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Thèses sur le sujet « Medical machine »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Veropoulos, Konstantinos. "Machine learning approaches to medical decision making." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367661.

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Punugu, Venkatapavani Pallavi. "Machine Learning in Neuroimaging." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10284048.

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<p> The application of machine learning algorithms to analyze and determine disease related patterns in neuroimaging has emerged to be of extreme interest in Computer-Aided Diagnosis (CAD). This study is a small step towards categorizing Alzheimer's disease, Neurode-generative diseases, Psychiatric diseases and Cerebrovascular Small Vessel diseases using CAD. In this study, the SPECT neuroimages are pre-processed using powerful data reduction techniques such as Singular Value Decomposition (SVD), Independent Component Analysis (ICA) and Automated Anatomical Labeling (AAL). Each of the pre-proc

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Chi, Chih-Lin Street William N. "Medical decision support systems based on machine learning." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/283.

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Chi, Chih-Lin. "Medical decision support systems based on machine learning." Diss., University of Iowa, 2009. https://ir.uiowa.edu/etd/283.

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This dissertation discusses three problems from different areas of medical research and their machine learning solutions. Each solution is a distinct type of decision support system. They show three common properties: personalized healthcare decision support, reduction of the use of medical resources, and improvement of outcomes. The first decision support system assists individual hospital selection. This system can help a user make the best decision in terms of the combination of mortality, complication, and travel distance. Both machine learning and optimization techniques are utilized in t

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Xu, Chongrui. "Quantitative Radiomic Analysis for Prognostic Medical Applications." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21517.

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Radiomics, a non-invasive and quantitative mining medical imaging information method, could extract molecular biological features and enormous feature combinations to customise individualised treatment and solve the problem of heterogeneity, satisfying the standards of precision medicine. However, it faces many challenges in the feature selection process, including redundant features, irrelevant features and the overfitting risk. More important, people know little about radiomics biological background and its connection to radiology, so it is difficult to apply radiology directly to medicine a

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Letzner, Josefine. "Analysis of Emergency Medical Transport Datasets using Machine Learning." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215162.

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The selection of hospital once an ambulance has picked up its patient is today decided by the ambulance staff. This report describes a supervised machinelearning approach for predicting hospital selection. This is a multi-classclassification problem. The performance of random forest, logistic regression and neural network were compared to each other and to a baseline, namely the one rule-algorithm. The algorithms were applied to real world data from SOS-alarm, the company that operate Sweden’s emergency call services. Performance was measured with accuracy and f1-score. Random Forest got the b

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Rosén, Henrik. "Automation of Medical Underwriting by Appliance of Machine Learning." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171843.

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One of the most important fields regarding growth and development for mostorganizations today is the digitalization, or digital transformation. The offering oftechnological solutions to enhance existing, or create new, processes or products isemerging. That is, it’s of great importance that organizations continuously affirm thepotential of applying new technical solutions into their existing processes. For example, a well implemented AI solution for automation of an existing process is likely tocontribute with considerable business value.Medical underwriting for individual insurances, which is

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Afnouch, Marwa. "Machine Learning Applications in Medical Diagnosis, case study : bone metastasis." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0039.

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Les métastases sont un groupe de cellules anormales qui se développe en dehors des limites de l’organe d’origine et se propagent à d’autres organes. En particulier, les métastases osseuses proviennent d’un organe du corps, tel que le sein, le poumon ou la prostate, et se propagent à l’os. Bien que cette maladie ait été découverte il y a plus d’un siècle, elle n’est toujours pas bien définie et les traitements existants sont peu efficaces, probablement parce qu’elle est difficile et longue à détecter. Pour aider les médecins, les nouvelles techniques d’apprentissage automatique sont une solutio

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Saleem, Afsah. "Machine learning for computer-aided diagnostics from complex medical images." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2025. https://ro.ecu.edu.au/theses/2927.

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Machine learning has significantly transformed medical image analysis in the current age of artificial intelligence offering vast potential in improving disease diagnosis and management. Cardiovascular diseases (CVDs) are among the leading cause of global mortality, emphasizing the need for early detection for effective intervention and prevention. Abdominal Aortic Calcification (AAC) is an early indicator and contributor to Atherosclerotic Cardiovascular Diseases (ASCVDs) and is commonly assessed through imaging modalities such as computed tomography (CT), X-rays, and Dual-energy X-ray Absorp

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Bao, Guoqing. "End-to-End Machine Learning Models for Multimodal Medical Data Analysis." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28153.

