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