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Clampitt, Megan. "Indexation de l'état de santé des coraux par une approche basée sur l'intelligence artificielle". Electronic Thesis or Diss., Université Côte d'Azur, 2023. http://www.theses.fr/2023COAZ6019.
Pełny tekst źródłaCoral reefs are deteriorating at a startling rate and the development of fast and efficient monitoring schemas that attempt to evaluate coral health without only focusing on the absence or presence of disease or bleaching is essential. My Ph.D. research aims to combine the fields of Coral Biology, Computer Science, and Marine Conservation with the main question of my thesis being: how can artificial intelligence tools be used to assess coral health states from colony photographs? Since the assessment of individual coral colony health state remains poorly defined, our approach is to use AI tools to assess visual cues such as physically damaging conditions (boring organisms & predation), contact with other organisms (algae, sediment), and color changes that could correlate with health states. This was achieved by utilizing photographic data from the Tara Pacific Expedition to build the first version of AI machines capable of automatically recognizing these visual cues and then applying this tool to two types of field studies i). A longitudinal study set up in Moorea, French Polynesia aimed to investigate coral health as assessed by mortality/partial mortality events. ii). A comparative study between damaged, pristine, and restoration sites in Raja Ampat, Indonesia. The objective of these studies is to extract the visual cues that distinguish healthy from unhealthy corals. Thus, I was able to create an AI Model capable of automatically annotating coral colony photographs for visual cues relevant to the current health state of the colony
Ouédraogo, Ismaila. "Technologie mobile et intelligence artificielle pour l'amélioration de la littératie en santé dans les milieux défavorisés". Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0023.
Pełny tekst źródłaAccess and use of health information is indeed a major challenge in sub-Saharan Africa, especially for populations with low literacy. These difficulties are exacerbated by the increasing prevalence of foreign language content in digital health solutions, as well as the sometimes inadequate design of these solutions for local populations. These factors must be taken into account in the development and implementation of digital health solutions to ensure that they are truly accessible and beneficial to all populations. In this context, this research focuses on improving the accessibility and use of health information (health literacy) among lowliterate populations in Burkina Faso through AI-enabled mobile health solutions. The research methodology includes literature reviews, interviews, surveys and observations to accurately understand the specific needs of low literacy users. Based on this feedback, concrete design principles will be established to guide the development of a prototype Interactive Voice Response (IVR) system in the Dioula language. The mobile service is then evaluated with users to enable iterative improvements to the solution, taking user feedback into account. In addition, this research contributes to the creation of annotated speech data in Dioula to address the lack of speech data for assistive speech technologies for the population. By highlighting the importance of local languages and adapted technologies, this study demonstrates how AI-enabled mobile health solutions can effectively overcome barriers related to literacy to improve the health literacy of marginalised populations. The findings of this study are in line with the United Nations Sustainable Development Goals (SDGs), thus reinforcing their positive impact on the health of vulnerable populations in Burkina Faso
Mercadier, Yves. "Classification automatique de textes par réseaux de neurones profonds : application au domaine de la santé". Thesis, Montpellier, 2020. http://www.theses.fr/2020MONTS068.
Pełny tekst źródłaThis Ph.D focuses on the analysis of textual data in the health domain and in particular on the supervised multi-class classification of data from biomedical literature and social media.One of the major difficulties when exploring such data by supervised learning methods is to have a sufficient number of data sets for models training. Indeed, it is generally necessary to label manually the data before performing the learning step. The large size of the data sets makes this labellisation task very expensive, which should be reduced with semi-automatic systems.In this context, active learning, in which the Oracle intervenes to choose the best examples to label, is promising. The intuition is as follows: by choosing the smartly the examples and not randomly, the models should improve with less effort for the oracle and therefore at lower cost (i.e. with less annotated examples). In this PhD, we will evaluate different active learning approaches combined with recent deep learning models.In addition, when small annotated data set is available, one possibility of improvement is to artificially increase the data quantity during the training phase, by automatically creating new data from existing data. More precisely, we inject knowledge by taking into account the invariant properties of the data with respect to certain transformations. The augmented data can thus cover an unexplored input space, avoid overfitting and improve the generalization of the model. In this Ph.D, we will propose and evaluate a new approach for textual data augmentation.These two contributions will be evaluated on different textual datasets in the medical domain
Yameogo, Relwende Aristide. "Risques et perspectives du big data et de l'intelligence artificielle : approche éthique et épistémologique". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMLH10.
