34 research outputs found

    Broadening the Bandwidth of Piezoelectric Energy Harvesters Using Liquid Filled Mass

    Get PDF
    AbstractA narrow bandwidth is one of the most challenging issues that vibrational energy harvesters have to overcome. This paper demonstrates a novel method of broadening the bandwidth without significantly reducing the peak output voltage. The method uses a liquid filled mass to create a sliding mass effect in order to broaden the bandwidth. The fluid mass increased the full-width-half-maximum (FWHM) value from 1.6Hz to 4.45Hz with no significant decrease in peak-to-peak voltage when compared to an empty mass. The fluid filled mass has a non-linear mass distribution during low frequency, high acceleration applications

    Dutch Historical Spelling Normalization for Parsing and Coreference Resolution

    Get PDF
    Non-canonical language can be handled in an NLP pipeline using normalization of the input (e.g., MoNoise; van der Goot & van Noord, CLINjournal 2017) or domain adaptation of the pipeline (e.g., Hupkes & Bod, LREC 2016); we focus on the former. MoNoise shows that normalization is effective for social media language. We consider a different domain: Dutch literature from Project Gutenberg. We work with 9 fragments that make up the OpenBoek corpus (van den Berg et al., CLIN 2021). The fragments consist of 10,000+ tokens from texts first published 1860-1920, both translated and originally Dutch.MoNoise consists of several modules: a lookup table, automatic spelling correction (aspell), and word embeddings; we aim to explore these techniques on our data in future work. Here we report results of a rule-based approach implemented with a sed script (i.e., regular expressions) for normalizing frequently occurring non-standard spellings.The output consists of instructions to the Alpino parser (van Noord, TALN 2006) to treat words with non-canonical orthography as if they occur with modern spelling. The advantage of this approach is that the resulting parse trees contain the original tokens, and existing annotation layers (such as coreference) do not have to be re-aligned. Consider the following sentence from Couperus, Eline Vere (ch. I, § II):18-1|- Is het [ @alt zo zoo ] goed ? vroeg zij met bevende stem , [ @alt ene eene ] , van te voren bestudeerde poze aannemende .Here [ @alt zo zoo ] indicates that the original token zoo should be treated as zo. Besides doubled vowels, other frequent spelling normalizations are de/den, zei/zeide, and mensen/menschen. When multiple alternatives are given the parser considers the input as a lattice and uses the sequence of tokens that generates the most likely parse. Parse trees for the above sentence show that the automatic spelling normalization is not perfect (the correct normalization of eene is een with POS lid rather than ene), but it does lead to a correct bracketing of the NP eene … poze. Furthermore, it turns out that a comma is missing after bestudeerde in the Project Gutenberg etext we use (EBook-No. 19563); the DBNL version of this text (coup002elin01_01) does have this comma—this underscores the importance of professionally edited critical editions.We will perform an intrinsic evaluation of our spelling normalization pipeline with manually corrected texts and report F1 scores (Reynaert, LREC 2008). We also perform an extrinsic evaluation of downstream tasks: part-of-speech tagging, mention detection, and coreference resolution. Scores for the latter two tasks on Multatuli, Max Havelaar:mentions lea pronrecall prec f1 recall prec f1 CoNLL accoriginal 89.96 81.29 85.40 54.80 47.07 50.64 65.76 55.00normalized 90.18 82.22 86.02 54.82 45.96 50.00 65.48 54.20The mention score is improved, which makes sense given that parsing of NPs seems to improve after spelling normalization, but there is a decrease in the coreference metrics, which warrants further investigation

    Explaining primary health care pharmacy expenditure using classification of medications for chronic conditions

