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RECIIS (Online) ; 16(3): 742-745, jul.-set. 2022.
Article in Portuguese | LILACS | ID: biblio-1399031


O livro A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la, de Sabina Leonelli, publicado pela Editora Fiocruz em 2022, explora em seus capítulos as definições do termo Big data e os seus impactos negativos na pesquisa científica. Em seguida, a autora revela uma nova abordagem epistemológica para o Big data e, por fim, apresenta um conjunto de propostas para a pesquisa científica. A revisão e atualização de definições, tanto quanto as importantes reflexões e os questionamentos por um uso consciente do Big data na pesquisa científica fazem com que a obra adicione importantes contribuições à biblioteca do pesquisador de informação e comunicação em saúde

The book titled A pesquisa científica na era do Big Data: cinco maneiras que mostram como o Big Data prejudica a ciência, e como podemos salvá-la [The scientific research in the age of Big Data: five ways that show how the Big Data harms the science, and how we can save it], by Sabina Leonelli, published in 2002, by Editora Fiocruz, explores in its chapters the definitions of Big Data and its negative impacts on scientific research. Then, the author reveals a new epistemological approach to Big data and finally she presents a set of proposals for developing a good scientific research. The literature review and updating of definitions as well as the important reflections and questions for a conscious use of Big data in scientific research make the work an important contribution to the researcher's library of the information and communication about health.

El libro denominado A pesquisa científica na era do Big data: cinco maneiras que mostram como o Big data prejudica a ciência, e como podemos salvá-la [La investigación científica en la era del Big data: cinco maneras que muestran como el Big data perjudica la ciencia, y como la salvar], de Sabina Leonelli, publicado en 2002, por la Editora Fiocruz, explora em sus capítulos las definiciones de Big data y sus impactos negativos en la investigación científica. A continuación, la autora revela un nuevo enfoque epistemológico del Big data y, al fin y al cabo, presenta un conjunto de propuestas para desarrollar una investigación científica de cualidad. La revisión de literatura y la actualización de las definiciones, así como las importantes reflexiones y discusiones para un uso consciente del Big data en la investigación científica, hacen de la obra un aporte importante a la biblioteca del investigador de la información y la comunicación acerca de la salud

Humans , Big Data , Science , Public Health , Database , Scientific Research and Technological Development , Health Communication , Data Science , COVID-19
Arq. neuropsiquiatr ; 80(5,supl.1): 342-347, May 2022. graf
Article in English | LILACS-Express | LILACS | ID: biblio-1393951


ABSTRACT Recent advances in technology have allowed us access to a multitude of datasets pertaining to various dimensions in neurology. Together with the enormous opportunities, we also face challenges related to data quality, ethics and intrinsic difficulties related to the application of data science in healthcare. In this article we will describe the main advances in the field of artificial intelligence and Big Data applied to neurology with a focus on neurosciences based on medical images. Real-World Data (RWD) and analytics related to large volumes of information will be described as well as some of the most relevant scientific initiatives at the time of this writing.

RESUMO Os recentes avanços na tecnologia nos permitiram acessar uma infinidade de conjuntos de dados pertencentes a várias dimensões da neurologia. Juntamente com as enormes oportunidades, também enfrentamos desafios relacionados à qualidade dos dados, ética e dificuldades intrínsecas relacionadas à aplicação da ciência de dados na área da saúde. Neste artigo descreveremos os principais avanços no campo da inteligência artificial e Big Data aplicados à neurologia com foco nas neurociências baseadas em imagens médicas. Dados do mundo real (RWD) e análises relacionadas ao grande volume de informações serão descritos, bem como algumas das iniciativas científicas mais relevantes no momento da redação deste artigo.

