42 research outputs found

    Clinical Text Mining: Secondary Use of Electronic Patient Records

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    This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields

    Assessing mortality prediction through different representation models based on concepts extracted from clinical notes

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    Recent years have seen particular interest in using electronic medical records (EMRs) for secondary purposes to enhance the quality and safety of healthcare delivery. EMRs tend to contain large amounts of valuable clinical notes. Learning of embedding is a method for converting notes into a format that makes them comparable. Transformer-based representation models have recently made a great leap forward. These models are pre-trained on large online datasets to understand natural language texts effectively. The quality of a learning embedding is influenced by how clinical notes are used as input to representation models. A clinical note has several sections with different levels of information value. It is also common for healthcare providers to use different expressions for the same concept. Existing methods use clinical notes directly or with an initial preprocessing as input to representation models. However, to learn a good embedding, we identified the most essential clinical notes section. We then mapped the extracted concepts from selected sections to the standard names in the Unified Medical Language System (UMLS). We used the standard phrases corresponding to the unique concepts as input for clinical models. We performed experiments to measure the usefulness of the learned embedding vectors in the task of hospital mortality prediction on a subset of the publicly available Medical Information Mart for Intensive Care (MIMIC-III) dataset. According to the experiments, clinical transformer-based representation models produced better results with getting input generated by standard names of extracted unique concepts compared to other input formats. The best-performing models were BioBERT, PubMedBERT, and UmlsBERT, respectively

    Completeness of information in electronic compared with paper-based patients’ records in a maternity setting in Dakar, Senegal

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    Background: Evaluate the consistency of information in paper-based records when registered in parallel with an electronic medical record.Methods: The study was performed at PMSHC in Dakar Senegal. From the end of year 2016, patients’ files were recorded on both paper-based and electronically. Additionally, previous records were electronically registered. To investigate the completeness of records before and after the electronic recording system has been implemented, information about some maternal and fetal/neonatal characteristics were assessed. When the variable was recorded, the system returned 1, unrecorded variables were coded as 0. We then calculated, for each variable, the unrecorded rate before 2017 and after that date. The study period extended from 2011 to June 2019, a nearly ten-year period. Data were extracted from E-perinatal to MS excel 2019 then SPSS 25 software. Frequencies of unrecorded variables were compared with chi-squared test at a level of significance of 5%.Results: A total of 48,270 unique patients’ records were identified during the eight-year period.Β  Among the study population, data for patients’ age, address and parity were available most of the time before and after 2017 (0.5% missing data versus 0.3% for age and 2.6% versus 1.3% for home address and from 0.3% to 0.0% for parity). However, phone number, maternal weight, maternal height, last menstrual period and blood group were found to be missing up to 96% before 2017. From 2017, these rates experienced a sudden decrease at a significant level: from 82.4% to 27.8% for phone number, from 96% to 56.3% for maternal weight and from 60.1% to 21.3% for blood group. Regarding newborns’ data, it was found that fetal height, head circumference and chest circumference were missing up to just under 25% before 2017. After that date, their completeness improved and flattened under 5%.Conclusions: Structured and computerized files reduce missing data. There is an urgent need the Ministry of health provides hospitals and health care providers with guidelines that describes the standardized information that should be gathered and shared in health and care records

    Perlindungan Hak Pasien Sebagai Konsumen Untuk Mendapatkan Isi Rekam Medis Dalam Pelayanan Kesehatan

