40 research outputs found

    ΠŸΠ ΠžΠ‘Π›Π•ΠœΠ« Π”Π•Π’Π‘ΠšΠžΠ™ Π˜ΠΠ’ΠΠ›Π˜Π”ΠΠžΠ‘Π’Π˜ Π’ Π‘ΠžΠ’Π Π•ΠœΠ•ΠΠΠžΠ™ РОББИИ

    Get PDF
    Β Creation of system of early prophylaxis of children disability and support of the families bringing up disabled children and children with limited opportunities are among the main priorities of the Russian Federation state social policy. There are a number of problems requiring immediate solutions. Dynamics of children’s disability in our country is characterized by process stagnation. The age and gender structure of children’s disability practically doesn’t change. The analysis of its nosological structure shows that alienations and disorders of behavior, illness of a nervous system and congenital anomalies of development steadily occupy more than 60% among the illnesses which caused disability of children of all age groups. There was a decrease in the prevalence of total disability in most classes of diseases, such as injuries, diseases of the genitourinary system, respiratory system, musculoskeletal system, digestive system and growth of disability caused by neoplasms and diseases of the endocrine system. The underestimation of children’s disability bound to various reasons is supposed: social motivation of a family, complexity of legal veneering, strict requirements of service of medico-social examination, insufficient medical experts awareness on criteria of disability. Among disability formations risk factors the most discussed are the achievements of perinatology leading to improvement of nursing of prematurely born and small newborns, and wide uses of auxiliary genesial technologies. An important part of all preventive measures aimed at reducing the genetic load of population is prenatal and preimplantation diagnosis. It seems appropriate to extend the screening to congenital and hereditary metabolic diseases in neonatal period, including the most common nosological forms of infrequent illnesses. In solving problems of childhood disability prevention a priority should be given to development of services of family planning; improving antenatal and perinatal care; preventive work with healthy but having deviations in development children; development of medical genetic services; implementation of programs of different types of pathology screening.Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅ систСмы Ρ€Π°Π½Π½Π΅ΠΉ ΠΏΡ€ΠΎΡ„ΠΈΠ»Π°ΠΊΡ‚ΠΈΠΊΠΈ инвалидности Ρƒ Π΄Π΅Ρ‚Π΅ΠΉ ΠΈ ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠ° сСмСй, Π²ΠΎΡΠΏΠΈΡ‚Ρ‹Π²Π°ΡŽΡ‰ΠΈΡ… Π΄Π΅Ρ‚Π΅ΠΉ-ΠΈΠ½Π²Π°Π»ΠΈΠ΄ΠΎΠ² ΠΈ Π΄Π΅Ρ‚Π΅ΠΉ с ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡Π΅Π½Π½Ρ‹ΠΌΠΈ возмоТностями Π·Π΄ΠΎΡ€ΠΎΠ²ΡŒΡ, входят Π² число основных ΠΏΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚ΠΎΠ² государствСнной ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡ‚ΠΈΠΊΠΈ Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ. БущСствуСт