Article in Japanese | WPRIM | ID: wpr-936694


Real World Data (RWD) has various types of data sources, but each source has a different format and terminology code, which makes analysis process cumbersome and repetitive. The OMOP Common Data Model (CDM) is an open standard for analysis of RWD on a global scale, and the OHDSI community is responsible for its maintenance and development. What sets the OMOP CDM apart from other data standards is the way in which it has created a structure for integrating and handling terminology globally, and the way in which analysis is conducted without exposing individual patient information outside. Such features facilitate international collaboration. The method of not releasing patient data outside is expected to be widely utilized in future because it is highly compatible with Japan's pseudonymously processed information (PPI) based on the personal information protection act, in which PPI data cannot be provided to any third party but the purpose of use can be easily changed. There are many advantages not only for international collaboration, but also for domestic collaboration or in-house use. Epidemiologists and data scientists will be able to handle data in the same model they are accustomed to both domestically and internationally. That will be of great benefit to students, personnel, and their organizations especially when they study abroad, return home, or transfer internationally. Globally, collaborators from more than 70 countries are working on this project. Data on more than 800 million people after eliminating estimated duplicates, or 10% of the world's population, has been converted to the OMOP CDM. More than 250 related published articles have been registered with PubMed. On the other hand in Japan, there are many issues to be solved, such as support system and terminology mapping. To catch up with international levels, strong cooperation from a wide range of fields is needed.

Article in Chinese | WPRIM | ID: wpr-936414


@#With the arrival of the era of big data, increasing attention has been drawn to the application of artificial intelligence (AI) in the medical field. AI has many advantages, such as objectivity, accuracy, minimal invasiveness, time savings and high efficiency. Therefore, the combination of AI with dental diagnosis and treatment can help dentists improve work efficiency and save medical resources, offering potential significant benefits for dental application. At present, AI has been gradually integrated into prosthodontics, oral and maxillofacial surgery, orthodontics, endodontics and periodontics. The AI system can realize automatic tooth preparation, automatic tooth arrangement and implantology. Deep learning can be used to assist in diagnosing maxillary sinus inflammation, predicting the complications of tooth extraction and improving the accuracy of osteotomy. The AI system can also provide significant clues for the diagnosis, treatment and prognosis of oral and maxillofacial tumors. The breakthrough brought by AI in cephalometric and the assessment of facial attractiveness of patients has promoted the development of intelligent and personalized orthodontic treatment. Deep learning and analysis of medical images also promote the accuracy of root canal therapy as well as the diagnosis and treatment of periodontal diseases. AI technology has realized the leap from digitalization to automation and intelligence in oral diagnosis and treatment, and its application potential in the oral field should not be underestimated. Based on the concepts of AI, this paper will focus on the application of artificial intelligence in various oral clinical fields and briefly introduce its advantages, problems and future.

Article in English | WPRIM | ID: wpr-928940


Computational medicine is an emerging discipline that uses computer models and complex software to simulate the development and treatment of diseases. Advances in computer hardware and software technology, especially the development of algorithms and graphics processing units (GPUs), have led to the broader application of computers in the medical field. Computer vision based on mathematical biological modelling will revolutionize clinical research and diagnosis, and promote the innovative development of Chinese medicine, some biological models have begun to play a practical role in various types of research. This paper introduces the concepts and characteristics of computational medicine and then reviews the developmental history of the field, including Digital Human in Chinese medicine. Additionally, this study introduces research progress in computational medicine around the world, lists some specific clinical applications of computational medicine, discusses the key problems and limitations of the research and the development and application of computational medicine, and ultimately looks forward to the developmental prospects, especially in the field of computational Chinese medicine.

Algorithms , Computer Simulation , Humans
Article in Chinese | WPRIM | ID: wpr-940504


