Informatik
Refine
Document Type
- Master's Thesis (9)
- Bachelor Thesis (2)
- Preprint (2)
Language
- English (13) (remove)
Has Fulltext
- yes (13)
Is part of the Bibliography
- no (13)
Keywords
- Neuronales Netz (3)
- Maschinelles Lernen (2)
- Alzheimer (1)
- Application (1)
- Bildgebung (1)
- Data Mining (1)
- Datenanalyse (1)
- Deep learning (1)
- Electronic Commerce (1)
- Gangzyklus-Erkennung (1)
Institute
- Informatik (13)
An architectural concept for implementing the socio-technical workflow of Digital Pathology in Chile
(2014)
Virtual Microscopy opens up the possibility to remotely access high quality images at large scales for scientific research, education, and clinical application. For clinical diagnostics, Digital Pathology (DP) presents a novel opportunity to reduce variability [Bauer et al., 2013] due to the reproducible access to Whole Slide Imaging, quantitative parameters (e.g. HER2 stained membrane) [Al-Janabi et al., 2012], second opinion and Quality Assurance [Ho et al., 2013]. Despite of the mentioned advantages, the challenge remains to incorporate DP into the pathologists workflow within a heterogeneous environment of systems and infrastructures [Stathonikos
et al., 2013]. Different issues must be solved in order to optimize the impact of DP in the daily clinical practice [Daniel et al., 2012] [Ho et al., 2006]. The integration needs precise planning and comprehensive evaluation for adopting this technology
[Stathonikos et al., 2013]. This thesis will focus on an organizational development approach based on a Socio-Technical System (STS). The socio-technical approach covers: (i) the technical issue: tissue-scanner, NDP.view, NDP.serve, analysis software, and (ii) the social issue: pathologists, technicians. In order to improve the integration, a joint optimization (of i and ii) is necessary. The developed STS approach will optimize the integration of DP towards improved workflows in clinical environments. The improved workflows will reduce the pathologists turnaround time, improve the certainty of the diagnostics, and provide a more effective patient care within the covered institutions. An overt multi-site Participatory Observation, Questionnaires, and Business Process Modelling Notation will be used to analyse the existing pathological workflows. Based on this, the system will be modelled with the 3lgm2 Toolkit [Winter et al., 2007] under consideration of various technical subsystems that are present in the clinical environment. Afterwards, the interfaces between subsystems and its possible interoperabilities will be evaluated, taking into account the different existing standards and guidelines for image processing and management, as well as business processes in DP. In order to analyse the existing preconditions a questionnaire will be evaluated to establish a robust and valid view. In addition, the overt participatory observation will support this elevation, giving a deeper insight on the social part. This observation also covers the technical side including the whole pathological process. The socio technical model will then reveal measurable potential for optimization with incorporated DP (e.g. higher throughput for slides). The organizational development approach consists of a Socio-Technical System based on overt multi-site participatory observations, questionnaires, business process modelling and 3LGM2, will optimize the use of Digital Pathology in the daily clinical practice and raise the acceptance to incorporate integrate the new technology within the dayly workflow through the user centred process of incorporation.
• Perform and evaluate a questionnaire and a participant observation of pathologists work days in private & public institutions
• Create and evaluate a 3lgm2 model
• Model the current pathological process (viewpoint of pathologist & technical assistant) & perform and evaluate a contextual inquiry to elevate the pathologists requirements & expectations towards the system
• Test the future WF according the model parameters.
This project will detect unsuspected interrelations and interdependencies within the socio- technical workflow with a pathology laboratory. The observation will reveal the action conformity as well as the environment in which the process has to be embedded. Furthermore it will establish an optimized workflow for a specific clinical environment to prepare the implementation of DP. Additionally it will be possible to
quantify digitized images in order to improve decision making and lastly to improve patient care. In the future it will be possible to extend automated image analysis in order to support clinical decision support. Depending on acceptance, this can lead towards an automated clinical decision support for cases with low complexity.
Initial results of an ongoing research in the field of reactive mobile autonomy are presented. The aim is to create a reactive obstacle avoidance method for mobile agent operating in dynamic, unstructured, and unpredictable environment. The method is inspired by the stimulus-response behavior of simple animals. An obstacle avoidance controller is developed that uses raw visual information of the environment. It employs reinforcement learning and is therefore capable of self-developing. This should result with obstacle avoidance behavior that is adaptable and therefore generalizes on various operational modalities. The general assumptions of the agent capabilities, the features of the environment as well as the initial result of the simulation are presented. The plans for improvement and suitable performance evaluation are suggested.
Background: Stroke rehabilitation is a complex process that requires collaboration between stroke patients
and various health professionals. One important component of the rehabilitation is to set goals collaboratively with health professionals. The goal setting process can be time-consuming. In many cases, it is complicated for the patient and difficult to track for the health professionals. A simple user interface that supports patients, their family members and health professionals can help both sides to make the goal setting and attainment process easier.
