42 research outputs found
Clinical Text Mining: Secondary Use of Electronic Patient Records
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 bookβs 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
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 patientsβ records in a maternity setting in Dakar, Senegal
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, patientsβ 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 patientsβ records were identified during the eight-year period.Β Among the study population, data for patientsβ 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 newbornsβ 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
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
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
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
intelligenceβbased 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
ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π΅ΡΠ΅ΡΠΊΠΈΡ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΎΠ½Π½ΡΡ ΠΈΠ³Ρ ΠΏΡΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ
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.ΠΡΠΎΠ±Π»Π΅ΠΌΡ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ COVID-19, ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡΡ ΠΈ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΠΎΡΡΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ², ΠΏΡΠΈΠ²Π΅Π»ΠΈ ΠΊ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ². Π‘ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΈΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΌΠΎΠΆΠ½ΠΎ ΡΠ»ΡΡΡΠΈΡΡ ΠΏΡΠΎΡΠ΅ΡΡ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ, ΡΡΠΎΠ±Ρ ΠΎΠ½ Π»ΡΡΡΠ΅ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΎΠ²Π°Π» ΡΠ΅ΠΊΡΡΠ΅ΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ. ΠΡΠΈ ΡΡΠΎΠΌ Π²Π°ΠΆΠ½ΡΠΌ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ ΡΡΠ΅Ρ Π²Π»ΠΈΡΠ½ΠΈΡ ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π½Π° ΡΠ΅ΡΠ΅Π½ΠΈΡ, ΠΏΡΠΈΠ½ΠΈΠΌΠ°Π΅ΠΌΡΠ΅ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ. Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ³Ρ Π΄Π»Ρ ΡΠΈΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π±ΠΎΠ»ΡΠ½ΡΡ
. ΠΠ³ΡΠΎΠΊΠ°ΠΌΠΈ ΠΈ ΡΡΠ°ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΈ ΡΠ²Π»ΡΡΡΡΡ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈ, Π±ΡΠΈΠ³Π°Π΄Ρ ΡΠΊΠΎΡΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ, ΠΏΠ°ΡΠΈΠ΅Π½ΡΡ ΠΈ ΡΠ΅Π½ΡΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ. Π¦Π΅Π»Ρ ΠΈΠ³ΡΡ - ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ ΠΊΠΎΠ°Π»ΠΈΡΠΈΡ ΡΡΠ°ΡΡΠ½ΠΈΠΊΠΎΠ², ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΡΡ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ Π²ΡΠ³ΠΎΠ΄Ρ ΠΏΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΈ ΡΡΠΎΠΈΠΌΠΎΡΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² ΠΌΠΎΠΌΠ΅Π½Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΎΠ±ΡΠ°Ρ ΡΡ
Π΅ΠΌΠ° Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ, ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ ΡΠΈΡΡΠ°ΡΠΈΠΈ, ΠΏΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠ° ΠΈ ΡΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½Ρ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ ΠΏΡΠΎΠ²Π΅ΡΡΠ»ΠΎΡΡ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΈ Π²ΠΎ Π²ΡΠ΅ΠΌΡ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΡΡ
, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΠΏΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ COVID-19. Π ΡΠ²ΡΠ·ΠΈ Ρ ΠΌΠ°Π»ΡΠΌ ΠΎΠ±ΡΠ΅ΠΌΠΎΠΌ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ΠΌ Π°ΠΏΡΠΎΠ±ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠ°ΡΠΈΠΈ ΠΏΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ°ΡΡΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π±ΡΠ»Π° ΠΎΡΠ΅Π½Π΅Π½Π° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠ²ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠ°ΡΠΈΠΈ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· ΠΎΡΠΊΡΡΡΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°Ρ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ Π½Π°Π±ΠΎΡ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΉ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΡ
ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ Π²ΡΠ³ΠΎΠ΄Ρ, ΠΏΡΠΈ ΡΠΊΠ°Π·Π°Π½Π½ΡΡ
ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡΡ
