MW, Medical Watches

My project is a business plan about the set up of a company and the development of a new and innovative product aimed for the elders. I decide do this project when I discover that one of the more important needs that have the elders is to remember the medicines that they have to take. I thought that a good way could be through a smart watch. My watch have an only function, is a cheap device, easy to use, easy to understand and easy to set up, because the elders usually do not know to use complex electronics devices. There are other similar smart watches and other devices but do not have the necessary characteristics to be a good reminder for elders. My watch is centred to improve the life of the elders, but my product could also be useful for ill people who have to take many medicines during the day. After realizing this business plan, I have proved that my company is viable in the environment and profitable in the market.

Abundance of harbor porpoise (Phocoena phocoena) in three Alaskan regions, corrected for observer errors due to perception bias and species misidentification, and corrected for animals submerged from view

Estimating the abundance of cetaceans from aerial survey data requires careful attention to survey design and analysis. Once an aerial observer perceives a marine mammal or group of marine mammals, he or she has only a few seconds to identify and enumerate the individuals sighted, as well as to determine the distance to the sighting and record this information. In line-transect survey analyses, it is assumed that the observer has correctly identified and enumerated the group or individual. We describe methods used to test this assumption and how survey data should be adjusted to account for observer errors. Harbor porpoises (Phocoena phocoena) were censused during aerial surveys in the summer of 1997 in Southeast Alaska (9844 km survey effort), in the summer of 1998 in the Gulf of Alaska (10,127 km), and in the summer of 1999 in the Bering Sea (7849 km). Sightings of harbor porpoise during a beluga whale (Phocoena phocoena) survey in 1998 (1355 km) provided data on harbor porpoise abundance in Cook Inlet for the Gulf of Alaska stock. Sightings by primary observers at side windows were compared to an independent observer at a belly window to estimate the probability of misidentification, underestimation of group size, and the probability that porpoise on the surface at the trackline were missed (perception bias, g(0)). There were 129, 96, and 201 sightings of harbor porpoises in the three stock areas, respectively. Both g(0) and effective strip width (the realized width of the survey track) depended on survey year, and g(0) also depended on the visibility reported by observers. Harbor porpoise abundance in 1997–99 was estimated at 11,146 animals for the Southeast Alaska stock, 31,046 animals for the Gulf of Alaska stock, and 48,515 animals for the Bering Sea stock.

Measurement errors in body size of sea scallops (Placopecten magellanicus) and their effect on stock assessment models

Body-size measurement errors are usually ignored in stock assessments, but may be important when body-size data (e.g., from visual sur veys) are imprecise. We used experiments and models to quantify measurement errors and their effects on assessment models for sea scallops (Placopecten magellanicus). Errors in size data obscured modes from strong year classes and increased frequency and size of the largest and smallest sizes, potentially biasing growth, mortality, and biomass estimates. Modeling techniques for errors in age data proved useful for errors in size data. In terms of a goodness of model fit to the assessment data, it was more important to accommodate variance than bias. Models that accommodated size errors fitted size data substantially better. We recommend experimental quantification of errors along with a modeling approach that accommodates measurement errors because a direct algebraic approach was not robust and because error parameters were diff icult to estimate in our assessment model. The importance of measurement errors depends on many factors and should be evaluated on a case by case basis.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

Erros pré-analíticos em medicina laboratorial: uma revisão sistemática

MENEZES, Patrick Lourenço. Erros pré-analíticos em medicina laboratorial: uma revisão sistemática. 2013. 98 f. Dissertação (Mestrado em Saúde, Medicina Laboratorial e Tecnologia Forense) - Instituto de Biologia Roberto Alcântara Gomes, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, 2013. A relevância evidente dos erros pré-analíticos como problema de saúde pública fica patente tanto no dano potencial aos pacientes quanto nos custos ao sistema de saúde, ambos desnecessários e evitáveis. Alguns estudos apontam que a fase pré-analítica é a mais vulnerável a erros, sendo responsável por, aproximadamente, 60 a 90% dos erros laboratoriais em consequência da falta orientação aos pacientes sobre os procedimentos que serão realizados no laboratório clínico. Objetivos: Sistematizar as evidências científicas relacionadas aos erros pré-analíticos dos exames laboratoriais de análises clínicas. Método: Uma revisão sistemática foi realizada, buscando as bases de dados do Medical Literature Analysis and Retrieval System Online (MEDLINE), Scopus(que inclui MEDLINE e Embase), ISI Web of Knowledge, SciFinder, Literatura Latino-Americana e do Caribe em Ciências da Saúde (Lilacs) (que inclui a Scientific Electronic Library Online SciELO) e o Índice Bibliográfico Espanhol de Ciências de Saúde (IBECS), para artigos publicados entre janeiro de 1990 e junho de 2012 sobre erros de exames laboratoriais que possam ocorrer na fase pré-analítica. Os estudos foram incluídos de acordo com os seguintes exames laboratoriais: hemograma, análise bioquímica do sangue total ou do soro, exames de coagulação sanguínea,uroanálise e exames hematológicos ou bioquímicos em outros materiais e categorizados pelo tipo de erro pré-analítico e pela frequência dos incidentes. Resultados: A busca nas bases de dados bibliográficas resultou no seguinte número de artigos recuperados: 547 na MEDLINE, 229 na Scopus, 110 na ISI, 163 na SciFinder, 228 na Lilacs e 64 na IBECS, perfazendo um total de 1.341 títulos. Ao fim da revisão sistemática, obteve-se um conjunto de 83 artigos para leitura de texto completo, dos quais 14 foram incluídos na revisão. Os estudos abrangeram diferentes tipos de laboratórios, setores técnicos e origem de erros, segundo a fase do processo laboratorial. Discussão: Sete artigos demonstraram erros de pedidos médicos, com uma alta variabilidade nos valores de incidência. Os seis artigos que estudaram erros de coleta de amostra observaram redução deste desfecho. As proporções de eventos adversos relatados e os impactos clínicos variaram, levando a consequências descritas como: erros decorrentes da flebotomia, recoleta de amostras, repetições de exames, atrasos na liberação de resultados de exames e possíveis danos ao paciente. Conclusões: O laboratório deve ter instruções por escrito para cada teste, que descreva o tipo de amostra e procedimento de coleta de amostra. Meios de identificação por código de barras, sistemas robóticos e analíticos reduzem os erros pré-analíticos. A melhoria da fase pré-analítica de testes laboratoriais permanece um desafio para muitos laboratórios clínicos.