Objectives: The aim is to design and develop a software for the goal attainment process of stroke patients with milder disabilities that facilitates goal setting process and the traceability of the goal progress for patients and health professionals.
Methods: Based on previous evaluated results, the web interface was developed and improved. Using this knowledge, a goal setting interface was added. To analyze the the goal setting process, goal attainment scaling (GAS) was included as well as parts of the International Classification of Functioning, Disability and Health (ICF) core set for stroke. The results were discussed afterwards in focus groups and evaluated based on two stroke patients, one family member and health professionals.
Results: We developed an interactive prototype, that can aid the rehabilitation at home by inserting
problems with ICF codes and different kinds of goals, creating new activities and tracing goal progress by reviewing the different goals. With the help of the GAS the outcome of the patient’s goals are visualized by a line chart presenting the positive or negative outcomes of the stroke rehabilitation.
Conclusion: The interactive prototype showed that it can support stroke patients during their rehabilitation
at home. A usability test indicated that the goal setting and attainment process was perceived as useful for patients and their family members. Small improvements have to be made to simplify use and error handling. For health professionals, the prototype could also simplify the documentation process by using ICF in the prototype, and also improving collaboration when using the tool for coordination.
There are many drug interactions and to know every single interaction is impossible. In Uganda, a country located in East Africa, patients often do not get a patient information leflaet when a physician prescribes drugs because they only get the drugs without packaging and information inside. Even in developed countries many poeple die because of drug interactions.
This work aims at developing a clinical decision support system for different kinds of drug interactions: 1) drug-drug interaction,
2) drug-food interactions,
3) drug-condition
interactions and
4) drug-disease interactions.
This system must be integrated into an
existing hospital information system called electronic Health Management Information System (eHMIS).
In the first part of this thesis different kinds of clinical decision support systems are described to find out which one is the best for eHMIS. The two different types are knowledge-based and non knowledge-based systems. The second part of this thesis, the data base of eHMIS is extended to have a full
knowledge base for the new module which contains drug-drug interactions, drug-food interactions, drug-disease interactions as well as drug-condition interactions. Therefore new tables were created and filled with data of several data bases with drug interactions. The last part is about designing the clinical decision support system for drug interactions
with the knowledge base of eHMIS, including the implementation considering the integration into the existing system. To know how health professionals in Uganda work
with an electronical health system as well as their other work ows was important. The system now runs in a hospital in Kampala, the capital of Uganda and in a health center level three in Mifumi, a village located in the east of the country.
Every year, hundreds of thousands of patients are affected by treatment failure or adverse drug reactions, many of which could be revented by pharmacogenomic testing. To address these deficiencies in care, clinics require
automated clinical decision support through computer based systems, which provide clinicians with patient-specific ecommendations. The primary knowledge needed for clinical pharmacogneomics is currently being
developed through textual and unstructured guidelines.
In this thesis, it is evaluated whether a web service can annotate clinically relevant genetic variants with guideline information using web services and identify areas of challenge. The proposed tool displays a formal representation of pharmacogenomic guideline information through a web service and existing resources. It enables the annotation of variant call format (VCF) files with clinical guideline information from the Pharmacogenomic Knowledge Base (PharmGKB) and Clinical Pharmacogenetics Implementation Consortium (CPIC).
The applicability of the web service to nnotate clinically relevant variants with pharmacogenomics guideline information is evaluated by translating five guidelines to a web service workflow and executing the process to annotate publically available genomes. The workflow finds genetic variants covered in CPIC guidelines and influenced drugs.
The results show that the web service could be used to annotate in real time clinically relevant variants with up-to-date pharmacogenomics guideline information, although several challenges such as translating variants into star allele nomenclature and the absence of a unique haplotype nomenclature
remain before the clinical implementation of this approach and the use on other drugs.
eHMIS is a Ugandan Hospital Information System (HIS), which targets the Sub-Saharan market. In its first version all forms were programmed statically and adaptations were done by code modifications. In 2014 the development of a second version of eHMIS based on Java started.
This work aims at introducing dynamic forms to this new version. While forms that are significantly important to the workflow of the application will remain static, others are replaced by forms that are dynamically designed by the user. By that, the application will become more flexible and local and situational tailoring will be possible without inducing extra costs.
In this thesis the design, implementation and testing of dynamic forms in eHMIS is discussed. The architecture is based on the questionnaire resource of FHIR®. The module enables the user to create questions and group them into sections and questionnaires. For each question the type of answer expected and other constraints can be defined. A user interface covering all functions was designed, so that no programming skills are required. In a first step dynamic forms were integrated in the application's workflow for recording symptoms, though other fields of application are possible. For testing, a usability experiment was conducted in Tororo Hospital in Eastern Uganda, using the thinking aloud method. Results were analysed and evaluated to detect usability problems and gain a general impression of user satisfaction.
