Translation of "data-driven control " (English → French) :

data-driven control | data-driven execution | eager evaluation

bottom-up control structure | bottom-up inference | data-driven control structure | data-driven inference | event-driven control structure | event-driven inference

expectation-driven thinking | goal driven thinking | goal-driven control structure | top-down approach | top-down control structure | top-down thinking

goal-directed control [ goal-driven control ]

data-driven attack [ data driven attack ]

data-driven discovery [ data-driven approach ]

data-directed | data directed | data-driven

Definition: This block contains a wide variety of disorders that differ in severity and clinical form but that are all attributable to the use of one or more psychoactive substances, which may or may not have been medically prescribed. The third character of the code identifies the substance involved, and the fourth character specifies the clinical state. The codes should be used, as required, for each substance specified, but it should be noted that not all fourth character codes are applicable to all substances. Identification of the psychoactive substance should be based on as many sources of information as possible. These include self-report data, analysis of blood and other body fluids, characteristic physical and psychological symptom ...[+++]s, clinical signs and behaviour, and other evidence such as a drug being in the patient's possession or reports from informed third parties. Many drug users take more than one type of psychoactive substance. The main diagnosis should be classified, whenever possible, according to the substance or class of substances that has caused or contributed most to the presenting clinical syndrome. Other diagnoses should be coded when other psychoactive substances have been taken in intoxicating amounts (common fourth character .0) or to the extent of causing harm (common fourth character .1), dependence (common fourth character .2) or other disorders (common fourth character .3-.9). Only in cases in which patterns of psychoactive substance-taking are chaotic and indiscriminate, or in which the contributions of different psychoactive substances are inextricably mixed, should the diagnosis of disorders resulting from multiple drug use (F19.-) be used. | Modifiers The following fourth-character subdivisions are for use with categories F10-F19: Code Title .0 Acute intoxication A condition that follows the administration of a psychoactive substance resulting in disturbances in level of consciousness, cognition, perception, affect or behaviour, o ...

data-directed inference | data-driven inference

data directed reasoning | data driven reasoning

11. Considers that big data, cloud services, the Internet of Things, research and innovation are key to economic development and need a coherent approach throughout EU legislation; believes that compliance with data protection legislation and effective privacy safeguards and security safeguards as set in the General Data Protection Regulation, including special provisions regarding children as vulnerable consumers, are crucial for building trust for citizens and consumers in the data-driven economy sector; stresses the need to raise awareness of the role of data and the meaning of data-sharing for consumers, as regards their fundamenta ...[+++]l rights and within the economy, and to lay down rules on data ownership and citizens’ control over their personal data; underlines the role of personalisation of services and products that should be developed in compliance with data protection requirements; calls for the promotion of privacy by default and by design, which could also have positive impact on innovation and economic growth; stresses the need to ensure a non-discriminatory approach to all data processing; underlines the importance of a risk-based approach, which helps avoid any unnecessary administrative burden and provides legal certainty, as in the General Data Protection Regulation, especially for SMEs and start-ups, as well as democratic oversight and constant monitoring by public authorities; stresses that personal data need special protection and recognises that putting in place additional safeguards, such as pseudonymisation or anonymisation, can enhance protection where personal data are used by big data applications and online service providers;

107. Believes that compliance with data protection legislation and effective privacy and security safeguards as set in the General Data Protection Regulation, including special provisions regarding children as vulnerable consumers, are crucial for building the trust of citizens and consumers in the data-driven economy sector; stresses the need to raise awareness of the role of data and the meaning of data-sharing for consumers as regards their fundamental rights within the economy, and to lay down rules on data ownership and citizens’ control over their personal ...[+++] data; underlines the role of personalisation of services and products that should be developed in compliance with data protection requirements; calls for the promotion of privacy by default and by design, which could also have a positive impact on innovation and economic growth; stresses the need to ensure a non-discriminatory approach to all data processing; underlines the importance of a risk-based approach, which helps avoid any unnecessary administrative burden and provides legal certainty, especially for SMEs and start-ups, as well as democratic oversight and constant monitoring by public authorities; stresses that personal data need special protection and recognises that putting in place additional safeguards, such as pseudonymisation or anonymisation, can enhance protection where personal data are used by Big Data applications and online service providers;

[9] Businesses that use 'data-driven decision-making' enjoy a 5-6% increase in productivity, Big Data for All: Privacy and User Control in the Age of Analytics, O. Teme/J.