    Full text link
    Background The Valencian Autonomous Community (Spain) has implemented a scheme of purchasing services with the participation of public and private providers. Five districts are managed using public¿private partnership. The financing model is capitation and inter-center invoice. The pharmaceutical benefits are not included in the per capita assignment. Objectives Modeling and explaining pharmacy expenditure using electronic prescriptions drug data. Methods A database of electronic prescription corresponding to 625,246 patients between November 2008 and October 2009 was used to run four linear models that explain the pharmaceutical expenditures. We take as dependent variable the neperian log of total pharmacy annual cost per patient in the primary health setting. The independent variables used combined demographics with revised classification in 18 chronic conditions obtained from the anatomical therapeutic chemical classification index (ATC). Results The retrospective model selected included: gender, pharmaceutical co-payment status and 8 dummy variables for the number of chronic conditions of each patient from 1 to 8 or more. The goodness-of-fit achieved is measured in R2 of 57%. Conclusions These models must be considered in the current capitation system for pharmaceutical budgeting in a primary care setting established at regional level, as is the case in the Valencian Autonomous Community. The use of diagnostics and information regarding hospital encounters appears to be a complementary option for refining models of capitation of pharmaceutical and total health expenditure.The authors thank the General Direction of Pharmacy of the Valencian Department of Health for financial support and the working group for providing the data set. The opinions expressed in this paper are those of the authors and do not necessarily reflect those of the afore-named. Any errors are the authors' responsibility. We would also like to thank the two anonymous reviewers for their comments, which helped greatly to improve this paper.Vivas Consuelo, DJJ.; Guadalajara Olmeda, MN.; Barrachina Martínez, I.; Trillo-Mata, J.; Usó-Talamantes, R.; De La Poza, E. (2011). Explaining primary health care pharmacy expenditure using classification of medications for chronic conditions. Health Policy. 103(1):9-15. https://doi.org/10.1016/j.healthpol.2011.08.014S915103

    The structure of the tetrasialoganglioside from human brain

    Get PDF
    Autosomal dominant retinal vasculopathy with cerebral leukodystrophy is a microvascular endotheliopathy with middle- age onset. In nine families, we identified heterozygous C- terminal frameshift mutations in TREX1, which encodes a 3'-5' exonuclease. These truncated proteins retain exonuclease activity but lose normal perinuclear localization. These data have implications for the maintenance of vascular integrity in the degenerative cerebral microangiopathies leading to stroke and dementias

    Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups

    Full text link
    Background: Risk adjustment instruments applied to existing electronic health records and administrative datasets may contribute to monitoring the correct prescribing of medicines. Objective: We aim to test the suitability of the model based on the CRG system and obtain specific adjusted weights for determined health states through a predictive model of pharmaceutical expenditure in primary health care. Methods: A database of 261,054 population in one health district of an Eastern region of Spain was used. The predictive power of two models was compared. The first model (ATC-model) used nine dummy variables: sex and 8 groups from 1 to 8 or more chronic conditions while in the second model (CRG-model) we include sex and 8 dummy variables for health core statuses 2-9. Results: The two models achieved similar levels of explanation. However, the CRG system offers higher clinical significance and higher operational utility in a real context, as it offers richer and more updated information on patients. Conclusions: The potential of the CRG model developed compared to ATC codes lies in its capacity to stratify the population according to specific chronic conditions of the patients, allowing us to know the degree of severity of a patient or group of patients, predict their pharmaceutical cost and establish specific programmes for their treatment. (C) 2014 Elsevier Ireland Ltd. All rights reserved.This study was financed by a grant from the Fondo de Investigaciones de la Seguridad Social Instituto de Salud Carlos III, the Spanish Ministry of Health (FIS PI12/0037). The authors would like to thank members (Juan Bru and Inma Sauri) of the Pharmacoeconomics Office of the Valencian Health Agency. The opinions expressed in this paper are those of the authors and do not necessary reflect those of the afore-named. Any errors are the authors' responsibility. We would also like to thank John Wright for the English editing.Vivas Consuelo, DJJ.; Usó Talamantes, R.; Trillo Mata, JL.; Caballer Tarazona, M.; Barrachina Martínez, I.; Buigues Pastor, L. (2014). Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups. Health Policy. 116(2-3):188-195. https://doi.org/10.1016/j.healthpol.2014.01.012S1881951162-

    Pharmaceutical Cost Management in an Ambulatory Setting Using a Risk Adjustment Tool