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The pathogenesis of infectious and severe diseases including COVID-19, metabolic disorders, and cancer can be highly complicated because it involves abnormalities in genetic, metabolic, anatomical as well as functional levels. The deteriorative changes could be quantitatively monitored on biochemical markers, genome-wide assays as well as different imaging modalities including radiographic and pathological data. Multimodal medical data, involving three common and essential diagnostic disciplines, i.e., pathology, radiography, and genomics, are increasingly utilized to unravel the complexity of

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Hjalmarsson, Victoria. "Machine learning and Multi-criteria decision analysis in healthcare : A comparison of machine learning algorithms for medical diagnosis." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33940.

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Medical records consist of a lot of data. Nevertheless, in today’s digitized society it is difficult for humans to convert data into information and recognize hidden patterns. Effective decision support tools can assist medical staff to reveal important information hidden in the vast amount of data and support their medical decisions. The objective of this thesis is to compare five machine learning algorithms for clinical diagnosis. The selected machine learning algorithms are C4.5, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbor (kNN) and Naïve Bayes classifier. First, the mac

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Bradley, Andrew Peter. "Machine learning for medical diagnostics: Techniques for feature extraction, classification, and evaluation." Thesis, University of Queensland, 1996.

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The use of computers as diagnostic aids in medicine is becoming a reality in the clinical arena; a major factor to this trend being the successful application of machine learning techniques. Three fundamentally different approaches to machine learning have been identified, which we call Exemplar, Hyper-plane, and Hyper-rectangle based methods. Part of this thesis is devoted to a novel hyper- rectangle based algorithm called the Multiscale Classifier (MSC), which is implemented as an inductive decision tree. The MSC can be applied to any N-dimensional classification problem, successively splitt

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Hêche, Félicien. "Risk-sensitive machine learning for emergency medical resource optimization and other applications." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD048.

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L'augmentation significative de la demande de soins médicaux d'urgence au cours des dernières décennies exerce une pression considérable sur les Services d'Aide Médicale Urgente (SAMU), entraînant de nombreux effets indésirables. Motivée par les résultats remarquables obtenus par les algorithmes d'apprentissage automatique modernes, cette thèse explore initialement l'utilisation de ces modèles afin d'optimiser les ressources du SAMU dans l'espoir de relever certains défis auxquels ce système de santé est confronté. La première contribution de cette thèse consiste à introduire un nouvel algorit

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Postovskaya, Anna. "Rule-based machine learning for prediction of Macaca mulatta SIV-vaccination outcome using transcriptome profiles." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-440182.

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One of the reasons, why the development of an effective HIV vaccine remains challenging, is the lack of understanding of potential vaccination-induced protection mechanisms. In the present study, Rhesus Macaques (Macaca mulatta) gene expression profiles obtained during vaccination with promising candidate vaccines against Simian Immunodeficiency Virus (SIV) were processed with a rule-based supervised machine learning approach to analyze the effects of vaccine combination treatment. The findings from constructed rule-based classifiers suggest that the immune response against SIV builds up throu

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Folk, Lillian C. "A study of the Veterinary Medical Database /." Free to MU Campus, others may purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1421133.

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Havaei, Seyed Mohammad. "Machine learning methods for brain tumor segmentation." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10260.

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Abstract : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20. There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year. To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor

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Andersson, Olle. "Predicting Patient Length Of Stay at Time of Admission Using Machine Learning." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255150.

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This master thesis investigates the possibility of using machine learning methods to predict patient length of stay at the time of admission to a clinical ward from the emergency department. The main aim of this thesis is to provide a comparative analysis of different algorithms and to suggest a suitable model that can be used in a hospital prediction software. The results show that it is possible to achieve a balanced accuracy of 0.72 at the time of admission and of 0.75 at a later stage in the process. The suggested algorithm was Random Forest which combines good accuracy with effective trai

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Bates, Russell. "Learning to extract tumour vasculature : techniques in machine learning for medical image analysis." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:933383a8-be39-44df-9beb-af94b32723ab.