Pełny tekst źródłaIn the 21st century, the use of big data and AI in the field of health has gradually expanded, although it is accompanied by problems linked to the emergence of practices based on the use of digital traces. The aim of this thesis is to evaluate the use of big data and AI in medical practice, to discover the processes generated by digital tools in the field of health and to highlight the ethical problems they pose.The use of ICTs in medical practice is mainly based on the use of EHR, prescription software and connected objects. These uses raise many problems for physicians who are aware of the risk involved in protecting patients' health data. In this work, we are implementing a method for designing CDSS, the temporal fuzzy vector space. This method allows us to model a new clinical diagnostic score for pulmonary embolism. Through the "Human-trace" paradigm, our research allows us not only to measure the limitation in the use of ICT, but also to highlight the interpretative biases due to the delinking between the individual caught in his complexity as a "Human-trace" and the data circulating about him via digital traces. If big data, coupled with AI can play a major role in the implementation of CDSS, it cannot be limited to this field. We are also studying how to set up big data and AI development processes that respect the deontological and medical ethics rules associated with the appropriation of ICTs by the actors of the health system
La, Barbera Giammarco. "Learning anatomical digital twins in pediatric 3D imaging for renal cancer surgery". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT040.
Pełny tekst źródłaPediatric renal cancers account for 9% of pediatric cancers with a 9/10 survival rate at the expense of the loss of a kidney. Nephron-sparing surgery (NSS, partial removal of the kidney) is possible if the cancer meets specific criteria (regarding volume, location and extent of the lesion). Indication for NSS is relying on preoperative imaging, in particular X-ray Computerized Tomography (CT). While assessing all criteria in 2D images is not always easy nor even feasible, 3D patient-specific models offer a promising solution. Building 3D models of the renal tumor anatomy based on segmentation is widely developed in adults but not in children. There is a need of dedicated image processing methods for pediatric patients due to the specificities of the images with respect to adults and to heterogeneity in pose and size of the structures (subjects going from few days of age to 16 years). Moreover, in CT images, injection of contrast agent (contrast-enhanced CT, ceCT) is often used to facilitate the identification of the interface between different tissues and structures but this might lead to heterogeneity in contrast and brightness of some anatomical structures, even among patients of the same medical database (i.e., same acquisition procedure). This can complicate the following analyses, such as segmentation. The first objective of this thesis is to perform organ/tumor segmentation from abdominal-visceral ceCT images. An individual 3D patient model is then derived. Transfer learning approaches (from adult data to children images) are proposed to improve state-of-the-art performances. The first question we want to answer is if such methods are feasible, despite the obvious structural difference between the datasets, thanks to geometric domain adaptation. A second question is if the standard techniques of data augmentation can be replaced by data homogenization techniques using Spatial Transformer Networks (STN), improving training time, memory requirement and performances. In order to deal with variability in contrast medium diffusion, a second objective is to perform a cross-domain CT image translation from ceCT to contrast-free CT (CT) and vice-versa, using Cycle Generative Adversarial Network (CycleGAN). In fact, the combined use of ceCT and CT images can improve the segmentation performances on certain anatomical structures in ceCT, but at the cost of a double radiation exposure. To limit the radiation dose, generative models could be used to synthesize one modality, instead of acquiring it. We present an extension of CycleGAN to generate such images, from unpaired databases. Anatomical constraints are introduced by automatically selecting the region of interest and by using the score of a Self-Supervised Body Regressor, improving the selection of anatomically-paired images between the two domains (CT and ceCT) and enforcing anatomical consistency. A third objective of this work is to complete the 3D model of patient affected by renal tumor including also arteries, veins and collecting system (i.e. ureters). An extensive study and benchmarking of the literature on anatomic tubular structure segmentation is presented. Modifications to state-of-the-art methods for our specific application are also proposed. Moreover, we present for the first time the use of the so-called vesselness function as loss function for training a segmentation network. We demonstrate that combining eigenvalue information with structural and voxel-wise information of other loss functions results in an improvement in performance. Eventually, a tool developed for using the proposed methods in a real clinical setting is shown as well as a clinical study to further evaluate the benefits of using 3D models in pre-operative planning. The intent of this research is to demonstrate through a retrospective evaluation of experts how criteria for NSS are more likely to be found in 3D compared to 2D images. This study is still ongoing
Hadidi, Tareq. "Modélisation et simulation des déplacements de la vie quotidienne dans un habitat intelligent pour la santé". Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS005.