In the era of artificial intelligence based on big data, data acquisition, storage and processing are more convenient, which provides a guarantee for accelerating the development of traditional Chinese medicine (TCM), but it has not yet achieved organic integration with TCM theory. Based on preliminary research on the supramolecular "Qi chromatography" theory of TCM, combined with the current development trend of artificial intelligence, this paper analyzed the biological intelligence attribute of the function of TCM supramolecular "imprinting template", in order to provide reference for the development of TCM drug innovation. Both the human body and Chinese materia medica are giant complex supramolecular bodies evolved from natural organisms. According to the "imprinting template", the "social molecules" are controlled step by step to form the meridians and viscera. The interaction produces the original theory of TCM, in which the self-recognition, self-assembly, self-organization and self-replication of the "imprinting template" reflect the "intelligence" function attributes:the human body uses the "imprinting template" to self-identify and sense the ingredients of TCM, and store the memory information database in the meridian and collateral organs in the form of "imprinting template", and then pass the "imprinting template". The comparison, analysis, and judgment of imprinting templates guide the self-assembly, self-organization and self-replication among "molecular society", synthesize biological machines, produce biological functions, repair or strengthen biological supramolecular bodies, and present the most basic "intelligence" attribute. This suggests that the theory of theory-method-prescription-medicine of TCM is the weak embodiment of biological "intelligence", while the human brain function is the strong embodiment of biological "intelligence". Since the intelligent function of supramolecular "imprinting template" runs through the natural world, artificial intelligence that can characterize the strong "intelligence" form of the human brain will also be integrated into all aspects of the natural world, suggesting the development direction of "intelligence" functionalization of drug innovation mode.

Article in Chinese | WPRIM | ID: wpr-934581


Deep integration of healthcare and prevention in public hospitals is not only a basic function played by hospitals in their public health services, but also an inevitable choice to meet the health needs of the people in their life span. The authors analyzed the current situation in healthcare and prevention integration in Wuhan in recent years, focusing on such problems existing in the construction of healthcare and prevention integration in public hospitals, as unclear functional positioning of medical prevention integration in public hospitals, insufficient refinement of healthcare and prevention integration policies, delay in the construction of public health informatization, and poor public health awareness of medical personnel. In view of the above problems, the authors put forward the following improvement suggestions: optimizing the policy environment of healthcare and prevention integration, strengthening the leading role of the hospital management, building a hospital public health big data platform, mobilizing the initiative of clinical technicians, and improving the work identity of hospital public health workers.

Article in Chinese | WPRIM | ID: wpr-931420


Objective:To discuss the practice and application of "artificial intelligence + big data" in the construction of thoracic surgery golden course.Methods:The intern students of the Department of Thoracic Surgery in Harbin Medical University Cancer Hospital were selected as the research objects, and they were randomly divided into 2 groups with 36 cases in each group. The control group was taught with regular courses, and the observation group was taught by the golden course system under "artificial intelligence + big data". After the course, self-made assessment forms were used to assess the academic performance (theoretical knowledge assessment results and skill operation assessment results) of the two groups of medical students. The excellent and good rate of knowledge mastery and the mastery of clinical operation techniques were scored by the teachers, and the evaluation was made from the aspects of learning attitude, the mastery degree of theoretical knowledge and clinical operational techniques, etc. In addition, self-made innovative thinking ability scale was used to assess the medical students. SPSS 22.0 was used for independent samples t test and chi-square test. Results:There was no statistically significant difference between the two groups of theoretical knowledge assessment scores and skill operation assessment scores before the teaching; after the course, the theoretical knowledge assessment scores and skill operation assessment scores of the control group were higher than those before the teaching, with statistically significant differences ( t=5.37, 4.17, P<0.05). After the course, the theoretical knowledge assessment scores and skill operation assessment scores of the observation group were higher than those before the teaching, with significant differences ( t=10.93, 8.24, P<0.05). The results of theoretical knowledge assessment and skill operation assessment in the observation group were significantly higher than those in the control group after the course ( t=7.10, 5.77, P<0.05). In the control group, 17 cases were excellent in knowledge mastery, accounting for 47.22%, and the rate of knowledge mastery was 83.33% (30/36); in the observation group, 26 cases were excellent in knowledge mastery, accounting for 72.22%, and the excellent and good rate of knowledge mastery was 100% (36/36), and the difference was statistically significant ( χ2=4.55, P=0.033). After the course, the innovative thinking ability of the control group was higher than that before the teaching, the innovative thinking ability of the observation group was higher than that before the teaching, and the innovative thinking ability of the observation group was higher than that of the control group, and the difference was statistically significant ( t=7.07, P<0.001). Conclusion:The use of the "artificial intelligence + big data" golden course to build a teaching system can improve the academic performance, knowledge mastery and innovative thinking ability of medical students.

