Challenge 2 projects
June 6 - December 5 2017
Prediction challenge (the incidence of cancers)
Developing a predictive tool for the progression of cancer in time and space,depending on the known or supposed factors that determine its evolution.
The challenge consists in developing of a predictive tool for the incidence of cancers.The data used to learn the models are detailed below.
The Training set will be composed of the data covering the period from 1950 to 2003.The validation set will be composed of the data covering the period from 2003 to 2007.Note that the data from 2007 to 2012 in the process of being obtained and will be Integrated into the validation set.
This challenge is divided into two distinct parts: 1/ prediction in the world, 2/ prediction by country.
The registered projects:
||Time-series analysis and forecast models on cancer incidence|
| Aim: deliver a prediction model to confirm the correlations with colon cancer.
Associated skills: Data cleaning, Database Management Systems, Relationnal Database Management Systems, Mathematics, Statistic, Rules of Professional Responsibility and Ethical Obligation.
| Aim: provide a study on time-series analysis and forecast models on cancer incidence (Machine Learning approaches).|
Associated skills: Python, Java, C, C++, R, SQL, NOSQL, Machine learning, Deep learning, Elastic Search, Data preparation, Data cleaning, Data visualization, Mathematics.
|Cancer and local predictions||Baseline 2nd Edition|
| Aim: optimize the understanding and prediction of stand-alone cancers under certain circumstances (geographical e.g.).
Associated skills: Distributed Databases, Deep learning, Machine learning, Data visualization, Mathematics, Statistic, Biology, Oncology, Virology, Geriatrics, Treatment Services, Hierarchical Database Systems, Data cleaning.
| Aim: create an online platform hosting an interactive, robust, ready-to-use dataset. This platform should ideally be linked with a data science / machine learning environment to easily design and test statistical models.|
Associated skills: Data cleaning, Data preparation, Machine learning, Database Management Systems, Mathematics, Statistic, UI Design, Web design, Graphics, Oncology, SQL.
| Le CAT (Cancer Au Travail)
||Medical environment of cancer and impact on health state of the population|
| Objectif : montrer que les modèles sociaux et économiques favorables au travail après cancer impactent la durée de survie.
Compétences associées : Geographic Information Systems, Relationnal Database Management Systems, Distributed Databases, Data cleaning, Data visualization, Mathematics, Sociology, Statistic, Political science, Labour law.
| Aim: provide insight on the most efficient measures to reduce cancer impact on people in order to identify optimal levers to implement in a public health perspective.|
Associated skills: R, Data cleaning, Data preparation, Machine learning, Statistic, Anthropology, Economy, Communication/Information, Rules of Professional Responsibility and Ethical Obligations.
| Prediction of pediatric brain cancer incidence
||Immunology Based Predictive Model for : Lung Cancer Evolutions & Associated Treatments' Side Effects|
| Aim:predict child brain cancer incidence regarding the evolution of social and economic countries features.
Associated skills:Oncology, Pediatrics, Python, SQL, Machine learning, Data preparation, Mathematics, Biology, Statistic.
| Aim: build a predictive model to anticipate lung cancer evolution and severe side effects occurrence in order to react with more effective solutions for patients.|
Associated skills: Oncology Clinical Research, Molecular Oncology, Immunology, Biostatistics, Data visualization, Data Analysis, Patient experience, Statistic Biology.
| Cancer & Food
| Aim: find through data scietific methods the influence of food to cancer incidence based on consumption, production and cultural behaviors
Associated skills: Data Analysis, Data visualization.