Pełny tekst źródłaOur societies will have to meet rapidly the needs of 14 million people who often live in situations of loss of autonomy or dependence with quick solutions supported. Telemedicine, and especially the home telemonitoring, is now a solution to alleviate the shortage of health professionals confronted to the great increase in population in Europe. In this context, we investigated the HIS "Smart Habitat for Health". The work of this thesis was to develop a digital simulator of activities (displacements) of a person followed within a HIS. The creation of a simulator is seen as a solution to improve performance and quality of service of home telemonitoring. We tested several methods of simulation (artificial neural networks, Markov chains, Polya urns) and retained the hidden Markov (HMM). This simulator was implemented under MATLAB, after the modeling of data collected in HIS occupied by elderly people, some living alone. Validation of data generated by the simulator was performed by measuring surface correlation between real and simulated data. This work paves the way for production activity data simulated according to a profile type of patient, without going through lengthy and costly field experiments
Curé, Olivier. "Siam : système intéractif d'automédication multimédia". Paris 5, 1999. http://www.theses.fr/1999PA05S019.
Pełny tekst źródłaDi, Marco Lionel. "Récit d'ingénierie pédagogique en santé à l'usage de l'enseignant connecté Does the acceptance of hybrid learning affect learning approaches in France? Blended Learning for French Health Students: Does Acceptance of a Learning Management System Influence Students’ Self-Efficacy?" Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALS024.
Pełny tekst źródłaBackground. The general objective of this thesis was to evaluate a hybrid pedagogical method using an integrated learning environment (ILE) in the training of health professionals. Three research questions followed one after the other. Does the acceptability of blended learning affect students' learning strategies and learning approaches? Does the acceptability of an ILE affect students' self-efficacy? What characteristics of a dematerialised course make students' attention variable?Materials & Methods. We carried out 2 quantitative observational studies, as well as a single-blind observational experiment coupled with a qualitative analysis, with different classes of midwifery students of Grenoble-Alpes University Faculty of Medicine.Results. Students have suited learning approaches and strategies despite the use of a hybrid teaching method which they reject; there is no correlation between poor acceptability of the ILE and different spheres of students' self-efficacy; and the variability of attention declared by students varies according to certain factors common to those detected by artificial intelligence (type of language, slide duration…).Discussion. The internal and external validities of this work highlight the close links between interest, motivation, engagement by identification, and attention. It is thus possible to put forward principles of pedagogical engineering in health within the framework of dematerialized courses focusing on the content, form and type of knowledge capsule. Finally, the health teacher must above all be “connected to” the students, so that technical developments can be adapted to their needs
Bassement, Jennifer. "Identification of fall-risk factors degradation using quality of balance measurements". Thesis, Troyes, 2014. http://www.theses.fr/2014TROY0035/document.