Article in Chinese | WPRIM | ID: wpr-931412


In order to cultivate talents who fuel and lead medical innovation, it is critical to implement medical English writing "Golden Course" in the substantial development of English teaching in medical universities. Based on the guidelines issued by the Ministry of Education, this paper proposes five principles in the construction of medical English writing "Golden Course", suggests the integration of big data technology, and explores approaches to construct "Golden Course" in terms of teaching objectives, curriculum design, models of instruction, curriculum evaluation and teaching staff.

Article in Chinese | WPRIM | ID: wpr-930914


Biliary surgery has a long history. Since the Renaissance era, countless predece-ssors began to understand the biliary ducts, developed from cholecystectomy to extrahepatic bile duct exploration, and made significant progress. Academician Zhiqiang Huang firstly applied hepatectomy to the treatment of complex hepatolithiasis, and developed biliary surgery from extrahepatic bile duct to intrahepatic bile duct. The establishment and maturity of the "Precision biliary surgery technology system" marks the era of "segment" in biliary surgery. However, due to the many uncertainties of the concept and technology in the entire diagnosis and treatment process, biliary surgery is still one of the most complicated areas in abdominal surgery, and the prognosis is in urgent need of improvement. In the future, biliary surgery will make a breakthrough from the current hepatic segment to molecular level. Surgeons will cooperate with experts in various fields, make medical decisions based on big data and artificial intelligence, and perform more precise surgeries.

Goiânia; SES-GO; 2022. 1-132 p. ilus, graf, tab, fotos.(Gestão e inovação em tempos de pandemia: um relato de experiência à frente da SES-GO, 1).
Monography in Portuguese | LILACS, ColecionaSUS, CONASS, SES-GO | ID: biblio-1400208


Este e-book tem como objetivo trazer um compêndio de relatos de experiência relacionados à gestão de saúde do Estado de Goiás. Cada capítulo traz a descrição dos projetos desenvolvidos no âmbito da Secretaria de Estado da Saúde de Goiás, que são vinculados aos objetivos estratégicos do órgão. Estes projetos têm como objetivo fortalecer as ações estratégicas para otimizar o planejamento do Sistema Único de Saúde

This e-book aims to bring a compendium of experience reports related to health management in the State of Goiás. Each chapter brings a description of the projects developed within the scope of the State Department of Health of Goiás, which are linked to the strategic objectives of the agency. These projects aim to strengthen strategic actions to optimize the planning of the Unified Health System

Health Management , Public Health Administration , State Health Plans , Health Programs and Plans , Social Control Policies , Health Services Administration , Crew Resource Management, Healthcare , Health Policy
Einstein (Säo Paulo) ; 20: eAO6613, 2022. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1375329


ABSTRACT Objective To analyze the most common ophthalmologic disorders in pregnant women seen in a hospital in Munich in Germany using a big data analysis system, as well as to compare the results obtained with those from other epidemiological studies that used different data acquisition methods. Methods We retrospectively analyzed electronic health records of pregnant women who were seen at the ophthalmology department from 2003 to 2019 at the Ludwig-Maximilians-Universität München hospital. The main complaints that led to ophthalmic consultations during this period were evaluated, and also the variation in intraocular pressure of patients throughout gestational trimesters by analyzing data from the data warehouse system. Results A total of 27,326 electronic health records were analyzed. Of participants, 149 (0.54%) required eye care during pregnancy. Their mean intraocular pressure was 17mmHg in the first trimester, 12mmHg in the second trimester, and 14mmHg in the third trimester. The most prevalent findings were dry eye (29.3%) and conjunctivitis (16%), and ametropia (16%). The most common posterior segment problem was diabetic retinopathy (4.6%). The lower mean intraocular pressure in the second and third trimester found in our study is in accordance with other studies that used other method for data acquisition. Conclusion The most common ophthalmic conditions found in this study population were dry eye, conjunctivitis, and ametropia. The use of data warehouse proved to be useful for acquiring and analyzing data from many patients. This study results are comparable with other studies in published literature that adopted different methodology.