. ΠΡΠΈ ΡΡΠΎΠΌ Π²ΡΠ΅ΠΌΡ ΡΠ°ΡΡΠ΅ΡΠ° ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΈΠ³ΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² Π΄ΠΈΡΠΏΠ΅ΡΡΠ΅ΡΡΠΊΠΎΠΉ ΡΠ»ΡΠΆΠ±Π΅ ΡΡΠ°Π½ΡΠΈΠΉ ΡΠΊΠΎΡΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ
ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π΅ΡΠ΅ΡΠΊΠΈΡ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΎΠ½Π½ΡΡ ΠΈΠ³Ρ ΠΏΡΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΠΈ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ
ΠΡΠΎΠ±Π»Π΅ΠΌΡ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ COVID-19, ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ Π½Π΅ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΡΡΡΡ ΠΈ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΠΎΡΡΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ΅ΡΡΡΡΠΎΠ², ΠΏΡΠΈΠ²Π΅Π»ΠΈ ΠΊ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΠΈ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ². Π‘ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΈΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΌΠΎΠΆΠ½ΠΎ ΡΠ»ΡΡΡΠΈΡΡ ΠΏΡΠΎΡΠ΅ΡΡ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ, ΡΡΠΎΠ±Ρ ΠΎΠ½ Π»ΡΡΡΠ΅ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΎΠ²Π°Π» ΡΠ΅ΠΊΡΡΠ΅ΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ. ΠΡΠΈ ΡΡΠΎΠΌ Π²Π°ΠΆΠ½ΡΠΌ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ ΡΡΠ΅Ρ Π²Π»ΠΈΡΠ½ΠΈΡ ΠΏΡΠΈΡ
ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² Π½Π° ΡΠ΅ΡΠ΅Π½ΠΈΡ, ΠΏΡΠΈΠ½ΠΈΠΌΠ°Π΅ΠΌΡΠ΅ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ. Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΎΠ½Π½ΡΡ
ΠΈΠ³Ρ Π΄Π»Ρ ΡΠΈΡΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π±ΠΎΠ»ΡΠ½ΡΡ
. ΠΠ³ΡΠΎΠΊΠ°ΠΌΠΈ ΠΈ ΡΡΠ°ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΈ ΡΠ²Π»ΡΡΡΡΡ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈ, Π±ΡΠΈΠ³Π°Π΄Ρ ΡΠΊΠΎΡΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ, ΠΏΠ°ΡΠΈΠ΅Π½ΡΡ ΠΈ ΡΠ΅Π½ΡΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ. Π¦Π΅Π»Ρ ΠΈΠ³ΡΡ - ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ ΠΊΠΎΠ°Π»ΠΈΡΠΈΡ ΡΡΠ°ΡΡΠ½ΠΈΠΊΠΎΠ², ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΡΡ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ Π²ΡΠ³ΠΎΠ΄Ρ ΠΏΠΎ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΈ ΡΡΠΎΠΈΠΌΠΎΡΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² ΠΌΠΎΠΌΠ΅Π½Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΠΎΠ±ΡΠ°Ρ ΡΡ
Π΅ΠΌΠ° Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ, ΠΎΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ ΡΠΈΡΡΠ°ΡΠΈΠΈ, ΠΏΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠ° ΠΈ ΡΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½Ρ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ ΠΏΡΠΎΠ²Π΅ΡΡΠ»ΠΎΡΡ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΈ Π²ΠΎ Π²ΡΠ΅ΠΌΡ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΡΡ
, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΠΏΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ COVID-19. Π ΡΠ²ΡΠ·ΠΈ Ρ ΠΌΠ°Π»ΡΠΌ ΠΎΠ±ΡΠ΅ΠΌΠΎΠΌ Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ΠΌ Π°ΠΏΡΠΎΠ±ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠ°ΡΠΈΠΈ ΠΏΡΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ°ΡΡΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² Π±ΡΠ»Π° ΠΎΡΠ΅Π½Π΅Π½Π° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠ²ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΡΡΠ°ΡΠΈΠΈ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· ΠΎΡΠΊΡΡΡΡΡ
ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°Ρ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ° ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ Π½Π°Π±ΠΎΡ ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΉ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΡ
ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ Π²ΡΠ³ΠΎΠ΄Ρ, ΠΏΡΠΈ ΡΠΊΠ°Π·Π°Π½Π½ΡΡ
ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡΡ
. ΠΡΠΈ ΡΡΠΎΠΌ Π²ΡΠ΅ΠΌΡ ΡΠ°ΡΡΠ΅ΡΠ° ΠΊΠΎΠ°Π»ΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΈΠ³ΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² Π΄ΠΈΡΠΏΠ΅ΡΡΠ΅ΡΡΠΊΠΎΠΉ ΡΠ»ΡΠΆΠ±Π΅ ΡΡΠ°Π½ΡΠΈΠΉ ΡΠΊΠΎΡΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ
Towards a Structural Framework for Explicit Domain Knowledge in Visual Analytics
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
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 driverβs 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 driverβs 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