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Cancer is a leading cause of death worldwide with an estimated 14 million new cases occurring yearly and approximately 8 million deaths. Although much progress has been made in the understanding and treatment of cancer, there are still many mechanisms that remain poorly understood. The development of vasculature is known to be a key element in facilitating the growth of a tumour. Modern imaging modalities such as multi-photon fluorescence microscopy allow unprecedented opportunities to examine and quantify this vasculature in vivo. However, the appearance of vascular networks, imaged at these

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Bardolet, Pettersson Susana. "Managing imbalanced training data by sequential segmentation in machine learning." Thesis, Linköpings universitet, Avdelningen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-155091.

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Imbalanced training data is a common problem in machine learning applications. Thisproblem refers to datasets in which the foreground pixels are significantly fewer thanthe background pixels. By training a machine learning model with imbalanced data, theresult is typically a model that classifies all pixels as the background class. A result thatindicates no presence of a specific condition when it is actually present is particularlyundesired in medical imaging applications. This project proposes a sequential system oftwo fully convolutional neural networks to tackle the problem. Semantic segme

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Doğru, Gökhan. "Terminological Quality Evaluation in Turkish to English Corpus-Based Machine Translation in Medical Domain." Doctoral thesis, Universitat Autònoma de Barcelona. Programa de Doctorat en Traducció i Estudis Interculturals, 2021. http://hdl.handle.net/10803/673337.

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Els aspectes generals de qualitat de la traducció automàtica (TA), com l’adequació i la fluïdesa, s’han estudiat àmpliament, però els aspectes més detallats, com la qualitat de la traducció de la terminologia, s’han subestimat, especialment en el context dels estudis de traducció. L’objectiu d’aquest estudi és analitzar els tipus i freqüències d’errors terminològics en la traducció automàtica estadística (TAE) i la traducció automàtica neuronal (TAN) personalitzades amb l’objectiu final de comprendre com el tipus de sistema de TA, el tipus de corpus i la mida del corpus afecten la qualitat de

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Ive, Julia. "Towards a Better Human-Machine Collaboration in Statistical Translation : Example of Systematic Medical Reviews." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS225/document.

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La traduction automatique (TA) a connu des progrès significatifs ces dernières années et continue de s'améliorer. La TA est utilisée aujourd'hui avec succès dans de nombreux contextes, y compris les environnements professionnels de traduction et les scénarios de production. Cependant, le processus de traduction requiert souvent des connaissances plus larges qu'extraites de corpus parallèles. Étant donné qu'une injection de connaissances humaines dans la TA est nécessaire, l'un des moyens possibles d'améliorer TA est d'assurer une collaboration optimisée entre l'humain et la machine. À cette fi

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Liednikova, Anna. "Human-Machine Dialogue in the Medical Field. Using Dialog to Collect Important Patient Information." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0149.

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Les systèmes de dialogue pour les soins de santé sont développés pour automatiser et simplifier les tâches de routine telles que la collecte d'informations sur les patients ou la prise de rendez-vous. Souvent, ces modèles sont entraînés à imiter l'interaction entre le médecin et le patient, en étant disponibles à tout moment. C'est particulièrement important pour les patients souffrant de maladies chroniques. Ils consultent régulièrement un médecin et remplissent des questionnaires standardisés, tout en s'habituant à donner les mêmes réponses et en étant confrontés à des difficultés entre les

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Frunza, Oana Magdalena. "Personalized Medicine through Automatic Extraction of Information from Medical Texts." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22724.

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The wealth of medical-related information available today gives rise to a multidimensional source of knowledge. Research discoveries published in prestigious venues, electronic-health records data, discharge summaries, clinical notes, etc., all represent important medical information that can assist in the medical decision-making process. The challenge that comes with accessing and using such vast and diverse sources of data stands in the ability to distil and extract reliable and relevant information. Computer-based tools that use natural language processing and machine learning

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Nyongesa, Henry Okola. "Genetic based machine learning allied to multi-variable fuzzy control of anaesthesia." Thesis, University of Sheffield, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295759.

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Wallis, David. "A study of machine learning and deep learning methods and their application to medical imaging." Thesis, université Paris-Saclay, 2021. http://www.theses.fr/2021UPAST057.