Einstein (Säo Paulo) ; 20: eAO6224, 2022. graf
Article in English | LILACS-Express | LILACS | ID: biblio-1375363


ABSTRACT Objective Headache and rhinitis are highly prevalent and comorbid. The objective of the present study is to analyze the correlation of headache and rhinitis, in addition to the temporal pattern of these diseases in 17 years, using the Google Trends platform. Methods Google Trends was searched from January 2004 to June 2021, using the entry: ["rinite" (rhinitis) + "dor de cabeça" (headache)" + "Alzheimer" + "enxaqueca" (migraine)]. Migraine, primary headache, and Alzheimer's, with no clear relation with headache, were used as controls. After the descriptive analysis by dispersion diagrams, Pearson's test and a simple regression model were performed. Subsequently, this study analyzed the seasonality of the volume of research on rhinitis and headache. Results A strong correlation between rhinitis and headache (0.86) was found in the time interval analyzed. In addition, a seasonality was identified in the volume of searches for the term rhinitis with increased volume in the fall and peaks in the month of May, with a decrease in the spring and early summer. Moreover, an increase of searches on headache was observed, suggesting worse burden of this pathology. Conclusion Headaches and rhinitis were correlated in 17 years of research on the Google Trends platform. Circannual variation of both conditions was observed. Additional studies with digital research may be useful to better understand the epidemiology and comorbidities of headache.

J. vasc. bras ; 21: e20210215, 2022. tab, graf
Article in English | LILACS | ID: biblio-1394424


Abstract Background Worldwide, peripheral arterial disease (PAD) is a disorder with high morbidity, affecting more than 200 million people. Objectives Our objective was to analyze surgical treatment for PAD provided on the Brazilian Public Healthcare System over 12 years using publicly available data. Methods The study was conducted with analysis of data available on the Brazilian Health Ministry's database platform, assessing distributions of procedures and techniques over the years and their associated mortality and costs. Results A total of 129,424 procedures were analyzed (performed either for claudication or critical ischemia, proportion unknown). The vast majority of procedures were endovascular (65.49%) and this disproportion exhibited a rising trend (p<0.001). There were 3,306 in-hospital deaths (mortality of 2.55%), with lower mortality in the endovascular group (1.2% vs. 5.0%, p=0.008). The overall governmental expenditure on these procedures was U$ 238,010,096.51, and endovascular procedures were on average significantly more expensive than open surgery (U$ 1,932.27 vs. U$ 1,517.32; p=0.016). Conclusions Lower limb revascularizations were performed on the Brazilian Public Healthcare System with gradually increasing frequency from 2008 to 2019. Endovascular procedures were vastly more common and were associated with lower in-hospital mortality rates, but higher procedure costs.

Resumo Contexto A doença arterial periférica (DAP) é uma doença com alta morbidade global, afetando mais de 200 milhões de pessoas. Objetivos Neste estudo, analisamos o tratamento cirúrgico para DAP no sistema público de saúde do Brasil no período de 12 anos, com base em dados publicamente disponíveis. Métodos O estudo foi conduzido a partir da análise de dados disponíveis na plataforma do Departamento de Informática do Sistema Único de Saúde (DATASUS), do Ministério da Saúde, avaliando a distribuição da técnica cirúrgica utilizada, a mortalidade e o custo ao longo dos anos. Resultados Um total de 129.424 procedimentos foram analisados (para claudicantes e isquemia crítica, em proporção desconhecida). A maiora dos procedimentos foi via endovascular (65,49%), com tendência de aumento nessa desproporção (p < 0,001). Houve 3.306 mortes intra-hospitalares (mortalidade de 2,55%) com menor mortalidade no grupo endovascular (1,2% vs. 5,0%; p = 0,008). O investimento governamental total para esses procedimentos foi de US$ 238.010.096,51, e os procedimentos endovasculares foram significativamente mais caros que a cirurgia aberta convencional (US$ 1.932,27 vs. US$ 1.517,32; p = 0,016). Conclusões No sistema público de saúde brasileiro, as revascularizações de membros inferiores ocorreram com frequência crescente entre 2008 e 2019. Os procedimentos endovasculares foram mais comuns e relacionados a menor mortalidade intra-hospitalar, mas a maiores custos.