Pełny tekst źródłaFalls concern a third of the people aged over 65y and lead to the loss of functional ability. The detection of risks factors of falls is essential for early intervention. Six intrinsic risk factors of fall: vision, vestibular system, joint range of motion, leg muscle strength, joint proprioception and foot cutaneous proprioception were assessed with clinical tests before and after temporarily degradation. Standing balance was recorded on a force plate.From the force plate, 198 parameters of the centre of pressure displacement were computed. The parameters were used as variables to build neural network and logistic regression model for discriminating conditions. Feature selection analysis was performed to reduce the number of variables.Several models were built including 3 to 10 conditions. Models with 5 or less conditions appeared acceptable but better performance was found with models including 3 conditions. The best accuracy was 92% for a model including ankle range of motion, fatigue and vision contrast conditions. Qualities of balance parameters were able to diagnose impairments. However, the efficient models included only a few conditions. Models with more conditions could be built but would require a larger number of cases to reach high accuracy. The study showed that a neural network or a logistic model could be used for the diagnosis of balance impairments. Such a tool could seriously improve the prevention and rehabilitation practice
Guo, Jing. "Serious Games pour la e-Santé : application à la formation des médecins généralistes". Phd thesis, Toulouse 3, 2016. http://oatao.univ-toulouse.fr/17813/1/the%CC%80se_GUO.pdf.
Pełny tekst źródłaDujols, Pierre. "Analyse des énoncés médicaux en langage naturel : vers un prototype d'indexation automatique". Montpellier 2, 1990. http://www.theses.fr/1990MON20008.
Pełny tekst źródłaVazquez, Rodriguez Juan Fernando. "Transformateurs multimodaux pour la reconnaissance des émotions". Electronic Thesis or Diss., Université Grenoble Alpes, 2023. http://www.theses.fr/2023GRALM057.
Pełny tekst źródłaMental health and emotional well-being have significant influence on physical health, and are especially important for healthy aging. Continued progress on sensors and microelectronics has provided a number of new technologies that can be deployed in homes and used to monitor health and well-being. These can be combined with recent advances in machine learning to provide services that enhance the physical and emotional well-being of individuals to promote healthy aging. In this context, an automatic emotion recognition system can provide a tool to help assure the emotional well-being of frail people. Therefore, it is desirable to develop a technology that can draw information about human emotions from multiple sensor modalities and can be trained without the need for large labeled training datasets.This thesis addresses the problem of emotion recognition using the different types of signals that a smart environment may provide, such as visual, audio, and physiological signals. To do this, we develop different models based on the Transformer architecture, which has useful characteristics such as their capacity to model long-range dependencies, as well as their capability to discern the relevant parts of the input. We first propose a model to recognize emotions from individual physiological signals. We propose a self-supervised pre-training technique that uses unlabeled physiological signals, showing that that pre-training technique helps the model to perform better. This approach is then extended to take advantage of the complementarity of information that may exist in different physiological signals. For this, we develop a model that combines different physiological signals and also uses self-supervised pre-training to improve its performance. We propose a method for pre-training that does not require a dataset with the complete set of target signals, but can rather, be trained on individual datasets from each target signal.To further take advantage of the different modalities that a smart environment may provide, we also propose a model that uses as inputs multimodal signals such as video, audio, and physiological signals. Since these signals are of a different nature, they cover different ways in which emotions are expressed, thus they should provide complementary information concerning emotions, and therefore it is appealing to use them together. However, in real-world scenarios, there might be cases where a modality is missing. Our model is flexible enough to continue working when a modality is missing, albeit with a reduction in its performance. To address this problem, we propose a training strategy that reduces the drop in performance when a modality is missing.The methods developed in this thesis are evaluated using several datasets, obtaining results that demonstrate the effectiveness of our approach to pre-train Transformers to recognize emotions from physiological signals. The results also show the efficacy of our Transformer-based solution to aggregate multimodal information, and to accommodate missing modalities. These results demonstrate the feasibility of the proposed approaches to recognizing emotions from multiple environmental sensors. This opens new avenues for deeper exploration of using Transformer-based approaches to process information from environmental sensors and allows the development of emotion recognition technologies robust to missing modalities. The results of this work can contribute to better care for the mental health of frail people
Ajmi, Faiza. "Optimisation collaborative par des agents auto-adaptatifs pour résoudre les problèmes d'ordonnancement des patients en inter-intra urgences hospitalières". Thesis, Centrale Lille Institut, 2021. http://www.theses.fr/2021CLIL0019.
