Processing
on medical files
Correlation:
risk factors vs.
cancer
progression
‘infiltrant','cli',’
lobulaire',
'cci','invasion','medullaire','tubuleux’,
'ia','femara','arimidex','aromatase',
’chimiotherapie','curage'
Next step:
contextualization of
numeric data