    Get PDF
    © 2014 Vivas-Consuelo et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.Background Pharmaceutical expenditure is undergoing very high growth, and accounts for 30% of overall healthcare expenditure in Spain. In this paper we present a prediction model for primary health care pharmaceutical expenditure based on Clinical Risk Groups (CRG), a system that classifies individuals into mutually exclusive categories and assigns each person to a severity level if s/he has a chronic health condition. This model may be used to draw up budgets and control health spending. Methods Descriptive study, cross-sectional. The study used a database of 4,700,000 population, with the following information: age, gender, assigned CRG group, chronic conditions and pharmaceutical expenditure. The predictive model for pharmaceutical expenditure was developed using CRG with 9 core groups and estimated by means of ordinary least squares (OLS). The weights obtained in the regression model were used to establish a case mix system to assign a prospective budget to health districts. Results The risk adjustment tool proved to have an acceptable level of prediction (R2 0.55) to explain pharmaceutical expenditure. Significant differences were observed between the predictive budget using the model developed and real spending in some health districts. For evaluation of pharmaceutical spending of pediatricians, other models have to be established. Conclusion The model is a valid tool to implement rational measures of cost containment in pharmaceutical expenditure, though it requires specific weights to adjust and forecast budgets.This study was financed by a grant from the Fondo de Investigaciones de la Seguridad Social Instituto de Salud Carlos III, the Spanish Ministry of Health (FIS PI12/0037). The authors would like to thank members (Juan Bru and Inma Saurf) of the Pharmacoeconomics Office of the Valencian Health Department. The opinions expressed in this paper are those of the authors and do not necessary reflect those of the afore-named. Any errors are the authors' responsibility. We would also like to thank John Wright for the English editing.Vivas Consuelo, DJJ.; Usó Talamantes, R.; Guadalajara Olmeda, MN.; Trillo Mata, JL.; Sancho Mestre, C.; Buigues Pastor, L. (2014). Pharmaceutical Cost Management in an Ambulatory Setting Using a Risk Adjustment Tool. BMC Health Services Research. 14:462-472. https://doi.org/10.1186/1472-6963-14-462S46247214Hux JE, Naylor CD: Drug prices and third party payment: do they influence medication selection?. Pharmacoecon. 1994, 5 (4): 343-350. 10.2165/00019053-199405040-00008.Sicras-Mainar A, Serrat-Tarres J, Navarro-Artieda R, Llopart-Lopez J: [Prospects of adjusted clinical groups (ACG’s) in capitated payment risk adjustment]. Rev Esp Salud Publica. 2006, 80 (1): 55-65. 10.1590/S1135-57272006000100006.Mossey JM, Roos LL: Using insurance claims to measure health-status - the illness scale. J Chronic Dis. 1987, 40: S41-S50.Newhouse JP, Manning WG, Keeler EB, Sloss EM: Adjusting capitation rates using objective health measures and prior utilization. Health Care Financ Rev. 1989, 10 (3): 41-54.Ash A, Porell F, Gruenberg L, Sawitz E, Beiser A: Adjusting Medicare capitation payments using prior hospitalization data. Health Care Financ Rev. 1989, 10 (4): 17-29.Ellis RP, Pope GC, Iezzoni L, Ayanian JZ, Bates DW, Burstin H, Ash AS: Diagnosis-based risk adjustment for Medicare capitation payments. Health Care Financ Rev. 1996, 17 (3): 101-128.Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Lezzoni LI, Ingber MJ, Levy JM, Robst J: Risk adjustment of Medicare capitation payments using the CMS-HCC model. Health Care Financ Rev. 2004, 25 (4): 119-141.Starfield B, Weiner J, Mumford L, Steinwachs D: Ambulatory care groups: a categorization of diagnoses for research and management. Health Serv Res. 1991, 26 (1): 53-74.Weiner JP, Starfield BH, Steinwachs DM, Mumford LM: Development and application of a population-oriented measure of ambulatory care case-mix. Med Care. 1991, 29 (5): 452-472. 10.1097/00005650-199105000-00006.Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, Gay JC: Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004, 42 (1): 81-90. 10.1097/01.mlr.0000102367.93252.70.Berlinguet M, Preyra C, Dean S: Comparing the Value of Three Main Diagnostic-Based Risk-Adjustment Systems (DBRAS). 2005, Ottawa, Ontario: Edited by Foundation CHSRVon Korff M, Wagner EH, Saunders K: A chronic disease score from automated pharmacy data. J Clin Epidemiol. 1992, 45 (2): 197-203. 10.1016/0895-4356(92)90016-G.Malone DC, Billups SJ, Valuck RJ, Carter BL: Development of a chronic disease indicator score using a Veterans Affairs Medical Center medication database. IMPROVE Investigators. J Clin Epidemiol. 1999, 52 (6): 551-557. 10.1016/S0895-4356(99)00029-3.Clark DO, Von Korff M, Saunders K, Baluch WM, Simon GE: A chronic disease score with empirically derived weights. Med Care. 1995, 33 (8): 783-795. 