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Nous utilisons d'abord des réseaux neuronaux convolutifs (CNNs) pour automatiser la détection des ganglions lymphatiques médiastinaux dans les images TEP/TDM. Nous construisons un modèle entièrement automatisé pour passer directement des images TEP/TDM à la localisation des ganglions. Les résultats montrent une performance comparable à celle d'un médecin. Dans la seconde partie de la thèse, nous testons la performance, l'interprétabilité et la stabilité des modèles radiomiques et CNN sur trois ensembles de données (IRM cérébrale 2D, TDM pulmonaire 3D, TEP/TDM médiastinale 3D). Nous comparons l

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Bustos, Aurelia. "Extraction of medical knowledge from clinical reports and chest x-rays using machine learning techniques." Doctoral thesis, Universidad de Alicante, 2019. http://hdl.handle.net/10045/102193.

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This thesis addresses the extraction of medical knowledge from clinical text using deep learning techniques. In particular, the proposed methods focus on cancer clinical trial protocols and chest x-rays reports. The main results are a proof of concept of the capability of machine learning methods to discern which are regarded as inclusion or exclusion criteria in short free-text clinical notes, and a large scale chest x-ray image dataset labeled with radiological findings, diagnoses and anatomic locations. Clinical trials provide the evidence needed to determine the safety and effectiveness o

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Verleyen, Wim. "Machine learning for systems pathology." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4512.

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Systems pathology attempts to introduce more holistic approaches towards pathology and attempts to integrate clinicopathological information with “-omics” technology. This doctorate researches two examples of a systems approach for pathology: (1) a personalized patient output prediction for ovarian cancer and (2) an analytical approach differentiates between individual and collective tumour invasion. During the personalized patient output prediction for ovarian cancer study, clinicopathological measurements and proteomic biomarkers are analysed with a set of newly engineered bioinformatic tool

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Chen, Li. "Statistical Machine Learning for Multi-platform Biomedical Data Analysis." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77188.

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Recent advances in biotechnologies have enabled multiplatform and large-scale quantitative measurements of biomedical events. The need to analyze the produced vast amount of imaging and genomic data stimulates various novel applications of statistical machine learning methods in many areas of biomedical research. The main objective is to assist biomedical investigators to better interpret, analyze, and understand the biomedical questions based on the acquired data. Given the computational challenges imposed by these high-dimensional and complex data, machine learning research finds its new opp

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Manivannan, Siyamalan. "Visual feature learning with application to medical image classification." Thesis, University of Dundee, 2015. https://discovery.dundee.ac.uk/en/studentTheses/10e26212-e836-4ccd-9b12-a576458de5eb.

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Various hand-crafted features have been explored for medical image classification, which include SIFT and Local Binary Patterns (LBP). However, hand-crafted features may not be optimally discriminative for classifying images from particular domains (e.g. colonoscopy), as not necessarily tuned to the domain’s characteristics. In this work, I give emphasis on learning highly discriminative local features and image representations to achieve the best possible classification performance for medical images, particularly for colonoscopy and histology (cell) images. I propose approaches to learn loca

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Rahman, M. Mostafizur. "Machine learning based data pre-processing for the purpose of medical data mining and decision support." Thesis, University of Hull, 2014. http://hydra.hull.ac.uk/resources/hull:10103.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. Sometimes, improved data quality is itself the goal of the analysis, usually to improve processes in a production database and the designing of decision support. As medicine moves forward there is a need for sophisticated decision support systems that make use of data mining to support more orthodox knowledge engineering and Health Informatics practice. However, the real-life medical data rarely complies with the requirement

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Feng, Yunyi. "Identification of Medical Coding Errors and Evaluation of Representation Methods for Clinical Notes Using Machine Learning." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1555421482252775.

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Deshpande, Hrishikesh. "Dictionary learning for pattern classification in medical imaging." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S032/document.

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La plupart des signaux naturels peuvent être représentés par une combinaison linéaire de quelques atomes dans un dictionnaire. Ces représentations parcimonieuses et les méthodes d'apprentissage de dictionnaires (AD) ont suscité un vif intérêt au cours des dernières années. Bien que les méthodes d'AD classiques soient efficaces dans des applications telles que le débruitage d'images, plusieurs méthodes d'AD discriminatifs ont été proposées pour obtenir des dictionnaires mieux adaptés à la classification. Dans ce travail, nous avons montré que la taille des dictionnaires de chaque classe est un

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Kanwal, Summrina. "Towards a novel medical diagnosis system for clinical decision support system applications." Thesis, University of Stirling, 2016. http://hdl.handle.net/1893/25397.

