8,233 research outputs found
Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol
The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies
to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques
allow processing of real-time observational information and continuously learning from data to build
understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s
native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk.
Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the
relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the
Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes
(France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will
be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the
ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations.
Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of
security regarding patients’ data. Several important ethical considerations related to EMA method must also be
considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on
daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences
in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a
participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring.
Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks
factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by Fundación Jiménez Díaz Hospital, Instituto
de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan
Nacional de Drogas (20151073), American Foundation for Suicide Prevention
(AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740
AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the
European Union. MINECO/FEDER (‘ADVENTURE’, id. TEC2015–69868-C2–1-R)
and MCIU Explora Grant ‘aMBITION’ (id. TEC2017–92552-EXP), the French Embassy
in Madrid, Spain, The foundation de l’avenir, and the Fondation de
France. The work of D. Ramírez and A. Artés-Rodríguez has been partly supported
by Ministerio de Economía of Spain under projects: OTOSIS
(TEC2013–41718-R), AID (TEC2014–62194-EXP) and the COMONSENS Network
(TEC2015–69648-REDC), by the Ministerio de Economía of Spain jointly with
the European Commission (ERDF) under projects ADVENTURE (TEC2015–
69868-C2–1-R) and CAIMAN (TEC2017–86921-C2–2-R), and by the Comunidad
de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P.
Moreno-Muñoz has been supported by FPI grant BES-2016-07762
Learning to run a Power Network Challenge: a Retrospective Analysis
Power networks, responsible for transporting electricity across large
geographical regions, are complex infrastructures on which modern life
critically depend. Variations in demand and production profiles, with
increasing renewable energy integration, as well as the high voltage network
technology, constitute a real challenge for human operators when optimizing
electricity transportation while avoiding blackouts. Motivated to investigate
the potential of Artificial Intelligence methods in enabling adaptability in
power network operation, we have designed a L2RPN challenge to encourage the
development of reinforcement learning solutions to key problems present in the
next-generation power networks. The NeurIPS 2020 competition was well received
by the international community attracting over 300 participants worldwide. The
main contribution of this challenge is our proposed comprehensive Grid2Op
framework, and associated benchmark, which plays realistic sequential network
operations scenarios. The framework is open-sourced and easily re-usable to
define new environments with its companion GridAlive ecosystem. It relies on
existing non-linear physical simulators and let us create a series of
perturbations and challenges that are representative of two important problems:
a) the uncertainty resulting from the increased use of unpredictable renewable
energy sources, and b) the robustness required with contingent line
disconnections. In this paper, we provide details about the competition
highlights. We present the benchmark suite and analyse the winning solutions of
the challenge, observing one super-human performance demonstration by the best
agent. We propose our organizational insights for a successful competition and
conclude on open research avenues. We expect our work will foster research to
create more sustainable solutions for power network operations
Type determination in an optimizing compiler for APL
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When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming
AI powered code-recommendation systems, such as Copilot and CodeWhisperer,
provide code suggestions inside a programmer's environment (e.g., an IDE) with
the aim to improve their productivity. Since, in these scenarios, programmers
accept and reject suggestions, ideally, such a system should use this feedback
in furtherance of this goal. In this work we leverage prior data of programmers
interacting with Copilot to develop interventions that can save programmer
time. We propose a utility theory framework, which models this interaction with
programmers and decides when and which suggestions to display. Our framework
Conditional suggestion Display from Human Feedback (CDHF) is based on
predictive models of programmer actions. Using data from 535 programmers we
build models that predict the likelihood of suggestion acceptance. In a
retrospective evaluation on real-world programming tasks solved with
AI-assisted programming, we find that CDHF can achieve favorable tradeoffs. Our
findings show the promise of integrating human feedback to improve interaction
with large language models in scenarios such as programming and possibly
writing tasks.Comment: arXiv admin note: text overlap with arXiv:2210.1430
The Artificial Intelligence Workbench: a retrospective review
Last decade, biomedical and bioinformatics researchers have been demanding advanced and user-friendly applications for real use in practice. In this context, the Artificial Intelligence Workbench, an open-source Java desktop application framework for scientific software development, emerged with the goal of provid-ing support to both fundamental and applied research in the domain of transla-tional biomedicine and bioinformatics. AIBench automatically provides function-alities that are common to scientific applications, such as user parameter defini-tion, logging facilities, multi-threading execution, experiment repeatability, work-flow management, and fast user interface development, among others. Moreover, AIBench promotes a reusable component based architecture, which also allows assembling new applications by the reuse of libraries from existing projects or third-party software. Ten years have passed since the first release of AIBench, so it is time to look back and check if it has fulfilled the purposes for which it was conceived to and how it evolved over time
Multiobjective strategies for New Product Development in the pharmaceutical industry
New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems
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