Технологии комплексного интеллектуального анализа клинических данных

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 «negation» (indicates that the disease is absent), «no patient» (indicates that the disease refers to the patient’s family member, but not to the patient), «severity of illness», «disease course», «body region to which the disease refers». 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. Обоснование. Медицинские учреждения генерируют большой поток как структурированных, так и неструктурированных данных, содержащих важную информацию о пациентах. В структурированном виде, как правило, хранятся результаты анализов, однако подавляющее количество данных хранится в неструктурированной форме в виде текстов на естественном языке (анамнезы, результаты осмотров, описания результатов обследований, таких как УЗИ, ЭКГ, рентгеновских исследований и др.). Используя методы интеллектуальной обработки накопленных массивов структурированных и неструктурированных данных, можно автоматизировать решение многих задач, возникающих в клинической практике и повысить качество медицинской помощи.Цель исследования: создание комплексной системы интеллектуальной обработки данных в многопрофильном педиатрическом центре.Методы. Извлечение информации из клинических текстов на русском языке осуществляется на основе полного лингвистического анализа. Извлекаются упоминания заболеваний, симптомов, областей тела, лекарственных препаратов. В тексте также распознаются атрибуты заболеваний: «отрицание» (указывает на то, что заболевание отсутствует), «не пациент» (указывает на то, что заболевание относится не к пациенту, а к его родственнику), «тяжесть заболевания», «течение заболевания», «область тела, к которой относится заболевание». Для извлечения информации используются медицинские тезаурусы, набор вручную составленных шаблонов, а также различные методы на основе машинного обучения. Полученные из текстов данные используются для решения задачи автоматической диагностики хронических заболеваний. Предложен метод на основе машинного обучения для классификации пациентов со схожими нозологиями, а также метод для определения наиболее информативных признаков.Результаты. Экспериментальное исследование разработанных методов проводилось на обезличенных историях болезни пациентов педиатрического центра. Проведена оценка качества разработанных методов извлечения информации из клинических текстов на русском языке. Проведена экспериментальная оценка метода автоматической диагностики на данных пациентов с аллергическими заболеваниями и болезными органов дыхания, нефрологическими и ревматическими заболеваниями. Определены наиболее подходящие методы машинного обучения для классификации пациентов для каждой группы заболеваний, а также наиболее информативные признаки. Использование данных, извлеченных из клинических текстов совместно со структурированными данными, позволило повысить качество диагностики хронических заболеваний по сравнению с использованием лишь доступных структурированных данных. Получены также шаблонные комбинации признаков заболеваний.Заключение. Разработанные методы были реализованы в системе интеллектуальной обработки данных в многопрофильном педиатрическом центре. Проведенные исследования свидетельствуют о перспективности использования системы для повышения качества медицинской помощи пациентам детской возрастной категории

The Glasgow Benefit Inventory: a systematic review of the use and value of an otorhinolaryngological generic patient-recorded outcome measure

The Glasgow Benefit Inventory (GBI) is a validated, generic patient-recorded outcome measure widely used in otolaryngology to report change in quality of life post-intervention.To date, no systematic review has made (i) a quality assessment of reporting of Glasgow Benefit Inventory outcomes; (ii) a comparison between Glasgow Benefit Inventory outcomes for different interventions and objectives; (iii) an evaluation of subscales in describing the area of benefit; (iv) commented on its value in clinical practice and research.Systematic review.'Glasgow Benefit Inventory' and 'GBI' were used as keywords to search for published, unpublished and ongoing trials in PubMed, EMBASE, CINAHL and Google in addition to an ISI citation search for the original validating Glasgow Benefit Inventory paper between 1996 and January 2015.Papers were assessed for study type and quality graded by a predesigned scale, by two authors independently. Papers with sufficient quality Glasgow Benefit Inventory data were identified for statistical comparisons. Papers with 50% and gave sufficient Glasgow Benefit Inventory total and subscales for meta-analysis. For five of the 11 operation categories (vestibular schwannoma, tonsillectomy, cochlear implant, middle ear implant and stapes surgery) that were most likely to have a single clear clinical objective, score data had low-to-moderate heterogeneity. The value in the Glasgow Benefit Inventory having both positive and negative scores was shown by an overall negative score for the management of vestibular schwannoma. The other six operations gave considerable heterogeneity with rhinoplasty and septoplasty giving the greatest percentages (98% and 99%) most likely because of the considerable variations in patient selection. The data from these operations should not be used for comparative purposes. Five papers also reported the number of patients that had no or negative benefit, a potentially a more clinically useful outcome to report. Glasgow Benefit Inventory subscores for tonsillectomy were significantly different from ear surgery suggesting different areas of benefitThe Glasgow Benefit Inventory has been shown to differentiate the benefit between surgical and medical otolaryngology interventions as well as 'reassurance'. Reporting benefit as percentages with negative, no and positive benefit would enable better comparisons between different interventions with varying objectives and pathology. This could also allow easier evaluation of factors that predict benefit. Meta-analysis data are now available for comparison purposes for vestibular schwannoma, tonsillectomy, cochlear implant, middle ear implant and stapes surgery. Fuller report of the Glasgow Benefit Inventory outcomes for non-surgical otolaryngology interventions is encouraged