A considerable amount of research in the field of modern robotics deals with mobile agents and their autonomous operation in unstructured, dynamic, and unpredictable environments. Designing robust controllers that map sensory input to action in order to avoid obstacles remains a challenging task. Several biological concepts are amenable to autonomous navigation and reactive obstacle avoidance.
We present an overview of most noteworthy, elaborated, and interesting biologically-inspired approaches for solving the obstacle avoidance problem. We categorize these approaches into three groups: nature inspired optimization, reinforcement learning, and biorobotics. We emphasize the advantages and highlight potential drawbacks of each approach. We also identify the benefits of using biological principles in artificial intelligence in various research areas.
Medical imaging produces many images every day in clinical routine. Keeping up with the
daily image analysis task and this vast amount of data is quite a challenge for radiologists.
However, these analysis tasks can be automated with well-proven automatic segmentation
methods. Segmentation reviewing of an expert is necessary because learningbased
automatic segmentation methods may not perform well on exceptional image
data. Creating valid segmentations by reviewing them also improve the learning-based
methods.
Combining established standards with modern technologies creates a flexible environment
to efficiently evaluate multiple segmentation algorithm outputs based on different metrics
and visualizations and report these analysis results back to a clinical system environment.
The presented software system can inspect such quantitative results in a fast and intuitive
way, potentially improving the daily repetitive segmentation review and rework of a
research radiologist. The presented system is designed to be integrated into a virtual
distributed computing environment with other systems and analysis methods. Critical
factors for this particular environment are the handling of many patient data and routine
automated analysis with state of the art technology.
First experiments show that the time to review automatic segmentation results can be
roughly divided in half while the confidence of the radiologist is enhanced. The system
is also able to highlight individual slices which are essential for the expert’s review
decision. For this highlighting, different metric scores are compared and evaluated.
Alzheimer’s Disease affects millions of people worldwide, but till today, the gold standard
for definitive diagnosis of this disease is a biopsy. Nevertheless, with the progress
of the disease, a volume loss in the Hippocampus can be observed. Therefore, good
segmentation methods are crucial to facilitate quantification of this loss.
The focus of this work is on the development of a Machine Learning algorithm, more
precisely a Generative Adversarial Network, for the automated segmentation of the
human Hippocampus and its substructures in Magnetic Resonance Images. In particular,
the task is to determine if the integration of a pre-trained network that generates
segmentations into a Generative Adversarial Network scheme can improve generated
segmentations. In this context, a segmentation network in form of a U-net corresponds
to the generator. The discriminator is developed separately and merged in a second
step with the generator for combined training.
With a literature review regarding the automated segmentation of the Hippocampus,
current methods in this field and their medical and technological basics were identified.
The datasets were preprocessed to make them suitable for the use in a neural
network. In the training process, the generator was trained first until convergence.
Then, the Generative Adversarial Network including the pre-trained generator was
trained. The outcomes were evaluated via cross-validation in two different datasets
(Kulaga-Yoskovitz and Winterburn). The Generative Adversarial Network scheme
was tested regarding different architectural and training aspects, including the usage
of skip-connections and a combined loss function.
The best results were achieved in the Kulaga-Yoskovitz dataset with a Dice coefficient
of 90.84 % after the combined training of generator and discriminator with a joined
loss function. This improves the current state of the art method in the same task and
dataset with a Dice index of 88.79 % by Romero [Rom17]. Except of two cases in the
Winterburn dataset, the proposed combined method could always improve the Dice
results after the training of only the generator, even though only by a small amount.
In this bachelor thesis, different models for predicting the influenza virus are
examined in more detail.
The focus is on epidemiological compartmental models, as well as on different
Machine Learning approaches.
In particular, the basics chapter presents the SIR model and its various extensions.
Furthermore, Deep Learning and Social Network approaches are
investigated and the applied methods of a selected article are analysed in more
detail.
The practical part of this work consists in the implementation of a Multiple
Linear Regression model and an Artificial Neural Network. For the development
of both models the programming language Python was chosen using the
Deep Learning Framework Keras.
Tests were performed with real data from the Réseau Sentinelles, a French
organisation for monitoring national health.
The results of the tests show that the Neural Network is able to make better
predictions than the Multiple Linear Regression model.
The discussion shows ideas for improving influenza prediction including the
establishment of a worldwide collaboration between the surveillance centres as
well as the consolidation of historical data with real-time social media data.
Therefore, this work consists of a state-of-the art of models regarding the
spread of influenza virus, the development and comparison of several models
programmed in Python, evaluated on real data.