10.1097/00005650-199508000-00004.Lamers LM: Pharmacy costs groups: a risk-adjuster for capitation payments based on the use of prescribed drugs. Med Care. 1999, 37 (8): 824-830. 10.1097/00005650-199908000-00012.Lamers LM: Health-based risk adjustment: is inpatient and outpatient diagnostic information sufficient?. Inquiry. 2001, 38 (4): 423-431.Lamers LM, van Vliet RC: The Pharmacy-based Cost Group model: validating and adjusting the classification of medications for chronic conditions to the Dutch situation. Health Policy. 2004, 68 (1): 113-121. 10.1016/j.healthpol.2003.09.001.Lamers LM, Vliet RC: Health-based risk adjustment Improving the pharmacy-based cost group model to reduce gaming possibilities. Eur J Health Econ. 2003, 4 (2): 107-114. 10.1007/s10198-002-0159-9.Johnson RE, Hornbrook MC, Nichols GA: Replicating the chronic disease score (CDS) from automated pharmacy data. J Clin Epidemiol. 1994, 47 (10): 1191-1199. 10.1016/0895-4356(94)90106-6.Zhao Y, Ellis RP, Ash AS, Calabrese D, Ayanian JZ, Slaughter JP, Weyuker L, Bowen B: Measuring population health risks using inpatient diagnoses and outpatient pharmacy data. Health Serv Res. 2001, 36 (6 Pt 2): 180-193.Stam PJ, van Vliet RC, van de Ven WP: Diagnostic, pharmacy-based, and self-reported health measures in risk equalization models. Med Care. 2010, 48 (5): 448-457. 10.1097/MLR.0b013e3181d559b4.Hanley GE, Morgan S, Reid RJ: Explaining prescription drug use and expenditures using the adjusted clinical groups case-mix system in the population of British Columbia, Canada. Can Med Care. 2010, 48 (5): 402-408. 10.1097/MLR.0b013e3181ca3d5d.Aguado A, Guino E, Mukherjee B, Sicras A, Serrat J, Acedo M, Ferro JJ, Moreno V: Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: a cross-sectional study of patient electronic records in primary care. BMC Health Serv Res. 2008, 8 (4): 11.Garcia-Goni M, Ibern P: Predictability of drug expenditures: An application using morbidity data. Health Econ. 2008, 17 (1): 119-126. 10.1002/hec.1238.Garcia-Goni M, Ibern P, Inoriza JM: Hybrid risk adjustment for pharmaceutical benefits. Eur J Health Econ. 2009, 10 (3): 299-308. 10.1007/s10198-008-0133-2.Vivas-Consuelo D, Uso-Talamantes R, Trillo-Mata JL, Caballer-Tarazona M, Barrachina-Martinez I, Buigues-Pastor L: Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups. Health Policy. 2014, 116 (2–3): 188-195.Robst J, Levy JM, Ingber MJ: Diagnosis-based risk adjustment for medicare prescription drug plan payments. Health Care Financ Rev. 2007, 28 (4): 15-30.Zhao Y, Ash AS, Ellis RP, Ayanian JZ, Pope GC, Bowen B, Weyuker L: Predicting pharmacy costs and other medical costs using diagnoses and drug claims. Med Care. 2005, 43 (1): 34-43.Buchner F, Goepffarth D, Wasem J: The new risk adjustment formula in Germany: implementation and first experiences. Health Policy. 2013, 109 (3): 253-262. 10.1016/j.healthpol.2012.12.001.Inoriza JM, Coderch J, Carreras M, Vall-Llosera L, Garcia-Goni M, Lisbona JM, Ibern P: [Measurement of morbidity attended in an integrated health care organization]. Gac Sanit. 2009, 23 (1): 29-37. 10.1016/j.gaceta.2008.02.003.Orueta JF, Mateos Del Pino M, Barrio Beraza I, Nuno Solinis R, Cuadrado Zubizarreta M, Sola Sarabia C: [Stratification of the population in the Basque Country: results in the first year of implementation.]. Aten Primaria. 2012, 45 (1): 54-60.Sicras-Mainar A, Navarro-Artieda R: [Validating the Adjusted Clinical Groups [ACG] case-mix system in a Spanish population setting: a multicenter study]. Gac Sanit. 2009, 23 (3): 228-231. 10.1016/j.gaceta.2008.04.005.Omar RZ, O’Sullivan C, Petersen I, Islam A, Majeed A: A model based on age, sex, and morbidity to explain variation in UK general practice prescribing: cohort study. BMJ. 2008, 337: a238-10.1136/bmj.a238.Caballer-Tarazona M, Buigues-Pastor L, Saurí- Ferrer I, Uso-Talamantes R, Trillo-Mata JL: [A standardized amount indicator by equivalent patient to control outpatient pharmaceutical expenditure, Spain]. Rev Esp Salud Publica. 2011, 86: 371-380.De la Poza-Plaza E, Barrachina I, Trillo-Mata J, Uso-Talamantes R: Sistema de Prescripción y dispensación electrónica en la Agencia Valenciana de Salud. El Prof de la Inf. 2011, 20: 9.Vivas D, Guadalajara N, Barrachina I, Trillo JL, Uso R, De-la-Poza E: Explaining primary healthcare pharmacy expenditure using classification of medications for chronic conditions. Health Policy. 2011, 103 (1): 9-15. 10.1016/j.healthpol.2011.08.014.Buntin MB, Zaslavsky AM: Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. J Health Econ. 2004, 23 (3): 525-542. 10.1016/j.jhealeco.2003.10.005.Duan N: Smearing estimate - a nonparametric retransformation method. J Am Stat Assoc. 1983, 78 (383): 605-610. 10.1080/01621459.1983.10478017.Calderon-Larranaga A, Abrams C, Poblador-Plou B, Weiner JP, Prados-Torres A: Applying diagnosis and pharmacy-based risk models to predict pharmacy use in Aragon, Spain: the impact of a local calibration. BMC Health Serv Res. 2010, 10: 22-10.1186/1472-6963-10-22