Pełny tekst źródłaThis thesis addresses the scheduling patients in emergency department (ED) considering downstreamconstraints, by using collaborative optimization approaches to optimize the total waiting time of patients.These approaches are used by integrating, in the behavior of each agent, a metaheuristic that evolvesefficiently, thanks to two interaction protocols "friends" and "enemies". In addition, each agent self-adaptsusing a reinforcement learning algorithm adapted to the studied problem. This self-adaptation considersthe agents’ experiences and their knowledge of the ED environment. The learning of the agents allowsto accelerate the convergence by guiding the search for good solutions towards more promising areas inthe search space. In order to ensure the continuity of quality patient care, we also propose in this thesis,a joint approach for scheduling and assigning downstream beds to patients. We illustrate the proposedcollaborative approaches and demonstrate their effectiveness on real data provided from the ED of the LilleUniversity Hospital Center obtained in the framework of the ANR OIILH project. The results obtainedshow that the collaborative Learning approach leads to better results compared to the scenario in whichagents work individually or without learning. The application of the algorithms that manage the patientscare in downstream services, provides results in the form of a dashboard, containing static and dynamicinformation. This information is updated in real time and allows emergency staff to assign patients morequickly to the adequate structures. The results of the simulation show that the proposed AI algorithms cansignificantly improve the efficiency of the emergency chain by reducing the total waiting time of patientsin inter-intra-emergency
Wang, Kun. "Algorithmes et méthodes pour le diagnostic ex-situ et in-situ de systèmes piles à combustible haute température de type oxyde solide". Phd thesis, Université de Franche-Comté, 2012. http://tel.archives-ouvertes.fr/tel-01017170.
Pełny tekst źródłaAhmed, Benyahia Amine. "Etude d’une méthodologie pour la construction d’un système de télésurveillance médicale : application à une plateforme dédiée au maintien et au suivi à domicile de personnes atteintes d’insuffisance cardiaque". Thesis, Belfort-Montbéliard, 2015. http://www.theses.fr/2015BELF0258/document.
Pełny tekst źródłaThe thesis, conducted as part of the E-care project, proposes a methodological process to facilitate the analysis and design of medical remote monitoring systems for early detection of signs of any complications. The proposed methodology is based on a multi-agent system using several types of ontologies associated with an expert system. The multi-agent system is suitable for medical monitoring with a distributed architecture to keep some autonomy and responsiveness of habitats. The process identifies the generic and specific aspects of each system. The designed architectures take into account all the patient data such as: patient profile, medical history, drug treatments, physiological and behavioral data, as well as data relating to patient's environment and his lifestyle. These architectures should be open to be adapted to new data sources.This methodology was applied to E-care project in order to define its information system. This information system is composed of two types of ontologies (problem ontology and several domain ontologies) and an expert system for the detection of risk situations. The problem ontology was built to manage the system including users and their tasks. Three domain ontologies have been built to represent, disease, drugs and cardiovascular risk factors. The expert system uses inference rules, which are defined in collaboration with medical experts using their knowledge and some medical guidelines. This methodology also defined the system architecture, which consists of four autonomous agents types namely: medical sensors to collect physiological measurements. The gateway collects data from sensors and transmits them from the patients' homes to the server. The server processes data and gives access to them. Finally the database secures storage of patient data.As part of the E-care project, an experiment was conducted to validate the various system components. This experiment has gone through two phases, the first was held at the University Hospital of Strasbourg, and the second is in the patients' homes
Toofanee, Mohammud Shaad Ally. "An innovative ecosystem based on deep learning : Contributions for the prevention and prediction of diabetes complications". Electronic Thesis or Diss., Limoges, 2023. https://aurore.unilim.fr/theses/nxfile/default/656b0a1f-2ff2-49c5-bb3e-f34704d6f6b0/blobholder:0/2023LIMO0107.pdf.