ряд ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌ, Ρ‚Ρ€Π΅Π±ΡƒΡŽΡ‰ΠΈΡ… Π½Π΅Π·Π°ΠΌΠ΅Π΄Π»ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ. Π£Ρ€ΠΎΠ²Π΅Π½ΡŒ дСтской инвалидности Π² нашСй странС характСризуСтся стагнациСй процСсса. Возрастная ΠΈ гСндСрная структура дСтской инвалидности практичСски Π½Π΅ мСняСтся. Анализ Π΅Π΅ нозологичСской структуры ΠΏΠΎΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚, Ρ‡Ρ‚ΠΎ психичСскиС расстройства ΠΈ расстройства повСдСния, Π±ΠΎΠ»Π΅Π·Π½ΠΈ Π½Π΅Ρ€Π²Π½ΠΎΠΉ систСмы ΠΈ Π²Ρ€ΠΎΠΆΠ΄Π΅Π½Π½Ρ‹Π΅ Π°Π½ΠΎΠΌΠ°Π»ΠΈΠΈ развития ΡΡ‚Π°Π±ΠΈΠ»ΡŒΠ½ΠΎ ΡΠΎΡΡ‚Π°Π²Π»ΡΡŽΡ‚ Π±ΠΎΠ»Π΅Π΅ 60% срСди Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ, ΠΎΠ±ΡƒΡΠ»ΠΎΠ²ΠΈΠ²ΡˆΠΈΡ… ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡ‚ΡŒ Π΄Π΅Ρ‚Π΅ΠΉ всСх возрастных Π³Ρ€ΡƒΠΏΠΏ. ΠŸΡ€ΠΎΠΈΠ·ΠΎΡˆΠ»ΠΎ сниТСниС распространСнности ΠΎΠ±Ρ‰Π΅ΠΉ инвалидности ΠΏΠΎ Π±ΠΎΠ»ΡŒΡˆΠΈΠ½ΡΡ‚Π²Ρƒ классов Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ (ΠΏΡ€ΠΈ Ρ‚Ρ€Π°Π²ΠΌΠ°Ρ…, заболСваниях ΠΌΠΎΡ‡Π΅ΠΏΠΎΠ»ΠΎΠ²ΠΎΠΉ систСмы, ΠΎΡ€Π³Π°Π½ΠΎΠ² дыхания, костно-ΠΌΡ‹ΡˆΠ΅Ρ‡Π½ΠΎΠΉ систСмы, ΠΎΡ€Π³Π°Π½ΠΎΠ² пищСварСния) ΠΈ рост инвалидности, обусловлСнной новообразованиями ΠΈ болСзнями эндокринной систСмы. ΠŸΡ€Π΅Π΄ΠΏΠΎΠ»Π°Π³Π°Π΅Ρ‚ΡΡ Π½Π΅Π΄ΠΎΡƒΡ‡Π΅Ρ‚ дСтской инвалидности, связанный с Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹ΠΌΠΈ ΠΏΡ€ΠΈΡ‡ΠΈΠ½Π°ΠΌΠΈ, Π° ΠΈΠΌΠ΅Π½Π½ΠΎ с ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎΠΉ ΠΌΠΎΡ‚ΠΈΠ²ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΡΡ‚ΡŒΡŽ сСмьи, слоТностями ΡŽΡ€ΠΈΠ΄ΠΈΡ‡Π΅ΡΠΊΠΎΠ³ΠΎ оформлСния, ТСсткими трСбованиями слуТбы ΠΌΠ΅Π΄ΠΈΠΊΠΎ-ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎΠΉ экспСртизы, нСдостаточной ΠΎΡΠ²Π΅Π΄ΠΎΠΌΠ»Π΅Π½Π½ΠΎΡΡ‚ΡŒΡŽ ΠΎ критСриях инвалидности мСдицинских спСциалистов. Π‘Ρ€Π΅Π΄ΠΈ Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² риска формирования инвалидности Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ обсуТдаСмыми ΡΠ²Π»ΡΡŽΡ‚ΡΡ достиТСния ΠΏΠ΅Ρ€ΠΈΠ½Π°Ρ‚ΠΎΠ»ΠΎΠ³ΠΈΠΈ, приводящиС ΠΊ ΡƒΠ»ΡƒΡ‡ΡˆΠ΅Π½ΠΈΡŽ выхаТивания Π½Π΅Π΄ΠΎΠ½ΠΎΡˆΠ΅Π½Π½Ρ‹Ρ… ΠΈ маловСсных Π½ΠΎΠ²ΠΎΡ€ΠΎΠΆΠ΄Π΅Π½Π½Ρ‹Ρ…, ΠΈ ΡˆΠΈΡ€ΠΎΠΊΠΎΠ΅ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠΌΠΎΠ³Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… Ρ€Π΅ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΈΠ²Π½Ρ‹Ρ… Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ. Π’Π°ΠΆΠ½ΠΎΠΉ Ρ‡Π°ΡΡ‚ΡŒΡŽ всСх профилактичСских мСроприятий, Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½Π½Ρ‹Ρ… Π½Π° сниТСниС гСнСтичСского Π³Ρ€ΡƒΠ·Π° популяции, являСтся ΠΏΡ€Π΅Π½Π°Ρ‚Π°Π»ΡŒΠ½Π°Ρ ΠΈ прСимплантационная диагностика. ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»ΡΠ΅Ρ‚ΡΡ цСлСсообразным Ρ€Π°ΡΡˆΠΈΡ€Π΅Π½ΠΈΠ΅ скрининга Π½Π° Π²Ρ€ΠΎΠΆΠ΄Π΅Π½Π½Ρ‹Π΅ ΠΈ наслСдствСнныС Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΎΠ±ΠΌΠ΅Π½Π° Π² Π½Π΅ΠΎΠ½Π°Ρ‚Π°Π»ΡŒΠ½ΠΎΠΌ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π΅, Π²ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅ Π² Π½Π΅Π³ΠΎ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ распространСнных нозологичСских Ρ„ΠΎΡ€ΠΌ Ρ€Π΅Π΄ΠΊΠΈΡ… Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ. Π’ Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΈ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌ ΠΏΡ€ΠΎΡ„ΠΈΠ»Π°ΠΊΡ‚ΠΈΠΊΠΈ дСтской инвалидности слСдуСт ΠΎΡ‚Π΄Π°Π²Π°Ρ‚ΡŒ ΠΏΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚ Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΡŽ слуТб планирования дСтороТдСния, ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡŽ Π°Π½Ρ‚Π΅Π½Π°Ρ‚Π°Π»ΡŒΠ½ΠΎΠΉ ΠΈ ΠΏΠ΅Ρ€ΠΈΠ½Π°Ρ‚Π°Π»ΡŒΠ½ΠΎΠΉ ΠΏΠΎΠΌΠΎΡ‰ΠΈ, профилактичСской Ρ€Π°Π±ΠΎΡ‚Π΅ со Π·Π΄ΠΎΡ€ΠΎΠ²Ρ‹ΠΌΠΈ Π΄Π΅Ρ‚ΡŒΠΌΠΈ, Π½ΠΎ ΠΈΠΌΠ΅ΡŽΡ‰ΠΈΠΌΠΈ отклонСния Π² Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΠΈ, Π²Π½Π΅Π΄Ρ€Π΅Π½ΠΈΡŽ ΡΠΊΡ€ΠΈΠ½ΠΈΡ€ΡƒΡŽΡ‰ΠΈΡ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ Π½Π° Ρ€Π°Π·Π½Ρ‹Π΅ Π²ΠΈΠ΄Ρ‹ ΠΏΠ°Ρ‚ΠΎΠ»ΠΎΠ³ΠΈΠΈ, Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΡŽ ΠΌΠ΅Π΄ΠΈΠΊΠΎ-гСнСтичСской слуТбы

    Π’Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ комплСксного ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° клиничСских Π΄Π°Π½Π½Ρ‹Ρ…

    Get PDF
    The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient’s features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented.Background: Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality.Aims: the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center.Materials and methods: Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as Β«negationΒ» (indicates that the disease is absent), Β«no patientΒ» (indicates that the disease refers to the patient’s family member, but not to the patient), Β«severity of illnessΒ», Β«disease courseΒ», Β«body region to which the disease refersΒ». Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the method for determining the most informative patients’ features are also proposed.Results: Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records of patients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases.Conclusions: The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare. ОбоснованиС. ΠœΠ΅Π΄ΠΈΡ†ΠΈΠ½ΡΠΊΠΈΠ΅ учрСТдСния Π³Π΅Π½Π΅Ρ€ΠΈΡ€ΡƒΡŽΡ‚ большой ΠΏΠΎΡ‚ΠΎΠΊ ΠΊΠ°ΠΊ структурированных, Ρ‚Π°ΠΊ ΠΈ нСструктурированных Π΄Π°Π½Π½Ρ‹Ρ…, содСрТащих Π²Π°ΠΆΠ½ΡƒΡŽ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΡŽ ΠΎ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°Ρ…. Π’ структурированном Π²ΠΈΠ΄Π΅, ΠΊΠ°ΠΊ ΠΏΡ€Π°Π²ΠΈΠ»ΠΎ, хранятся Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Π°Π½Π°Π»ΠΈΠ·ΠΎΠ², ΠΎΠ΄Π½Π°ΠΊΠΎ ΠΏΠΎΠ΄Π°Π²Π»ΡΡŽΡ‰Π΅Π΅ количСство Π΄Π°Π½Π½Ρ‹Ρ… хранится Π² нСструктурированной Ρ„ΠΎΡ€ΠΌΠ΅ Π² Π²ΠΈΠ΄Π΅ тСкстов Π½Π° СстСствСнном языкС (Π°Π½Π°ΠΌΠ½Π΅Π·Ρ‹, Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ осмотров, описания Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² обслСдований, Ρ‚Π°ΠΊΠΈΡ… ΠΊΠ°ΠΊ Π£Π—Π˜, Π­ΠšΠ“, рСнтгСновских исслСдований ΠΈ Π΄Ρ€.). Π˜ΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π½Π°ΠΊΠΎΠΏΠ»Π΅Π½Π½Ρ‹Ρ… массивов структурированных ΠΈ нСструктурированных Π΄Π°Π½Π½Ρ‹Ρ…, ΠΌΠΎΠΆΠ½ΠΎ Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ Ρ€Π΅ΡˆΠ΅Π½ΠΈΠ΅ ΠΌΠ½ΠΎΠ³ΠΈΡ… Π·Π°Π΄Π°Ρ‡, Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡŽΡ‰ΠΈΡ… Π² клиничСской ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅ ΠΈ ΠΏΠΎΠ²Ρ‹ΡΠΈΡ‚ΡŒ качСство мСдицинской ΠΏΠΎΠΌΠΎΡ‰ΠΈ.ЦСль исслСдования: созданиС комплСксной систСмы ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π΄Π°Π½Π½Ρ‹Ρ… Π² ΠΌΠ½ΠΎΠ³ΠΎΠΏΡ€ΠΎΡ„ΠΈΠ»ΡŒΠ½ΠΎΠΌ пСдиатричСском Ρ†Π΅Π½Ρ‚Ρ€Π΅.ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹. Π˜Π·Π²Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈΠ· клиничСских тСкстов Π½Π° русском языкС осущСствляСтся Π½Π° основС ΠΏΠΎΠ»Π½ΠΎΠ³ΠΎ лингвистичСского Π°Π½Π°Π»ΠΈΠ·Π°. Π˜Π·Π²Π»Π΅ΠΊΠ°ΡŽΡ‚ΡΡ упоминания Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, симптомов, областСй Ρ‚Π΅Π»Π°, лСкарствСнных ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚ΠΎΠ². Π’ тСкстС Ρ‚Π°ΠΊΠΆΠ΅ Ρ€Π°ΡΠΏΠΎΠ·Π½Π°ΡŽΡ‚ΡΡ Π°Ρ‚Ρ€ΠΈΠ±ΡƒΡ‚Ρ‹ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ: Β«ΠΎΡ‚Ρ€ΠΈΡ†Π°Π½ΠΈΠ΅Β» (ΡƒΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚ Π½Π° Ρ‚ΠΎ, Ρ‡Ρ‚ΠΎ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠ΅ отсутствуСт), Β«Π½Π΅ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Β» (ΡƒΠΊΠ°Π·Ρ‹Π²Π°Π΅Ρ‚ Π½Π° Ρ‚ΠΎ, Ρ‡Ρ‚ΠΎ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠ΅ относится Π½Π΅ ΠΊ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Ρƒ, Π° ΠΊ Π΅Π³ΠΎ родствСннику), Β«Ρ‚ΡΠΆΠ΅ΡΡ‚ΡŒ заболСвания», Β«Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ заболСвания», Β«ΠΎΠ±Π»Π°ΡΡ‚ΡŒ Ρ‚Π΅Π»Π°, ΠΊ ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠΉ относится Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠ΅Β». Для извлСчСния ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‚ΡΡ мСдицинскиС тСзаурусы, Π½Π°Π±ΠΎΡ€ Π²Ρ€ΡƒΡ‡Π½ΡƒΡŽ составлСнных шаблонов, Π° Ρ‚Π°ΠΊΠΆΠ΅ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π½Π° основС машинного обучСния. ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Π΅ ΠΈΠ· тСкстов Π΄Π°Π½Π½Ρ‹Π΅ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‚ΡΡ для Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ‡ΠΈ автоматичСской диагностики хроничСских Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅Ρ‚ΠΎΠ΄ Π½Π° основС машинного обучСния для классификации ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² со схоТими нозологиями, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΌΠ΅Ρ‚ΠΎΠ΄ для опрСдСлСния Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½Ρ‹Ρ… ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ².Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. Π­ΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½ΠΎΠ΅ исслСдованиС Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡŒ Π½Π° ΠΎΠ±Π΅Π·Π»ΠΈΡ‡Π΅Π½Π½Ρ‹Ρ… историях Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² пСдиатричСского Ρ†Π΅Π½Ρ‚Ρ€Π°. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΎΡ†Π΅Π½ΠΊΠ° качСства Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½Ρ‹Ρ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² извлСчСния ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈΠ· клиничСских тСкстов Π½Π° русском языкС. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π° ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Π°Ρ ΠΎΡ†Π΅Π½ΠΊΠ° ΠΌΠ΅Ρ‚ΠΎΠ΄Π° автоматичСской диагностики Π½Π° Π΄Π°Π½Π½Ρ‹Ρ… ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² с аллСргичСскими заболСваниями ΠΈ Π±ΠΎΠ»Π΅Π·Π½Ρ‹ΠΌΠΈ ΠΎΡ€Π³Π°Π½ΠΎΠ² дыхания, нСфрологичСскими ΠΈ рСвматичСскими заболСваниями. ΠžΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Ρ‹ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ подходящиС ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ машинного обучСния для классификации ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² для ΠΊΠ°ΠΆΠ΄ΠΎΠΉ Π³Ρ€ΡƒΠΏΠΏΡ‹ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, Π° Ρ‚Π°ΠΊΠΆΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½Ρ‹Π΅ ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΈ. ИспользованиС Π΄Π°Π½Π½Ρ‹Ρ…, ΠΈΠ·Π²Π»Π΅Ρ‡Π΅Π½Π½Ρ‹Ρ… ΠΈΠ· клиничСских тСкстов совмСстно со структурированными Π΄Π°Π½Π½Ρ‹ΠΌΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΏΠΎΠ²Ρ‹ΡΠΈΡ‚ΡŒ качСство диагностики хроничСских Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с использованиСм лишь доступных структурированных Π΄Π°Π½Π½Ρ‹Ρ…. ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½Ρ‹ Ρ‚Π°ΠΊΠΆΠ΅ ΡˆΠ°Π±Π»ΠΎΠ½Π½Ρ‹Π΅ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ.Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅. Π Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½Ρ‹Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π±Ρ‹Π»ΠΈ Ρ€Π΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½Ρ‹ Π² систСмС ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π΄Π°Π½Π½Ρ‹Ρ… Π² ΠΌΠ½ΠΎΠ³ΠΎΠΏΡ€ΠΎΡ„ΠΈΠ»ΡŒΠ½ΠΎΠΌ пСдиатричСском Ρ†Π΅Π½Ρ‚Ρ€Π΅. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ‹Π΅ исслСдования ΡΠ²ΠΈΠ΄Π΅Ρ‚Π΅Π»ΡŒΡΡ‚Π²ΡƒΡŽΡ‚ ΠΎ пСрспСктивности использования систСмы для ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ качСства мСдицинской ΠΏΠΎΠΌΠΎΡ‰ΠΈ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°ΠΌ дСтской возрастной ΠΊΠ°Ρ‚Π΅Π³ΠΎΡ€ΠΈΠΈ

    Π“Π΅Π½Π΄Π΅Ρ€Π½Ρ‹Π΅ особСнности распространСнности повСдСнчСских Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² риска Ρƒ ΠΆΠΈΡ‚Π΅Π»Π΅ΠΉ Π‘Π°Π½ΠΊΡ‚-ΠŸΠ΅Ρ‚Π΅Ρ€Π±ΡƒΡ€Π³Π°

    Get PDF
