Notes

Position associated with acetylation within doxorubicin-induced cardiotoxicity.
The use of different data formats complicates the standardization and exchange of valuable medical data. Moreover, a big part of medical data is stored as unstructured medical records that are complicated to process. In this work we solve the task of unstructured allergy anamnesis categorization according to categories provided by FHIR. We applied two stage classification model with manually labeled records. On the first stage the model filters records with information about allergies and on the second stage it categorizes each record. The model showed high performance. The development of this approach will ensure secondary use of data and interoperability.The demographic change is no longer a prognosis, but a reality seen in everyday life situations and requires mechanisms to make the public and private space elderly-adequate. These required mechanisms need to consider the varying aging process for each individual as well as adapt to the dynamic daily life of individuals characterized by spatial, temporal and activity variance. Developing assistance systems that are user-adaptive within dynamic environments is a challenging task. AI-based cyber-physical assistance systems enable such adaptive, flexible and individual assistance by processing acquired data from the physical environment using cyber resources and delivering intelligent assistance as well as interfaces to further medical services. This contribution discusses a flexible, reusable, and user-specific concept for AI-based assistance systems. Relying on distributed and heterogeneous data, the user's context is continuously modeled and reasoned over to infer actionable knowledge within a middleware between the data layer and the application layer. To demonstrate the applicability of the concept, the use case of intelligently supporting patients' medication adherence is shown.Human Activity Recognition (HAR) is becoming a significant issue in modern times and directly impact the field of mobile health. Therefore, it is essential the designing of systems which are capable of recognizing properly the activities conducted by the individuals. PHA-793887 In this work, we developed a system using the Internet of Things (IoT) and machine learning technologies in order to monitor and assist individuals in their daily life. We compared the data collected using a mobile application and a wearable device with built-in sensors (accelerometer and gyroscope) with the data of a publicly available dataset. By this way, we were able to validate our results and also investigate the functionality and applicability of the wearable device that we choose for the Human Activity Recognition problem. The classification results for the different types of activities presented using our dataset (99%) outperforms the results from the publicly database (97%).The paper compares two approaches to multi-step ahead glycaemia forecasting. While the direct approach uses a different model for each number of steps ahead, the iterative approach applies one one-step ahead model iteratively. Although it is well known that the iterative approach suffers from the error accumulation problem, there are no clear outcomes supporting a proper choice between those two methods. This paper provides such comparison for different ARX models and shows that the iterative approach outperformed the direct method for one-hour ahead (12-steps ahead) forecasting. Moreover, the classical linear ARX model outperformed more complex non-linear versions for training data covering one-month period.In this paper, we follow up on research dealing with body tracking and motor rehabilitation. We describe the current situation in telerehabilitation in the home environment. Existing solutions do not allow wide adoption due to hardware requirements and complicated setup. We come with the possibility of telerehabilitation using only laptop or mobile web camera. Together with physiotherapists, we have compiled a set of complex motor exercises to show that the system can be practically used.Specific predictive models for diabetes polyneuropathy based on screening methods, for example Nerve conduction studies (NCS, can reach up to AUC 65.8 - 84.7 % for the conditional diagnosis of DPN in primary care. Prediction methods that utilize data from personal health records deal with large non-specific datasets with different prediction methods. Li et al. utilized 30 independent variables, which allowed to implement a model with AUC = 0.8863 for a Multilayer perceptron (MLP). Linear regression (LR) based methods produced up to AUC = 0.8 %. This way, modern data mining and computational methods can be effectively adopted in clinical medicine to derive models that use patient-specific information to predict the development of diabetic polyneuropathy, however, there still is a space to improve the efficiency of the predictive models. The goal of this study is the implementation of machine learning methods for early risk identification of diabetes polyneuropathy based on structured electronic medical records. It was demonstrated that the machine learning methods allow to achieve up to 0.7982 precision, 0.8152 recall, 0.8064 f1-score, 0.8261 accuracy, and 0.8988 AUC using the neural network classifier.In this paper, we describe a strategy for the development of a genetic analysis comprehensive representation. The primary intention is to ensure the available utilization of genetic analysis results in clinical practice. The system is called Personnel Genetic Card (PGC), and it is developed in cooperation of CIIRC CTU in Prague and the Mediware company. Nowadays, genetic information is more and more part of medicine and life quality services (e.g. nutritional consulting). Therefore, there is necessary to bind genetic information with the clinical phenotype, such as drug metabolism or intolerance to various substances. We proposed a structured form of the record, where we utilize the LOINC® standard to identify genetic test parameters, and several terminology databases for representing specific genetic information (e.g. HGNC, NCBI RefSeq, NCBI dbNSP, HGVS). Further, there are also several knowledge databases (PharmGKB, SNPedia, ClinVar) that collect interpretation for genetic analysis results. In the results of this paper, we describe our idea in the structure and process perspective.
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