Pełny tekst źródłaIn the year 2021, estimations indicated that approximately 537 million individuals were affected by diabetes, a number anticipated to escalate to 643 million by the year 2030 and further to 783 million by 2045. Diabetes, characterized as a persistent metabolic ailment, necessitates unceasing daily care and management. In the context of Mauritius, as per the most recent report by the International Diabetes Federation, the prevalence of diabetes, specifically Type 2 Diabetes (T2D), stood at 22.6% of the population in 2021, with projections indicating a surge to 26.6% by the year 2045. Amidst this alarming trend, a concurrent advancement has been observed in the realm of technology, with artificial intelligence techniques showcasing promising capabilities in the spheres of medicine and healthcare. This doctoral dissertation embarks on the exploration of the intersection between artificial intelligence and diabetes education, prevention, and management.We initially focused on exploring the potential of artificial intelligence (AI), more specifically, deep learning, to address a critical complication linked to diabetes – Diabetic Foot Ulcer (DFU). The emergence of DFU poses the grave risk of lower limb amputations, consequently leading to severe socio-economic repercussions. In response, we put forth an innovative solution named DFU-HELPER. This tool serves as a preliminary measure for validating the treatment protocols administered by healthcare professionals to individual patients afflicted by DFU. The initial assessment of the proposed tool has exhibited promising performance characteristics, although further refinement and rigorous testing are imperative. Collaborative efforts with public health experts will be pivotal in evaluating the practical efficacy of the tool in real-world scenarios. This approach seeks to bridge the gap between AI technologies and clinical interventions, with the ultimate goal of improving the management of patients with DFU.Our research also addressed the critical aspects of privacy and confidentiality inherent in handling health-related data. Acknowledging the extreme importance of safeguarding sensitive information, we delved into the realm of Peer-to-Peer Federated Learning. This investigation specifically found application in our proposal for the DFU-Helper tool discussed earlier. By exploring this advanced approach, we aimed to ensure that the implementation of our technology aligns with privacy standards, thereby fostering a trustworthy and secure environment for healthcare data management.Finally, our research extended to the development of an intelligent conversational agent designed to offer round-the-clock support for individuals seeking information about diabetes. In pursuit of this goal, the creation of an appropriate dataset was paramount. In this context, we leveraged Natural Language Processing techniques to curate data from online media sources focusing on diabetes-related content
Lin-Kwong-Chon, Christophe. "Approches neuronales adaptatives pour le contrôle tolérant aux défauts de systèmes pile à combustible". Thesis, La Réunion, 2020. http://www.theses.fr/2020LARE0008.
Pełny tekst źródłaThe proton exchange membrane fuel cell is a promising electrochemical converter for production of electricity from the decarbonated hydrogen carrier. However, some technological challenges limit its deployment, such as durability, reliability or financial cost. The active fault-tolerant control strategy is one of the solutions to mitigate any system fault according to three actions: diagnosis, decision and control. This study proposes to develop a generic controller module adaptive to health states through neural networks. Dynamic programming controller, reinforcement learning, and echo-state models are combined for the design of the adaptive controller. This controller employs three neural models with specific roles: an actor, a predictor and a critic. Flooding and membrane drying faults are considered in this study. The proposed controller was able to demonstrate interesting capabilities on a simulation fuel cell model in multi-variable regulation for oxygen stoichiometry, membrane pressure difference and temperature. The results show superior performance of the proposed controller compared to a proportional integral derivative controller. Stability analyses were conducted to prove the continuity of the adaptive controller. The controller has been validated experimentally on a single cell test-bench. The configuration of the test-bench imposed constraints specific to an on-line and real-time application. The generic nature of the controller offers the possibility to switch from one configuration to another without having to design another controller. Several tests are carried out for regulation of the zero-pressure difference at the membrane. The controller was validated on the occurrence of flooding and membrane dryness faults, including actuator and water purging disturbances. The approach and the generic controller adaptive to the states of health proposed in this thesis allow to satisfy control requirements regarding the fault-tolerant control strategy. The first interest lies in the compensation of the multilateral effects of faults that lead to unwanted dynamic changes. Another interest is to be able to modify in situ operating conditions, components or even auxiliaries while being able to ensure a stable and optimal control
Voarino, Nathalie. "Systèmes d’intelligence artificielle et santé : les enjeux d’une innovation responsable". Thèse, 2019. http://hdl.handle.net/1866/23526.