    Background: In developed countries there are significant gender differences in lifetime expectancy that can be explained by behavioral risk factorsΒ (RF).Objective: The aim of our study was to estimate gender features of behavioral RF in general population of Saint-Petersburg, Russia.Methods:Β As a part of all-Russian epidemiology survey ESSE-RF a random sampling of 1600 Saint-Petersburg inhabitants (25-64 y.o.) stratified by age andΒ sex was performed. All participants filled in the questionnaire. Anthropometry (weight, height, body-mass index (BMI), waist circumference (WC))Β and fasting blood-tests (lipids, glucose by Abbott Architect 8000 (USA)) were performed.Results: There were examined 573 (36%) men and 1027Β (64%) women. No gender differences in obesity were found according to BMI criteria β€” in 178 (31.2%) women and 352 (35.1%) men. ObesityΒ was more often detected in females according to WC criteria: АВРIII β€” 44.1 vs 30.3%; IDF 51.2 vs 66.4% (p 0.001 for both). Linear regressionΒ analysis was performed and age was associated with BMI β€” 1.6 kg/m2/decade, WC in women β€” 5,2 cm/decade and WC in men β€” 2.8 cm/decade,Β Ρ€ 0.001 for all anthropometric parameters. Optimal level of physical activity was equally documented in both genders β€” 540 (61.2%) women andΒ 286 (58.9%) men. Daily intake of sweets was lower in men β€” 228 (39.8%) vs 539 (52.5%) in women (p 0.001). 810 (50,6%) of trial subjects wereΒ non-smokers, 395 (24,7%) were former smokers, and 395 (24,7%) were smokers at the moment of trial. The higher number of female smokersΒ was observed β€” 194 (19.1%).Conclusion: A high prevalence of obesity is observed in sample of Saint-Petersburg inhabitants β€” it is higher amongΒ women according to WC criteria regardless of menopause, possibly due to bigger sweets consumption. Males smoke more often and consume lessΒ fresh fruits and vegetables which is accompanied by a higher prevalence of hyperglycemia and hypertriglyceridemia.Π’ Ρ€Π°Π·Π²ΠΈΡ‚Ρ‹Ρ… странах ΠΎΡ‚ΠΌΠ΅Ρ‡Π°ΡŽΡ‚ΡΡ Π³Π΅Π½Π΄Π΅Ρ€Π½Ρ‹Π΅ различия Π² ΠΎΠΆΠΈΠ΄Π°Π΅ΠΌΠΎΠΉ ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΠΆΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ ΠΆΠΈΠ·Π½ΠΈ, Ρ‡Ρ‚ΠΎ ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ объяснСно профилСм повСдСнчСских Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² риска.ЦСль исслСдования: ΠΈΠ·ΡƒΡ‡ΠΈΡ‚ΡŒ Π³Π΅Π½Π΄Π΅Ρ€Π½Ρ‹Π΅ особСнности профиля повСдСнчСских Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² сСрдСчно-сосудистого риска Π² популяции ΠΆΠΈΡ‚Π΅Π»Π΅ΠΉ Π‘Π°Π½ΠΊΡ‚-ΠŸΠ΅Ρ‚Π΅Ρ€Π±ΡƒΡ€Π³Π°. ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹: Π² Ρ€Π°ΠΌΠΊΠ°Ρ… ΠΌΠ½ΠΎΠ³ΠΎΡ†Π΅Π½Ρ‚Ρ€ΠΎΠ²ΠΎΠ³ΠΎ эпидСмиологичСского Π½Π°Π±Π»ΡŽΠ΄Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠ³ΠΎΒ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π­Π‘Π‘Π•-Π Π€ Π±Ρ‹Π»Π° сформирована случайная Π²Ρ‹Π±ΠΎΡ€ΠΊΠ° ΠΈΠ· ΠΆΠΈΡ‚Π΅Π»Π΅ΠΉ Π‘Π°Π½ΠΊΡ‚-ΠŸΠ΅Ρ‚Π΅Ρ€Π±ΡƒΡ€Π³Π°, стратифицированная ΠΏΠΎ ΠΏΠΎΠ»Ρƒ ΠΈ возрасту. Участники Π·Π°ΠΏΠΎΠ»Π½ΠΈΠ»ΠΈ стандартный опросник, Π±Ρ‹Π»Π° Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½Π° антропомСтрия: рост, вСс, индСкс массы Ρ‚Π΅Π»Π° (ИМВ), ΠΎΠΊΡ€ΡƒΠΆΠ½ΠΎΡΡ‚ΡŒ Ρ‚Π°Π»ΠΈΠΈ (ОВ). Натощак ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ Π»ΠΈΠΏΠΈΠ΄Π½Ρ‹ΠΉ спСктр, ΡƒΡ€ΠΎΠ²Π΅Π½ΡŒ Π³Π»ΠΈΠΊΠ΅ΠΌΠΈΠΈ.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹: обслСдованы 1600 Ρ‡Π΅Π»ΠΎΠ²Π΅ΠΊ, ΠΈΠ· Π½ΠΈΡ… ΠΌΡƒΠΆΡ‡ΠΈΠ½ 573Β (35,9%), ΠΆΠ΅Π½Ρ‰ΠΈΠ½ 1027 (64,1%). ΠžΠΆΠΈΡ€Π΅Π½ΠΈΠ΅ Ρƒ ΠΌΡƒΠΆΡ‡ΠΈΠ½ ΠΈ ΠΆΠ΅Π½Ρ‰ΠΈΠ½ Π²ΡΡ‚Ρ€Π΅Ρ‡Π°Π»ΠΎΡΡŒ Π² 31–66% случаСв (ΠΏΠΎ ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΡŽ ИМВ β€” Ρƒ 31,2% ΠΌΡƒΠΆΡ‡ΠΈΠ½ ΠΈΒ 35,1% ΠΆΠ΅Π½Ρ‰ΠΈΠ½; ΠΏΠΎ ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΡŽ мСтаболичСского синдрома (АВРIII) β€” Ρƒ 30,3 ΠΈ 44,1%; ΠΏΠΎ ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΡŽ IDF β€” Ρƒ 51,2 ΠΈ 66,4%, соотвСтствСнно; ΠΏΠΎ ΠΎΠ±ΠΎΠΈΠΌ критСриям ОВ Π·Π½Π°Ρ‡ΠΈΠΌΠΎ Ρ‡Π°Ρ‰Π΅ Π²ΡΡ‚Ρ€Π΅Ρ‡Π°Π»Π°ΡΡŒ Ρƒ ΠΆΠ΅Π½Ρ‰ΠΈΠ½, (p 0,001). Π›ΠΈΠ½Π΅ΠΉΠ½Ρ‹ΠΉ рСгрСссионный Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ» ΡƒΡΡ‚Π°Π½ΠΎΠ²ΠΈΡ‚ΡŒΒ Π°ΡΡΠΎΡ†ΠΈΠ°Ρ†ΠΈΡŽ возраста с ИМВ (1,6 ΠΊΠ³/ΠΌ2 Π½Π° 1 Π΄Π΅ΠΊΠ°Π΄Ρƒ), с ОВ Ρƒ ΠΆΠ΅Π½Ρ‰ΠΈΠ½ (5,2 см/Π΄Π΅ΠΊΠ°Π΄Π°) ΠΈ Ρƒ ΠΌΡƒΠΆΡ‡ΠΈΠ½ (2,8 см/Π΄Π΅ΠΊΠ°Π΄Π°; для всСх ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉΒ Ρ€ 0,001). ΠžΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΉ ΡƒΡ€ΠΎΠ²Π΅Π½ΡŒ Π΄Π²ΠΈΠ³Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΉ активности Π½Π΅ различался Ρƒ ΠΌΡƒΠΆΡ‡ΠΈΠ½ (286; 58,9%) ΠΈ ΠΆΠ΅Π½Ρ‰ΠΈΠ½ (540; 61,2%). Π•ΠΆΠ΅Π΄Π½Π΅Π²Π½ΠΎΠ΅Β ΠΏΠΎΡ‚Ρ€Π΅Π±Π»Π΅Π½ΠΈΠ΅ сладостСй Π·Π½Π°Ρ‡ΠΈΠΌΠΎ Ρ€Π΅ΠΆΠ΅ ΠΎΡ‚ΠΌΠ΅Ρ‡Π΅Π½ΠΎ Ρƒ ΠΌΡƒΠΆΡ‡ΠΈΠ½ (228; 39,8%) ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с ΠΆΠ΅Π½Ρ‰ΠΈΠ½Π°ΠΌΠΈ (539; 52,5%; Ρ€ 0,001). НС ΠΊΡƒΡ€ΠΈΠ»ΠΈΒ 810 (50,6%), 395 (24,7%) ΠΊΡƒΡ€ΠΈΠ»ΠΈ Π² ΠΏΡ€ΠΎΡˆΠ»ΠΎΠΌ ΠΈ 395 (24,7%) ΠΊΡƒΡ€ΠΈΠ»ΠΈ Π² ΠΌΠΎΠΌΠ΅Π½Ρ‚ опроса; наблюдалось большоС число курящих ΠΆΠ΅Π½Ρ‰ΠΈΠ½ β€” 194Β (19,1%).Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅: срСди ΠΆΠΈΡ‚Π΅Π»Π΅ΠΉ Π‘Π°Π½ΠΊΡ‚-ΠŸΠ΅Ρ‚Π΅Ρ€Π±ΡƒΡ€Π³Π° рСгистрируСтся высокая Ρ€Π°ΡΠΏΡ€ΠΎΡΡ‚Ρ€Π°Π½Π΅Π½Π½ΠΎΡΡ‚ΡŒ оТирСния (Π·Π½Π°Ρ‡ΠΈΠΌΠΎ Ρ‡Π°Ρ‰Π΅ срСди ТСнщин, согласно ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΡŽ ОВ, Π²Π½Π΅ зависимости ΠΎΡ‚ наличия ΠΌΠ΅Π½ΠΎΠΏΠ°ΡƒΠ·Ρ‹, Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ, Π·Π° счСт большСго потрСблСния сладких ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ΠΎΠ²).Β ΠœΡƒΠΆΡ‡ΠΈΠ½Ρ‹ Π·Π½Π°Ρ‡ΠΈΠΌΠΎ большС курят ΠΈ Ρ€Π΅ΠΆΠ΅ ΠΏΠΎΡ‚Ρ€Π΅Π±Π»ΡΡŽΡ‚ свСТиС ΠΎΠ²ΠΎΡ‰ΠΈ ΠΈ Ρ„Ρ€ΡƒΠΊΡ‚Ρ‹, Ρ‡Ρ‚ΠΎ сопровоТдаСтся большСй Ρ€Π°ΡΠΏΡ€ΠΎΡΡ‚Ρ€Π°Π½Π΅Π½Π½ΠΎΡΡ‚ΡŒΡŽΒ Π³ΠΈΠΏΠ΅Ρ€Π³Π»ΠΈΠΊΠ΅ΠΌΠΈΠΈ ΠΈ Π³ΠΈΠΏΠ΅Ρ€Ρ‚Ρ€ΠΈΠ³Π»ΠΈΡ†Π΅Ρ€ΠΈΠ΄Π΅ΠΌΠΈΠΈ

    Precision medicine : steps towards improving treatment with vitamin K antagonists and ACE-inhibitors

