Image database system for glaucoma diagnosis support

Tato práce popisuje přehled standardních a pokročilých metod používaných k diagnose glaukomu v ranném stádiu. Na základě teoretických poznatků je implementován internetově orientovaný informační systém pro oční lékaře, který má tři hlavní cíle. Prvním cílem je možnost sdílení osobních dat konkrétního pacienta bez nutnosti posílat tato data internetem. Druhým cílem je vytvořit účet pacienta založený na kompletním očním vyšetření. Posledním cílem je aplikovat algoritmus pro registraci intenzitního a barevného fundus obrazu a na jeho základě vytvořit internetově orientovanou tři-dimenzionální vizualizaci optického disku. Tato práce je součásti DAAD spolupráce mezi Ústavem Biomedicínského Inženýrství, Vysokého Učení Technického v Brně, Oční klinikou v Erlangenu a Ústavem Informačních Technologií, Friedrich-Alexander University, Erlangen-Nurnberg.This master thesis describes a conception of standard and advanced eye examination methods used for glaucoma diagnosis in its early stage. According to the theoretical knowledge, a web based information system for ophthalmologists with three main aims is implemented. The first aim is the possibility to share medical data of a concrete patient without sending his personal data through the Internet. The second aim is to create a patient account based on a complete eye examination procedure. The last aim is to improve the HRT diagnostic method with an image registration algorithm for the fundus and intensity images and create an optic nerve head web based 3D visualization. This master thesis is a part of project based on DAAD co-operation between Department of Biomedical Engineering, Brno University of Technology, Eye Clinic in Erlangen and Department of Computer Science, Friedrich-Alexander University, Erlangen-Nurnberg.

ANN for Diagnosing Hepatitis Virus

Abstract: This paper presents an artificial neural network based approach for the diagnosis of hepatitis virus. A number of factors that may possibly influence the performance of patients were outlined. Such factors as age, sex, Steroid, Antivirals, Fatigue, Malaise, Anorexia, Liver Big, Liver Firm Splean Palpable, Spiders, Ascites, Varices, Bilirubin, Alk Phosphate, SGOT, Albumin, Protine and Histology, were then used as input variables for the ANN model . Test data evaluation shows that the ANN model is able to correctly predict the diagnosis of more than 93% of prospective Patients

“EFFECTIVENESS OF AN EDUCATIONAL PROGRAM TO ENHANCE SELF-CARE SKILLS AFTER ACUTE CORONARY SYNDROME: A QUASI-EXPERIMENTAL STUDY”

The aim of this study is to determine the effectiveness of an educational program to enhance self-care skills in patients after an acute coronary syndrome. According to the findings of the study, a systematized and structured educational program, is effective in developing self-care skills in patients after an acute coronary syndrome.

Interactive exploration of population scale pharmacoepidemiology datasets

Population-scale drug prescription data linked with adverse drug reaction (ADR) data supports the fitting of models large enough to detect drug use and ADR patterns that are not detectable using traditional methods on smaller datasets. However, detecting ADR patterns in large datasets requires tools for scalable data processing, machine learning for data analysis, and interactive visualization. To our knowledge no existing pharmacoepidemiology tool supports all three requirements. We have therefore created a tool for interactive exploration of patterns in prescription datasets with millions of samples. We use Spark to preprocess the data for machine learning and for analyses using SQL queries. We have implemented models in Keras and the scikit-learn framework. The model results are visualized and interpreted using live Python coding in Jupyter. We apply our tool to explore a 384 million prescription data set from the Norwegian Prescription Database combined with a 62 million prescriptions for elders that were hospitalized. We preprocess the data in two minutes, train models in seconds, and plot the results in milliseconds. Our results show the power of combining computational power, short computation times, and ease of use for analysis of population scale pharmacoepidemiology datasets. The code is open source and available at: https://github.com/uit-hdl/norpd_prescription_analyse

Lymphoscintigraphy and triangulated body marking for morbidity reduction during sentinel node biopsy in breast cancer

Current trends in patient care include the desire for minimizing invasiveness of procedures and interventions. This aim is reflected in the increasing utilization of sentinel lymph node biopsy, which results in a lower level of morbidity in breast cancer staging, in comparison to extensive conventional axillary dissection. Optimized lymphoscintigraphy with triangulated body marking is a clinical option that can further reduce morbidity, more than when a hand held gamma probe alone is utilized. Unfortunately it is often either overlooked or not fully understood, and thus not utilized. This results in the unnecessary loss of an opportunity to further reduce morbidity. Optimized lymphoscintigraphy and triangulated body marking provides a detailed 3 dimensional map of the number and location of the sentinel nodes, available before the first incision is made. The number, location, relevance based on time/sequence of appearance of the nodes, all can influence 1) where the incision is made, 2) how extensive the dissection is, and 3) how many nodes are removed. In addition, complex patterns can arise from injections. These include prominent lymphatic channels, pseudo-sentinel nodes, echelon and reverse echelon nodes and even contamination, which are much more difficult to access with the probe only. With the detailed information provided by optimized lymphoscintigraphy and triangulated body marking, the surgeon can approach the axilla in a more enlightened fashion, in contrast to when the less informed probe only method is used. This allows for better planning, resulting in the best cosmetic effect and less trauma to the tissues, further reducing morbidity while maintaining adequate sampling of the sentinel node(s)

A New Deep State-Space Analysis Framework for Patient Latent State Estimation and Classification from EHR Time Series Data

Many diseases, including cancer and chronic conditions, require extended treatment periods and long-term strategies. Machine learning and AI research focusing on electronic health records (EHRs) have emerged to address this need. Effective treatment strategies involve more than capturing sequential changes in patient test values. It requires an explainable and clinically interpretable model by capturing the patient's internal state over time. In this study, we propose the "deep state-space analysis framework," using time-series unsupervised learning of EHRs with a deep state-space model. This framework enables learning, visualizing, and clustering of temporal changes in patient latent states related to disease progression. We evaluated our framework using time-series laboratory data from 12,695 cancer patients. By estimating latent states, we successfully discover latent states related to prognosis. By visualization and cluster analysis, the temporal transition of patient status and test items during state transitions characteristic of each anticancer drug were identified. Our framework surpasses existing methods in capturing interpretable latent space. It can be expected to enhance our comprehension of disease progression from EHRs, aiding treatment adjustments and prognostic determinations.Comment: 21 pages, 6 figure

Post vaccinal temporary sensorineural hearing loss

In our systematic research we identified four studies concerning the onset of neurological adverse events following vaccination and two excluding this association. A 33-year-old Italian man, belonging to the Italian Army was hospitalized because he suffered from vertigo, nausea and sudden right hearing loss not classified (NDD), that set in 24 h after the administration of tetanus-diphtheria and meningococcal vaccines. Some neurological events arising after vaccination are very difficult to treat. In our case, the functional recovery on low and medium frequencies was possible about 6 months after the morbid event