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Clinical diagnosis of chronic disease is a vital and challenging research problem which requires intensive clinical practice guidelines in order to ensure consistent and efficient patient care. Conventional medical diagnosis systems inculcate certain limitations, like complex diagnosis processes, lack of expertise, lack of well described procedures for conducting diagnoses, low computing skills, and so on. Automated clinical decision support system (CDSS) can help physicians and radiologists to overcome these challenges by combining the competency of radiologists and physicians with the capabi

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Wallner, Vanja. "Mapping medical expressions to MedDRA using Natural Language Processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-426916.

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Pharmacovigilance, also referred to as drug safety, is an important science for identifying risks related to medicine intake. Side effects of medicine can be caused by for example interactions, high dosage and misuse. In order to find patterns in what causes the unwanted effects, information needs to be gathered and mapped to predefined terms. This mapping is today done manually by experts which can be a very difficult and time consuming task. In this thesis the aim is to automate the process of mapping side effects by using machine learning techniques. The model was developed using informatio

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Alzubaidi, Laith. "Deep learning for medical imaging applications." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/227812/1/Laith_Alzubaidi_Thesis.pdf.

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This thesis investigated novel deep learning techniques for advanced medical imaging applications. It addressed three major research issues of employing deep learning for medical imaging applications including network architecture, lack of training data, and generalisation. It proposed three new frameworks for CNN network architecture and three novel transfer learning methods. The proposed solutions have been tested on four different medical imaging applications demonstrating their effectiveness and generalisation. These solutions have already been employed by the scientific community showing

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Massai, Elena. "A Machine Learning Approach for Better Understanding the Neuromodulation of Locomotion." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234218.

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Motor intent and control rely on complex high-level and spinal networks. Untilnow, little is known about this system’s organization and mechanisms. Whilecognitive abilities play an essential role in planning movements, learning andmemorizing, their involvement during stereotyped tasks execution, aslocomotion, is still controversial. Recently, the relationship between cognitivefunctions and gait has received increasing attention.Here, a machine learning approach is used to investigate the engagement ofdi↵erent cortical areas during motor activity. In particular, data coming fromthree subjects w

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Lin, Laura. "Applying human factors engineering to medical device design, an empirical evaluation of patient-controlled analgesia machine interfaces." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ29431.pdf.

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McGowan, Martin. "The development of an inline machine vision inspection system for operation in a medical device manufacturing facility." Thesis, Glasgow Caledonian University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443252.

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Kabir, Md Faisal. "Extracting Useful Information and Building Predictive Models from Medical and Health-Care Data Using Machine Learning Techniques." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31924.

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In healthcare, a large number of medical data has emerged. To effectively use these data to improve healthcare outcomes, clinicians need to identify the relevant measures and apply the correct analysis methods for the type of data at hand. In this dissertation, we present various machine learning (ML) and data mining (DM) methods that could be applied to the type of data sets that are available in the healthcare area. The first part of the dissertation investigates DM methods on healthcare or medical data to find significant information in the form of rules. Class association rule mining, a v

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Ansved, Linn, and Karin Eklann. "Exploring ways to convey medical information during digital triage : A combined user research and machine learning approach." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-386420.

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The aim of this project was to investigate what information is critical to convey to nurses when performing digital triage. In addition, the project aimed to investigate how such information could be visualized. This was done through a combined user research and machine learning approach, which enabled for a more nuanced and thorough investigation compared to only making use of one of the two fields. There is sparse research investigating how digital triaging can be improved and made more efficient. Therefore, this study has contributed with new and relevant insights. Three machine learning al

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COMELLI, Albert. "IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION." Doctoral thesis, Università degli Studi di Palermo, 2020. http://hdl.handle.net/10447/395504.

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Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excelle

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Drusiani, Alberto. "Deep Learning Text Classification for Medical Diagnosis." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17281/.