Pełny tekst źródłaThe use of artificial intelligence (AI) systems in health is part of the advent of a new "high definition" medicine that is predictive, preventive and personalized, benefiting from the unprecedented amount of data that is today available. At the heart of digital health innovation, the development of AI systems promises to lead to an interconnected and self-learning healthcare system. AI systems could thus help to redefine the classification of diseases, generate new medical knowledge, or predict the health trajectories of individuals for prevention purposes. Today, various applications in healthcare are being considered, ranging from assistance to medical decision-making through expert systems to precision medicine (e.g. pharmacological targeting), as well as individualized prevention through health trajectories developed on the basis of biological markers. However, urgent ethical concerns emerge with the increasing use of algorithms to analyze a growing number of data related to health (often personal and sensitive) as well as the reduction of human intervention in many automated processes. From the limitations of big data analysis, the need for data sharing and the algorithmic decision ‘opacity’ stems various ethical concerns relating to the protection of privacy and intimacy, free and informed consent, social justice, dehumanization of care and patients, and/or security. To address these challenges, many initiatives have focused on defining and applying principles for an ethical governance of AI. However, the operationalization of these principles faces various difficulties inherent to applied ethics, which originate either from the scope (universal or plural) of these principles or the way these principles are put into practice (inductive or deductive methods). These issues can be addressed with context-specific or bottom-up approaches of applied ethics. However, people who embrace these approaches still face several challenges. From an analysis of citizens' fears and expectations emerging from the discussions that took place during the coconstruction of the Montreal Declaration for a Responsible Development of AI, it is possible to get a sense of what these difficulties look like. From this analysis, three main challenges emerge: the incapacitation of health professionals and patients, the many hands problem, and artificial agency. These challenges call for AI systems that empower people and that allow to maintain human agency, in order to foster the development of (pragmatic) shared responsibility among the various stakeholders involved in the development of healthcare AI systems. Meeting these challenges is essential in order to adapt existing governance mechanisms and enable the development of a responsible digital innovation in healthcare and research that allows human beings to remain at the center of its development.
Langlois, Alexis. "Classification automatique de textes pour les revues de littérature mixtes en santé". Thèse, 2016. http://hdl.handle.net/1866/19109.
Pełny tekst źródłaThe interest of health researchers and policy-makers in literature reviews has continued to increase over the years. Mixed studies reviews are highly valued since they combine results from the best available studies on various topics while considering quantitative, qualitative and mixed research methods. These reviews can be used for several purposes such as justifying, designing and interpreting results of primary studies. Due to the proliferation of published papers and the growing number of nonempirical works such as editorials and opinion letters, screening records for mixed studies reviews is time consuming. Traditionally, reviewers are required to manually identify potential relevant studies. In order to facilitate this process, a comparison of different automated text classification methods was conducted in order to determine the most effective and robust approach to facilitate systematic mixed studies reviews. The group of algorithms considered in this study combined decision trees, naive Bayes classifiers, k-nearest neighbours, support vector machines and voting approaches. Statistical techniques were applied to assess the relevancy of multiple features according to a predefined dataset. The benefits of feature combination for numerical terms, synonyms and mathematical symbols were also measured. Furthermore, concepts extracted from a metathesaurus were used as additional features in order to improve the training process. Using the titles and abstracts of approximately 10,000 entries, decision trees perform the best with an accuracy of 88.76%, followed by support vector machine (86.94%). The final model based on decision trees relies on linear interpolation and a group of concepts extracted from a metathesaurus. This approach outperforms the mixed filters commonly used with bibliographic databases like MEDLINE. However, references chosen for training must be selected judiciously in order to address the model instability and the disparity of quantitative and qualitative study designs.
Phan, Philippe. "The use of artificial intelligence algorithms to guide surgical treatment of adolescent idiopathic scoliosis". Thèse, 2015. http://hdl.handle.net/1866/11764.