    No full text

    A method for research viscoplastic characteristics of materials using a vertical gas-gun stand

    No full text
    A methodology of constructing dynamic strain diagrams based on the method of direct impact on a vertical gas-gun stand has been developed and theoretically verified. The methodology has been verified by a reconstruction of a given static strain diagram when substituting the physical experiment by computer modeling of an axisymmetric problem, accounting for the wave processes in the striker-specimen-measuring rod system. The main inaccuracies in determining (reconstructing) viscoplastic characteristics of materials are shown to be determined by the degree of accuracy of experimental measurements. Based on the method of characteristics, an algorithm has been developed for calculating the displacements of the impacted end of the rod based on the results of the measurements of strains by two gauges in the vicinity of the impacted and supported ends of the rod for a multiply passing strain wave. This makes it possible to analyze the deformation process up to the moment the striker stops and to compare the computational and experimental results based on the residual size of the tested specimen

    Experimental and calculated approach to the study of deformation and strength characteristics of elastoviscoplastic materials by direct impact method

    No full text
    It is proposed to develop experimental and calculated approach to the study of the strength characteristics of elastoviscoplastic materials in a non-uniform strain-stress state. Integral characteristics (forces, displacements and displacement speed) of the deformation process of hat-shaped specimens in tension are determined by a direct impact method, and their strain – stress states are determined by numerical solution of the axisymmetric problem. The results of experimental and theoretical study of the deformation and failure of hat-shaped specimens in the presence of stress concentrators are obtained

    Experimental and calculated approach to the study of deformation and strength characteristics of elastoviscoplastic materials by direct impact method

    No full text
    It is proposed to develop experimental and calculated approach to the study of the strength characteristics of elastoviscoplastic materials in a non-uniform strain-stress state. Integral characteristics (forces, displacements and displacement speed) of the deformation process of hat-shaped specimens in tension are determined by a direct impact method, and their strain – stress states are determined by numerical solution of the axisymmetric problem. The results of experimental and theoretical study of the deformation and failure of hat-shaped specimens in the presence of stress concentrators are obtained
    corecore