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data stories
Haute Autorité de Santé française
Development and deployment of an automatic document classification module based on NLP (Natural Language Processing) for the French National Authority for Health
Artelys develops a data mapping tool with the French Ministry of Home Affairs to strengthen its data governance.
Improving passenger information for bus users in the Paris area: Artelys demonstrates the contribution of artificial intelligence methods to produce more accurate and stable estimated bus arrival times at their stops.
The structuring of a territorial road database combined with Artificial Intelligence makes it possible to target actions to prevent road accidents in the Loiret department.
Artelys and the ANAP have been collaborating during 3 years to supply the healthcare sector with the most recent advancements in Data Science.
The MMT initiative (Man Machine Teaming) explores the possibilities of development for cognitive air combat systems.
Operating transport networks with the support of AI. This is the field explored by the online competition “Learn to Run a Power Network in a sustainable world“ launched by RTE with Artelys support.
A redesigned relationship between Man and Machine
MMT was launched and financed by the French General Direction of Armies (DGA). It is animated by the companies Dassault Aviation and Thales. Artelys is part of the ecosystem of partners, composed of French start-ups, SMEs and research centers.
As part of the Maestro project, Artelys is exploring the application of Monte-Carlo Tree Search algorithm and Reinforcement Learning to aerial trajectory optimization through Constraint Programming.
Official information: https://man-machine-teaming.com/
Data Science and AI to support crucial usecases in the healthcare sector
For 3 years, Artelys has been supporting the ANAP to implement techniques which solve concrete use cases. Those technics involve statistical data analysis, AI prototypes and have contributed to a strategy of internal acculturation to Data Science tools and Agile methodology.
Several use cases have been identified with the experts and have given rise to different services:
- Development and integration of a predictive module of the occupation rate of beds embedded in an existing tool for hospitals.
- Segmentation of the users of the web content of ANAP, according to their user profile. Statistics and clustering methods (K-Means) have been exploited to build efficient user personae.
- Analysis of the adequacy between healthcare professional demands and content offered by ANAP on its website. A list of clusters of interest have emerged thanks to a segmentation method (Hierarchical Ascendant Classification for biclustering) and led to recommendation concerning the structure of the offer.
- State of play of the real estate owned by the medico-social establishment in France. Creation and analysis of tailored indicators.
- Development of a configurable web tool using text mining to explore the thematic of Twitter posts relative to healthcare issues. Emergence of relationship between popular themes thanks to Natural Language Processing (NLP).
Gather the diverse available data streams to understand and act for road safety
The Loiret department is a member of the European project INTERREG NWE BE-GOOD and is in charge of the challenge “SAFER ROADS” whose goal is to bring characteristics by data use for road safety improvement. It means better understanding of the accident contexts and more efficient prevention actions.
One important stake is to get the data available and actionable by concerting the diverse actors of mobility sector who own it.
Artelys organized workshops in which several actors collaborated to provide data streams and specify their needs: public administration of the department (GIS, Road Observatory, Infrastructure administration), police forces (Gendarmerie Nationale), firemen (SDIS), insurance companies (Mondial Assistance, Thelem) and navigation services (Waze, Coyote).
A web platform has been developed by Artelys and made available to the public administration, stakeholders but also to citizens. It contains:
- A data warehouse with harmonized multi-source data streams and additional information on the context. This data are directly usable through requests or simple dashboard visualization.
- More than 24 000 accidents from the previous 10 years located on the road network and complemented by infrastructure and context information.
- 8 thematic dashboards to analyze influence of multiple variables on the accident risk (infrastructure, speed, traffic, seasonality, weather, accident situation, aggravating factors).
- A prediction algorithm which evaluates, on the road network, the risk level of accidents for a given future context. This algorithm is being developed and relies on supervised machine learning using decision trees.
- A clustering algorithm which has made some reference profiles of accidents emerged.
Operating transport networks with the support of artificial intelligence
In the competition ”Learn to Run a Power Network in a Sustainable World”, AI agents developed by the community have to face real-life TSO challenges.
They have to learn how to design the best set of actions on the grid in terms of robustness against realistic scenarios: consumption peaks, line overloads, intermittent renewable production, plants maintenance, but also cyber–attacks. Machine learning methods, reinforcement learning in particular, are designed to navigate in a vast multidimensional state space and converge to an optimal policy.
Artelys had several contributions at different levels of the competition, by developing four open-source Python packages.
- Grid2viz – An intuitive web application that offers interactive views into the results of Reinforcement Learning agents that ran on the competition environment (grid2op library). It has been developed with Dash.
- Chronix2Grid – A modular package that allows to generate synthetic but realistic consumption, production (solar, wind, hydraulic, nuclear and thermal), electricity loss (dissipation) and economic dispatched productions time-series for a given power grid. Parameters are tuned thanks to a set of indicators based on real-life time-series.
- ExpertOp4Grid – An “expert” agent dedicated to solve overloads in line grids using cheap but non-linear action with an expert approach inspired by recent research. For any new overloaded situations, it computes an influence graph around the overload of interest, and ranks the substations and topologies to explore, to find a solution.
- Oracle4Grid – An “oracle” agent that learns about the future by performing simulations and then finds the best course in order to maximize the cumulated reward. It provides indicators that provide benchmarks in agent performance.
See the competition on Codalab platform.