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The ICD coding is the international standard for the classification of diseases and related disorders, drawn up by the World Health Organization. It was introduced to simplify the exchange of medical data, to speed up statistical analyzes and to make insurance reimbursements efficient. The manual classification of ICD-9-CM codes still requires a human effort that implies a considerable waste of resources and for this reason several methods have been presented over the years to automate the process. In this thesis an approach is proposed for the automatic classification of medical diagnoses i

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Meng, Kai M. Eng Massachusetts Institute of Technology. "Reduce cycle time and work in process in a medical device factory : scheduling policies for needle assembly machine." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42328.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.<br>Includes bibliographical references (p. 58).<br>Many manufacturing firms have improved their operations by implementing a work-in-process (WIP) limiting control strategy. This project explores the application of this concept to limit WIP and reduce cycle time for the Becton, Dickinson and Company's manufacturing facility in Tuas, Singapore. BD's Eclipse Safety Needle production line is facing increasing pressure to reduce its high WIP and long cycle times. With the forecast of increasing demand,

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Strallhofer, Daniel, and Jonatan Ahlqvist. "Classifying Urgency : A Study in Machine Learning for Classifying the Level of Medical Emergency of an Animal’s Situation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231476.

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This paper explores the use of Naive Bayes as well a Linear Support Vector Machines in order to classify a text based on the level of medical emergency. The primary source of testing will be an online veterinarian service’s customer data. The aspects explored are whether a single text gives enough information for a medical decision to be made and if there are alternative data gathering processes that would be preferred. Past research has proven that text classiﬁers based on Naive Bayes and SVMs can often give good results. We show how to optimize the results so that important decisions can be

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Qian, Songyue. "Using convolutional neural network to generate neuro image template." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1546620227038248.

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Keelan, Oliver, and Henrik Mårtensson. "Feature Engineering and Machine Learning for Driver Sleepiness Detection." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142001.

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Falling asleep while operating a moving vehicle is a contributing factor to the statistics of road related accidents. It has been estimated that 20% of all accidents where a vehicle has been involved are due to sleepiness behind the wheel. To prevent accidents and to save lives are of uttermost importance. In this thesis, given the world’s largest dataset of driver participants, two methods of evaluating driver sleepiness have been evaluated. The first method was based on the creation of epochs from lane departures and KSS, whilst the second method was based solely on the creation of epochs ba

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Villot, Berling Mathilda, and Julia Önerud. "Simulating Fetal ECG Using Machine Learning on Ultrasound Images." Thesis, KTH, Medicinteknik och hälsosystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291249.

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ECG is used clinically to detect a multitude of medical conditions, such as heart-problems like arrhythmias and heart failure, and to give a good general image of the function of the heart with a quick and harmless exam. In many clinical cases, normal ECG measurements cannot be taken, such as with fetuses where ECG signals from the mother’s own body hinder the measurement. This paper examines using machine learning algorithms to be able to simulate ECG graphs from ultrasound data alone. These algorithms are trained on ultrasound and ECG data acquired from the same patient simultaneously. The d

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Ruan, Wei. "Topic Segmentation and Medical Named Entities Recognition for Pictorially Visualizing Health Record Summary System." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39023.

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Medical Information Visualization makes optimized use of digitized data of medical records, e.g. Electronic Medical Record. This thesis is an extended work of Pictorial Information Visualization System (PIVS) developed by Yongji Jin (Jin, 2016) Jiaren Suo (Suo, 2017) which is a graphical visualization system by picturizing patient’s medical history summary depicting patients’ medical information in order to help patients and doctors to easily capture patients’ past and present conditions. The summary information has been manually entered into the interface where the information can be taken fr

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Kantedal, Simon. "Evaluating Segmentation of MR Volumes Using Predictive Models and Machine Learning." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171102.

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A reliable evaluation system is essential for every automatic process. While techniques for automatic segmentation of images have been extensively researched in recent years, evaluation of the same has not received an equal amount of attention. Amra Medical AB has developed a system for automatic segmentation of magnetic resonance (MR) images of human bodies using an atlas-based approach. Through their software, Amra is able to derive body composition measurements, such as muscle and fat volumes, from the segmented MR images. As of now, the automatic segmentations are quality controlled by cli

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Finch, Dezon K. "TagLine: Information Extraction for Semi-Structured Text Elements In Medical Progress Notes." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4321.

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Text analysis has become an important research activity in the Department of Veterans Affairs (VA). Statistical text mining and natural language processing have been shown to be very effective for extracting useful information from medical documents. However, neither of these techniques is effective at extracting the information stored in semi-structure text elements. A prototype system (TagLine) was developed as a method for extracting information from the semi-structured portions of text using machine learning. Features for the learning machine were suggested by prior work, as well as by

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