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Recent progress in communications technology has been very rapid. High-speed mobile Internet access and mobile devices have enabled the development of robust technologies such as machine translation, automated speech recognition, voice synthesis, and even speech-to-speech translation. Communication applications that support sign language recognition are also being introduced and upgraded. Nonetheless, people with speech, hearing, or mental impairment still require special communication assistance, especially for medical purposes; this makes their health and life dependent on other people. Automatic solutions for speech recognition or voice synthesis from the text are poor fits for communication in the medical domain because they are dependent on error-prone statistical models. Additionally, in emergency cases, rapid information exchange is essential. Systems dependent on manual text input are insufficient. Recently introduced systems for automatic sign language recognition are dependent on statistical models and image and gesture quality. Such systems remain in early development and are based mostly on minimal hand gestures unsuitable for medical purposes. Furthermore, Internet-dependent solutions cannot be used in most countries requiring humanitarian aid. We propose a high-speed, intuitive, Internet-free, voice-free, and text-free tool suited for emergency medical communication. Our solution is a pictogram-based communication application that provides easy communication means for individuals who are speech- or hearing-impaired, have mental health issues impairing communication, or non-natives who do not speak the local language. It provides support and clarification in communication with such people using intuitive icons and interactive symbols easy to find on a mobile device. Such pictogram-based communication can be quite effective and, ultimately, make some people’s lives happier, easier, and safer. We have developed a conceptual prototype of a patient-physician communicator on a smartwatch that can be used for local as well as remote communication.

DNA can provide vast amount of information about an organism. Geneticists with the use of Next-Generation Sequencing technology are able to analyze genes variations in search of pathogenic mutations in humans. The amount of data obtained from NGS can be huge, what makes it impossible to analyze without automatic support. A number of applications was created to assist NGS data study, but the results of their computations is a list of potential causal variants, which can be treated as a suggestion. For final diagnosis doctors’ experience and broad scientific knowledge is required. To support the process of identifying pathogenic variants we are developing a solution to extract valuable information for selected genetic variants from medical articles. In our article, we would like to present our work on extending existing variant prioritization methods of Exomiser and OmimExplorer applications with the knowledge extracted from articles available in PubMed database using semantic text analysis. The results were compared with Google Scholar search results. We evaluated our work with the data of nine anonymous patients. Such approach enables medical personnel to profit from the newest scientific knowledge during the diagnosis process.

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. The main machine translation evaluation metrics have also been used in analysis of the systems. A comparison and implementation of a real-time medical translator is the main focus of our experiments., Comment: machine translation, statistical machine translation, neural machine trasnlation, nlp, text processing, medical communication