Measurement of the Dalitz plot slope parameters for K- -> pi0 pi0 pi- decay using ISTRA+ detector

The Dalitz plot slope parameters g, h and k for the K- -> pi0 pi0 pi- decay have been measured using in-flight decays detected with the ISTRA+ setup operating in the 25 GeV negative secondary beam of the U-70 PS. About 252 K events with four-momenta measured for the pi- and four involved photons were used for the analysis. The values obtained g=0.627+/-0.004(stat)+/-0.010(syst), h=0.046+/-0.004(stat)+/-0.012(syst), k=0.001+/-0.001(stat)+/-0.002(syst) are consistent with the world averages dominated by K+ data, but have significantly smaller errors.Comment: LaTeX, 10 pages, 8 eps-figures, update of IHEP 2002-1

Exact asymptotic form of the exchange interactions between shallow centers in doped semiconductors

The method developed in [L. P. Gor'kov and L. P. Pitaevskii, Sov. Phys. Dokl. 8, 788 (1964); C. Herring and M. Flicker, Phys. Rev. 134, A362 (1964)] to calculate the asymptotic form of exchange interactions between hydrogen atoms in the ground state is extended to excited states. The approach is then applied to shallow centers in semiconductors. The problem of the asymptotic dependence of the exchange interactions in semiconductors is complicated by the multiple degeneracy of the ground state of an impurity (donor or acceptor) center in valley or band indices, crystalline anisotropy and strong spin-orbital interactions, especially for acceptor centers in III-V and II-VI groups semiconductors. Properties of two coupled centers in the dilute limit can be accessed experimentally, and the knowledge of the exact asymptotic expressions, in addition to being of fundamental interest, must be very helpful for numerical calculations and for interpolation of exchange forces in the case of intermediate concentrations. Our main conclusion concerns the sign of the magnetic interaction -- the ground state of a pair is always non-magnetic. Behavior of the exchange interactions in applied magnetic fields is also discussed

High statistics study of the K- -> pi0 e- nu decay

The decay K- -> pi0 e- nu has been studied using in-flight decays detected with the "ISTRA+" spectrometer working at the 25 GeV negative secondary beam of the U-70 PS. About 550K events were used for the analysis. The lambda+ parameter of the vector form-factor has been measured: lambda+ = 0.0286 +- 0.0008 (stat) +- 0.0006(syst). The limits on the possible tensor and scalar couplings have been obtained: f(T)/f+(0)=0.021 +0.064 -0.075 (stat) +- 0.026(syst) ; f(S)/f+(0)=0.002 +0.020 -0.022 (stat) +- 0.003(syst)Comment: LaTeX-2e, epsfig.sty, 10 pages, 7 figures in EPS forma

Gluon polarization in the nucleon from quasi-real photoproduction of high-pT hadron pairs

We present a determination of the gluon polarization Delta G/G in the nucleon, based on the helicity asymmetry of quasi-real photoproduction events, Q^2<1(GeV/c)^2, with a pair of large transverse-momentum hadrons in the final state. The data were obtained by the COMPASS experiment at CERN using a 160 GeV polarized muon beam scattered on a polarized 6-LiD target. The helicity asymmetry for the selected events is = 0.002 +- 0.019(stat.) +- 0.003(syst.). From this value, we obtain in a leading-order QCD analysis Delta G/G=0.024 +- 0.089(stat.) +- 0.057(syst.) at x_g = 0.095 and mu^2 =~ 3 (GeV}/c)^2.Comment: 10 pages, 3 figure

Measurement of the Spin Structure of the Deuteron in the DIS Region

We present a new measurement of the longitudinal spin asymmetry A_1^d and the spin-dependent structure function g_1^d of the deuteron in the range 1 GeV^2 < Q^2 < 100 GeV^2 and 0.004< x <0.7. The data were obtained by the COMPASS experiment at CERN using a 160 GeV polarised muon beam and a large polarised 6-LiD target. The results are in agreement with those from previous experiments and improve considerably the statistical accuracy in the region 0.004 < x < 0.03.Comment: 10 pages, 6 figures, subm. to PLB, revised: author list, Fig. 4, details adde