Pełny tekst źródłaAdolescent idiopathic scoliosis (AIS) is a three-dimensional deformity of the spine. Management of AIS includes conservative treatment with observation, the use of braces to limit its progression or surgery to correct the deformity and cease its progression. Surgical treatment of AIS remains controversial with respect to not only indications but also surgical strategy. Despite the existence of classifications to guide AIS treatment, intra- and inter-observer variability in surgical strategy has been described in the literature. Technological advances and their integration into the medical field have led to the use of artificial intelligence (AI) algorithms to assist with AIS classification and three-dimensional evaluation. With the evolution of surgical techniques and instrumentation, it is probable that the intra- and inter-observer variability could increase. However, some AI algorithms have shown the potential to lower variability in classification and guide treatment. The overall objective of this thesis was to develop software using AI tools that has the capacity to integrate AIS patient data and available evidence from the literature to guide AIS surgical treatment. To do so, a literature review on existing computer applications developed with regards to AIS evaluation and management was undertaken to gather all the elements that would lead to usable software in the clinical setting. This review highlighted the fact that many applications use a non-descript “black box” between input and output, which limits clinical integration where management based on evidence is essential. In the first study, we developed a decision tree to classify AIS based on the Lenke scheme. The Lenke scheme was popular in the past, but has recently been criticized for its complexity leading to intra and inter-observer variability. The resultant decision tree demonstrated an ability to increase classification accuracy in proportion to the time spent classifying. Importantly, this increase in accuracy was independently of previous knowledge about AIS. In the second study, a surgical strategy rule-based algorithm was developed using rules extracted from the literature to guide surgeons in the selection of the approach and levels of fusion for AIS. When this rule-based algorithm was tested against a database of 1,556 AIS cases, it was able to output a surgical strategy similar to the one undertaken by an expert surgeon in 70% of cases. This study confirmed the ability of a rule-based algorithm based on the literature to output valid surgical strategies. In the third study, classification of 1,776 AIS patients was undertaken using Kohonen Self-Organizing-Maps (SOM), which is a kind of neural network that demonstrates there are typical AIS curve types (i.e: single curves and double thoracic curves) for which there is little variability in surgical treatment when compared to the recommendations from the Lenke scheme. Other curve types (i.e: multiple curves or in transition zones between typical curves) have much greater variability in surgical strategy. Finally, a software platform integrating all the above studies was developed. The interface of this software platform allows for: 1) the input of AIS patient radiographic measurements; 2) classification of the curve type using the decision tree; 3) output of surgical strategy options based on rules extracted from the literature. A comparison of surgical correction obtained by patients receiving surgical treatment suggested by the software showed a tendency to obtain better balance -though non-statistically significant - than those who were treated differently from the surgical strategies outputted by the software. Overall, studies from this thesis suggest that the use of AI algorithms in the classification and selection of surgical strategies for AIS can be integrated in a software platform that could assist the surgeon in the planning of appropriate surgical treatment.
Zhezherun, Yuliia. "The right to privacy through the development of smart technologies : how our personal health data is affected". Thesis, 2020. http://hdl.handle.net/1866/24458.
Pełny tekst źródłaThe evolution of technology, notwithstanding its benefits, can negatively impact some of our fundamental rights as it develops faster than the latter. Indeed, this thesis aims to meet challenges generated by smart technologies and the impact they can have on the health of communities as well as on our fundamental rights. This thesis focuses on the legal constraints, present and to come, including the right to privacy, through the development and use of smart technologies that seize our personal health information. More specifically, this work seeks to analyze whether the benefits of accessing our information through smart technologies to improve the health and safety of populations outweigh the legal consequences. This work explores the potential of smart technologies, the interest in using them individually and collectively, especially in the public health sector, and the human rights violations their use can generate. Moreover, it looks at technological innovations that help improve State health systems to be able to better respond to future epidemics, particularly at the international level, such as at the WHO. These data, followed by other possible complications due to the increased use of intelligent technologies that restrict our privacy, will allow us to conclude whether such an intrusion in our right to privacy can be justified in a free and democratic society. Finally, this work examines the limits of the social acceptability of the invasion of privacy in exchange for better health conditions so that States and supra-State bodies can make informed decisions, without violating constitutional rights. This work will help us understand the issues that our judicial system will inevitably face while proposing strategies for the prevention of diseases and other health problems through the use of smart technologies. One of the main proposed solutions is the creation of a national and international database at the WHO generated by the data of smart health devices.