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    Pemberi pelayanan kesehatan memiliki sejumlah kewajiban terhadap pasien. Salah satunya adalah menyelenggarakan rekam medis dalam pelayanan kesehatan. Kewajiban tersebut timbul sebagai bentuk pemenuhan hak pasien untuk mendapatkan isi rekam medis. Adanya unsur ekonomi dalam pelayanan kesehatan menyebabkan hubungan dalam pelayanan kesehatan dapat dipandang sebagai bentuk hubungan transaksi komersial. Dengan demikian, hak pasien terkait isi rekam medis tersebut seharusnya dilindungi oleh ketentuan dalam Undang-Undang Perlindungan Konsumen. Tujuan: Memahami dasar hukum untuk melindungi hak pasien sebagai konsumen untuk mendapatkan isi rekam medis menurut perspektif Undang-Undang Perlindungan Konsumen merupakan tujuan dari penelitian ini. Metode: Peneltian ini melakukan pendekatan hukum normatif melalui analisis yang bersifat deskriptif. Data sekunder yang digunakan dalam penelitian ini diperoleh melalui studi kepustakaan. Hasil: Pasien merupakan konsumen dalam pelayanan kesehatan. Pelanggaran terhadap hak pasien untuk mendapatkan isi rekam medis merupakan bentuk pelanggaran hak konsumen dalam pelayanan kesehatan. Berdasarkan Undang-Undang Perlindungan Konsumen, sengketa yang ditimbulkan karena pelanggaran hak tersebut dapat dilakukan melalui pemberian ganti rugi kepada pasien oleh pelaku usaha, pengajuan gugatan oleh pasien yang dirugikan, dan bahkan tuntutan pidana terhadap pelaku usaha

    AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors

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    Cryptocurrencies have become a popular and widely researched topic of interest in recent years for investors and scholars. In order to make informed investment decisions, it is essential to comprehend the factors that impact cryptocurrency prices and to identify risky cryptocurrencies. This paper focuses on analyzing historical data and using artificial intelligence algorithms on on-chain parameters to identify the factors affecting a cryptocurrency's price and to find risky cryptocurrencies. We conducted an analysis of historical cryptocurrencies' on-chain data and measured the correlation between the price and other parameters. In addition, we used clustering and classification in order to get a better understanding of a cryptocurrency and classify it as risky or not. The analysis revealed that a significant proportion of cryptocurrencies (39%) disappeared from the market, while only a small fraction (10%) survived for more than 1000 days. Our analysis revealed a significant negative correlation between cryptocurrency price and maximum and total supply, as well as a weak positive correlation between price and 24-hour trading volume. Moreover, we clustered cryptocurrencies into five distinct groups using their on-chain parameters, which provides investors with a more comprehensive understanding of a cryptocurrency when compared to those clustered with it. Finally, by implementing multiple classifiers to predict whether a cryptocurrency is risky or not, we obtained the best f1-score of 76% using K-Nearest Neighbor.Comment: 8 pages, 5 figures, 7 tables. Accepted for publication in The Fifth International Conference on Blockchain Computing and Applications (BCCA 2023

    Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial

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    Background: Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. Objective: The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. Methods: The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. Results: We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence–based CAC innovations to improve coding practice. Expected results to be published summer 2024. Conclusions: The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. Trial Registration: clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT0628686

    ИспользованиС Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΈΡ… ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΠΈΠ³Ρ€ ΠΏΡ€ΠΈ принятии ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ ΠΎΡ€ΠΈΠ΅Π½Ρ‚ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΏΡ€ΠΈ госпитализации Π² условиях ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ

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    The problems of organizing medical care in the context of the COVID-19 pandemic, associated with the uncertainty and limitedness of various resources, led to the need to improve decision-making systems for hospitalization of patients. Situational management can improve the decision-making process to fit the current situation better. At the same time, it becomes important to take into account the influence of psychological factors on decisions made during hospitalization. The paper proposes the use of coalition games for situational management during hospitalization of patients. The players and members of the coalition are hospitals, ambulance teams, patients and computed tomography centers. The goal of the game is to form a coalition of participants that provides the maximum benefit in terms of time and cost of hospitalization at the time of decision making. The general scheme of hospitalization, the main sources of information about the situation, the formulation and formalization of the problem are considered. An experiment was carried out in which the formation of a coalition during hospitalization was tested based on data obtained from analyzing the dynamics of the COVID-19 pandemic. Due to the small amount of data and the lack of approved models of the situation development, when carrying out the calculation, some of the parameters were estimated using heuristic models of the development of the situation, based on the analysis of information from open sources of information. The experiment result contains a set of coalitions that provide the maximum benefit under the specified constraints. At the same time, the calculation time of the coalition game allows using the proposed model of decision-making support during hospitalization in the dispatch service of ambulance stations.ΠŸΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ ΠΎΡ€Π³Π°Π½ΠΈΠ·Π°Ρ†ΠΈΠΈ мСдицинской ΠΏΠΎΠΌΠΎΡ‰ΠΈ Π² условиях ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ COVID-19, связанныС с Π½Π΅ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡ‚ΡŒΡŽ ΠΈ ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡Π΅Π½Π½ΠΎΡΡ‚ΡŒΡŽ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… рСсурсов, ΠΏΡ€ΠΈΠ²Π΅Π»ΠΈ ΠΊ нСобходимости ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ систСм принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΏΡ€ΠΈ госпитализации ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ². Π‘ ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ситуационного управлСния ΠΌΠΎΠΆΠ½ΠΎ ΡƒΠ»ΡƒΡ‡ΡˆΠΈΡ‚ΡŒ процСсс принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ, Ρ‡Ρ‚ΠΎΠ±Ρ‹ ΠΎΠ½ Π»ΡƒΡ‡ΡˆΠ΅ соотвСтствовал Ρ‚Π΅ΠΊΡƒΡ‰Π΅ΠΉ ситуации. ΠŸΡ€ΠΈ этом Π²Π°ΠΆΠ½Ρ‹ΠΌ становится ΡƒΡ‡Π΅Ρ‚ влияния психологичСских Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π½Π° Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ, ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Π΅ΠΌΡ‹Π΅ ΠΏΡ€ΠΈ госпитализации. Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ прСдлагаСтся использованиС ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΠΈΠ³Ρ€ для ситуационного управлСния ΠΏΡ€ΠΈ госпитализации Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ…. Π˜Π³Ρ€ΠΎΠΊΠ°ΠΌΠΈ ΠΈ участниками ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΈ ΡΠ²Π»ΡΡŽΡ‚ΡΡ госпитали, Π±Ρ€ΠΈΠ³Π°Π΄Ρ‹ скорой ΠΏΠΎΠΌΠΎΡ‰ΠΈ, ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Ρ‹ ΠΈ Ρ†Π΅Π½Ρ‚Ρ€Ρ‹ ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠΉ Ρ‚ΠΎΠΌΠΎΠ³Ρ€Π°Ρ„ΠΈΠΈ. ЦСль ΠΈΠ³Ρ€Ρ‹ - ΡΡ„ΠΎΡ€ΠΌΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΡŽ участников, ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡ΠΈΠ²Π°ΡŽΡ‰ΡƒΡŽ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½ΡƒΡŽ Π²Ρ‹Π³ΠΎΠ΄Ρƒ ΠΏΠΎ Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ ΠΈ стоимости госпитализации Π² ΠΌΠΎΠΌΠ΅Π½Ρ‚ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ. РассмотрСны общая схСма госпитализации, основныС источники ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΎ ситуации, постановка ΠΈ формализация ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½ экспСримСнт, Π² ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠΌ ΠΏΡ€ΠΎΠ²Π΅Ρ€ΡΠ»ΠΎΡΡŒ Ρ„ΠΎΡ€ΠΌΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΈ Π²ΠΎ врСмя госпитализации Π½Π° основС Π΄Π°Π½Π½Ρ‹Ρ…, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… ΠΏΡ€ΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ COVID-19. Π’ связи с ΠΌΠ°Π»Ρ‹ΠΌ объСмом Π΄Π°Π½Π½Ρ‹Ρ… ΠΈ отсутствиСм Π°ΠΏΡ€ΠΎΠ±ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ развития ситуации ΠΏΡ€ΠΈ ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ расчСта Ρ‡Π°ΡΡ‚ΡŒ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² Π±Ρ‹Π»Π° ΠΎΡ†Π΅Π½Π΅Π½Π° с использованиСм эвристичСских ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ развития ситуации, основанных Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈΠ· ΠΎΡ‚ΠΊΡ€Ρ‹Ρ‚Ρ‹Ρ… источников ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ экспСримСнта содСрТит Π½Π°Π±ΠΎΡ€ ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΉ, ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡ΠΈΠ²Π°ΡŽΡ‰ΠΈΡ… ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½ΡƒΡŽ Π²Ρ‹Π³ΠΎΠ΄Ρƒ, ΠΏΡ€ΠΈ ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… ограничСниях. ΠŸΡ€ΠΈ этом врСмя расчСта ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΎΠ½Π½ΠΎΠΉ ΠΈΠ³Ρ€Ρ‹ позволяСт ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡƒΡŽ модСль ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΏΡ€ΠΈ госпитализации Π² диспСтчСрской слуТбС станций скорой ΠΏΠΎΠΌΠΎΡ‰ΠΈ