Data Science and Artificial Intelligence to improve the service provided to RATP passengers
Artelys has explored different approaches for the RATP to improve the short-term prediction of bus arrival at their stops. On the one hand, the optimization of the current model parameterization, and on the other hand, the exploration of innovative methods using state-of-the-art Machine Learning models.
Being able to offer the most reliable estimate of the next bus transit times is a major lever for the RATP in order to offer an optimal experience to its bus lines users. This forecast, which is by nature short term, is made difficult by many parameters. Some are structural, such as the number of intersections, the distance between stops, the day, time and/or week of travel, while others are contextual and need to be updated in real time, such as road traffic and weather conditions.
In addition to the current RATP system improvement by optimizing the existing algorithm, the use of predictive Machine Learning models has been studied. To this end, Artelys carried out:
- A review of the state-of-the-art methods applied to short-term bus arrival prediction
- The construction of data sets for model training: from RATP system logs and exogenous data such as rainfall and temperature
- An implementation of the different relevant AI models: linear regression, local regression (to include non-linear effects locally), Gradient Boosting, Random Forest, KNN etc.
At the end of this study, Artelys was able to identify that a mixture of a linear regression model with a Random Forest model brought a significant performance improvement, compared to the model currently used, both in terms of estimated bus arrival times accuracy and in terms of stability of the displays provided to users.
Artificial intelligence models are therefore a relevant lever to considerably improve the traveler information service provided to RATP users. The industrialization of the algorithms developed by Artelys must now be integrated into the development roadmap to make them available when the bus operators in the French capital are opened to competition.
A tool to centralize information
The French Ministry of Home Affairs wanted to strengthen the governance of its data by equipping itself with a solution for referencing the data sets available and their characteristics in order to enhance their value and, in particular, to accelerate data-related project initiatives.
Artelys is working with the French Ministry of Home Affairs to develop a web-based tool for mapping available data sets for use by all members of the Ministry. Based on Flask, React and Elasticsearch technologies, it enables the centralization of information knowledge that was previously dispersed among the various business departments.
Through this tool, users can explore (search, sort, filter) and then compare the various data sources referenced within the ministry. The mapping application offers a showcase of available data, facilitating the launch of new projects and the reuse of existing data already consolidated. In addition to these search functions, the tool integrates administration functions with access rights management by data perimeter. Each area has a manager, which makes it easier to monitor the relevance of information.
Evolutions on the current search engine are currently being developed. They will use Natural Language Processing methods. Indeed, these developments take into account the management of semantic fields and business nomenclatures. In addition, spelling and typing errors will be automatically corrected during searches.
The French Ministry of Home Affairs and Artelys have made the code open source, thus committing to the transparency of the tool and allowing its reuse by the other public administrations. It is available at the following address : https://github.com/dnum-mi/cartographie-donnees
Development and deployment of an automatic document classification module based on NLP (Natural Language Processing) on behalft of the French National Authority for Health
One of the pivotal roles of the French National Authority for Health (HAS) is to measure and enhance the quality of care and patient safety in healthcare establishments. A vital aspect of this responsibility is the national monitoring of Serious Adverse Events Associated with Healthcare (SAEH). Addressing these events is crucial, as their analysis can elucidate their origins and prevent recurrence. Artelys stepped in to support the data team at HAS, responsible for data valorization, to ensure the scalability of tools used for annual SAEH analysis reports.
The projected increase in the number of SAEH incidents in the coming years has prompted the HAS to revamp its current processing method to enhance automation. Artelys played a significant role in this transformation, automating and improving the different tools used by HAS. This ultimately paves the way for redirecting expert efforts towards more value-added tasks.
Initially, Artelys focused on reinforcing the various preprocessing stages required for SAEH analysis. Existing tools coded in SAS were rewritten in Python, business teams were trained for their adoption, and the entire system was deployed and automated. Subsequently, following a data annotation campaign, an NLP-based approach was explored to facilitate automatic SAEH categorization. Various models have been tested, including state-of-the-art “Transformer” models achieving comparable performance to the current system based on business rules and manual expert analysis.
Artelys further assisted HAS in moving towards greater automation of the SAEH processing. Multiple scenarios were suggested, redefining expert intervention process. In one of these, only the least reliable classifications from the NLP model are reviewed by experts. This “human in the loop“ strategy offers twofold benefits: it ensures the quality of model predictions while also enriching the annotated database. This approach enables HAS to continue refining the classification model.
Artelys’ contribution offered an innovative approach to SAEH classification, strengthened the existing operational system, and provided HAS with several usage scenarios. This ensures that, in the future, HAS can independently adjust expert intervention modalities in the SAEH processing.
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Artelys is strongly involved in R&D, exploring and experimenting big data techniques and tools to provide its customers state-of-art solutions.
intelligence
Research & Development
20% of our activity is dedicated to R&D
Artelys is strongly involved in R&D, exploring and experimenting big data techniques and tools to provide its customers state-of-art solutions.
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With more than 20 years of experience, Artelys puts its business, scientific and technical expertise at your service to help you exploit your data and solve your most complex problems using cutting-edge techniques in the field of artificial intelligence (Machine Learning, NLP, Computer Vision, etc).
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Artelys facilitates the integration of AI use cases in your business teams by offering the deployment of a complete SaaS application. From data ingestion and processing to the production of dynamic graphical interfaces and the development of customized AI capabilities, this solution makes it possible to add value to the data of your business team through AI.
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