    Like dogs in December: Women\u27s perspectives on HIV/AIDS prevention programs and married life in rural northeast Thailand

    No full text
    HIV/AIDS has spread rapidly in Thailand since the first documented cases in 1984, due to the prevalence and nature of the commercial sex industry and the social norms surrounding extramarital sex and condom use. Because the highest incidence of HIV/AIDS continues to be in large cities and resort towns, rural people have been perceived as having low-risk for HIV/AIDS transmission. The economically-disadvantaged rural population faces increasingly higher risk because of its mobility and interaction with urban populations. Married women\u27s risk is primarily found in sexual behavior within marriage; their limited ability to protect themselves relate to the difficulty of obtaining accurate information about their husbands\u27 extramarital sexual behavior. The Multisectoral AIDS Prevention Strategy (MAPS) Program, in coordination with Khon Kaen University and the Thai government, was a program designed to help rural community members assess their HIV/AIDS risk and take preventive action. The program aimed to improve rural women\u27s abilities to protect themselves, using a community-based, culturally-sensitive approach. However, by using traditional methods and systems for delivering health information, in practice the program shifted in focus from women to men in both the dynamics of participation and the AIDS prevention strategies produced by the program. The study was based on interviews and focus groups, emphasizing the points of view of married women who live within the program area but were not directly involved in program activities. The politics of women\u27s participation in program activities and leadership is the initial point of inquiry. Wives\u27 experience with personal risk assessment and how it relates to judgement of their husbands\u27 character are described. Having little success using the methods available through the health infrastructure, women have created their own innovative yet problematic AIDS prevention strategies. The actual practices of women are juxtaposed with the official MAPS-sponsored AIDS prevention strategies. Current perceptions about people with HIV/AIDS are described in so far as they suggest future opportunities and challenges for HIV/AIDS prevention efforts. Recommendations for maintaining a women focus and making better use of indigenous solutions conclude the work
    corecore