    ИспользованиС Π½Π΅Ρ‡Π΅Ρ‚ΠΊΠΈΡ… ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΠΈΠ³Ρ€ ΠΏΡ€ΠΈ принятии ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ ΠΎΡ€ΠΈΠ΅Π½Ρ‚ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΏΡ€ΠΈ госпитализации Π² условиях ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ

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    ΠŸΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ ΠΎΡ€Π³Π°Π½ΠΈΠ·Π°Ρ†ΠΈΠΈ мСдицинской ΠΏΠΎΠΌΠΎΡ‰ΠΈ Π² условиях ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ COVID-19, связанныС с Π½Π΅ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡ‚ΡŒΡŽ ΠΈ ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡Π΅Π½Π½ΠΎΡΡ‚ΡŒΡŽ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… рСсурсов, ΠΏΡ€ΠΈΠ²Π΅Π»ΠΈ ΠΊ нСобходимости ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ систСм принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΏΡ€ΠΈ госпитализации ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ². Π‘ ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ситуационного управлСния ΠΌΠΎΠΆΠ½ΠΎ ΡƒΠ»ΡƒΡ‡ΡˆΠΈΡ‚ΡŒ процСсс принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ, Ρ‡Ρ‚ΠΎΠ±Ρ‹ ΠΎΠ½ Π»ΡƒΡ‡ΡˆΠ΅ соотвСтствовал Ρ‚Π΅ΠΊΡƒΡ‰Π΅ΠΉ ситуации. ΠŸΡ€ΠΈ этом Π²Π°ΠΆΠ½Ρ‹ΠΌ становится ΡƒΡ‡Π΅Ρ‚ влияния психологичСских Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² Π½Π° Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ, ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Π΅ΠΌΡ‹Π΅ ΠΏΡ€ΠΈ госпитализации. Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ прСдлагаСтся использованиС ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΠΈΠ³Ρ€ для ситуационного управлСния ΠΏΡ€ΠΈ госпитализации Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ…. Π˜Π³Ρ€ΠΎΠΊΠ°ΠΌΠΈ ΠΈ участниками ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΈ ΡΠ²Π»ΡΡŽΡ‚ΡΡ госпитали, Π±Ρ€ΠΈΠ³Π°Π΄Ρ‹ скорой ΠΏΠΎΠΌΠΎΡ‰ΠΈ, ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Ρ‹ ΠΈ Ρ†Π΅Π½Ρ‚Ρ€Ρ‹ ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½ΠΎΠΉ Ρ‚ΠΎΠΌΠΎΠ³Ρ€Π°Ρ„ΠΈΠΈ. ЦСль ΠΈΠ³Ρ€Ρ‹ - ΡΡ„ΠΎΡ€ΠΌΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΡŽ участников, ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡ΠΈΠ²Π°ΡŽΡ‰ΡƒΡŽ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½ΡƒΡŽ Π²Ρ‹Π³ΠΎΠ΄Ρƒ ΠΏΠΎ Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ ΠΈ стоимости госпитализации Π² ΠΌΠΎΠΌΠ΅Π½Ρ‚ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ. РассмотрСны общая схСма госпитализации, основныС источники ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΎ ситуации, постановка ΠΈ формализация ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½ экспСримСнт, Π² ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠΌ ΠΏΡ€ΠΎΠ²Π΅Ρ€ΡΠ»ΠΎΡΡŒ Ρ„ΠΎΡ€ΠΌΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΈ Π²ΠΎ врСмя госпитализации Π½Π° основС Π΄Π°Π½Π½Ρ‹Ρ…, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… ΠΏΡ€ΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ COVID-19. Π’ связи с ΠΌΠ°Π»Ρ‹ΠΌ объСмом Π΄Π°Π½Π½Ρ‹Ρ… ΠΈ отсутствиСм Π°ΠΏΡ€ΠΎΠ±ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ развития ситуации ΠΏΡ€ΠΈ ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ расчСта Ρ‡Π°ΡΡ‚ΡŒ ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² Π±Ρ‹Π»Π° ΠΎΡ†Π΅Π½Π΅Π½Π° с использованиСм эвристичСских ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ развития ситуации, основанных Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈΠ· ΠΎΡ‚ΠΊΡ€Ρ‹Ρ‚Ρ‹Ρ… источников ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ экспСримСнта содСрТит Π½Π°Π±ΠΎΡ€ ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΉ, ΠΎΠ±Π΅ΡΠΏΠ΅Ρ‡ΠΈΠ²Π°ΡŽΡ‰ΠΈΡ… ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½ΡƒΡŽ Π²Ρ‹Π³ΠΎΠ΄Ρƒ, ΠΏΡ€ΠΈ ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… ограничСниях. ΠŸΡ€ΠΈ этом врСмя расчСта ΠΊΠΎΠ°Π»ΠΈΡ†ΠΈΠΎΠ½Π½ΠΎΠΉ ΠΈΠ³Ρ€Ρ‹ позволяСт ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡƒΡŽ модСль ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ принятия Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΏΡ€ΠΈ госпитализации Π² диспСтчСрской слуТбС станций скорой ΠΏΠΎΠΌΠΎΡ‰ΠΈ

    Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics

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    Clinicians and other analysts working with healthcare data are in need for better support to cope with large and complex data. While an increasing number of visual analytics environments integrates explicit domain knowledge as a means to deliver a precise representation of the available data, theoretical work so far has focused on the role of knowledge in the visual analytics process. There has been little discussion about how such explicit domain knowledge can be structured in a generalized framework. This paper collects desiderata for such a structural framework, proposes how to address these desiderata based on the model of linked data, and demonstrates the applicability in a visual analytics environment for physiotherapy.Comment: 8 pages, 5 figure

    Real-time head movement tracking through earables in moving vehicles

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    Abstract. The Internet of Things is enabling innovations in the automotive industry by expanding the capabilities of vehicles by connecting them with the cloud. One important application domain is traffic safety, which can benefit from monitoring the driver’s condition to see if they are capable of safely handling the vehicle. By detecting drowsiness, inattentiveness, and distraction of the driver it is possible to react before accidents happen. This thesis explores how accelerometer and gyroscope data collected using earables can be used to classify the orientation of the driver’s head in a moving vehicle. It is found that machine learning algorithms such as Random Forest and K-Nearest Neighbor can be used to reach fairly accurate classifications even without applying any noise reduction to the signal data. Data cleaning and transformation approaches are studied to see how the models could be improved further. This study paves the way for the development of driver monitoring systems capable of reacting to anomalous driving behavior before traffic accidents can happen
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