Humans , Vascular Surgical Procedures/statistics & numerical data , Peripheral Arterial Disease/surgery , Vascular Surgical Procedures/methods , Brazil , Retrospective Studies , Hospital Mortality , Costs and Cost Analysis , Big Data
Rev. Assoc. Med. Bras. (1992) ; 67(7): 937-941, July 2021. graf
Article in English | LILACS | ID: biblio-1346954


SUMMARY OBJECTIVE: To analyze the public data of hysterectomies performed in the only health system in the city of São Paulo between 2008 and 2018. METHODS: The following public health system data were extracted and analyzed: age, technique, number of surgeries, mortality during hospitalization, length of stay in the establishment (days), and amounts paid by the public network. RESULTS: A total of 20,119 procedures were analyzed. The most prevalent procedure was total hysterectomy (43.2%), followed by vaginal hysterectomy (26.7%), subtotal hysterectomy (24.3%), and laparoscopic hysterectomy (5.8%). Early discharge (hospital stay of up to 1 day) was more prevalent in cases of vaginal hysterectomy (39%). We observed a marked downward trend in the number of total hysterectomies. Total hysterectomy was the most expensive procedure; no significant difference was noted in the cost of vaginal versus laparoscopic hysterectomy. We noticed a trend of rising costs over the years. The most frequent hospital admission code was that of leiomyoma of the uterus in cases of total, subtotal, and laparoscopic hysterectomy. CONCLUSION: Despite the decrease in the number of hysterectomies over the 11-year study period in São Paulo, it remains in high demand mainly for the treatment of uterine leiomyomatosis. Laparoscopic hysterectomy has been gaining ground and showed a slightly upward trend with a shorter hospital stay. Laparoscopic and vaginal hysterectomy required less financial support from the health system than open surgery.

Humans , Female , Public Health , Laparoscopy , Brazil/epidemiology , Retrospective Studies , Hysterectomy , Hysterectomy, Vaginal
Cad. Ibero Am. Direito Sanit. (Impr.) ; 10(1): 93-112, jan.-mar.2021.
Article in Portuguese | LILACS-Express | LILACS | ID: biblio-1151016


Objetivo: O Sistema Único de Saúde (SUS) vem investido nas tecnologias da internet das coisas ­ Internet of Things (IoT), em inglês ­ para coletar dados dos pacientes. Esse artigo aponta as fragilidades quanto à privacidade de usuários do SUS e propor uma solução teórica, ainda a ser testada a partir de uma infraestrutura pautada em armazenamento pessoal de dados ­ personal data stores (PDS), em inglês ­ ou, a partir da segurança da blockchain. Metodologia: realizou-se revisão narrativa da literatura nacional e internacional relacionados a instrumentos, políticas e casos voltados a tecnologias de informação e comunicação na saúde a fim de apontar as fragilidades quanto à privacidade de usuários desse sistema. Resultados: percebeu-se que ainda existe uma falta de transparência no tratamento dos dados pessoais e pouco accountability por parte dos cidadãos, se fazendo necessária uma mudança de estratégia tecnológica e de governança. Conclusão: o PDS, de fato, empodera o usuário na medida que dá maior controle e transparência sobre o tratamento de seus dados. No entanto, essa solução, em um sistema como o utilizado pelo Departamento de Informática do SUS, pode comprometer a precisão dos dados usados nas políticas públicas, ao mesmo tempo que pode comprometer alguns direitos dos cidadãos, pois são dados salvos em registros e os metadados estão disponíveis publicamente. A implementação do PDS ainda não possui perspectiva de resultado ótimo. Ainda existem algumas restrições metodológicas quanto aos direitos dos cidadãos ou à eficiência do Estado, mas é um passo no empoderamento civil e uma melhoria exigida por lei quanto à privacidade e à proteção de dados pessoais.

Objective: Brazilian Unified Health System (SUS, in Portuguese) has invested in Internet of Things (IoT) technologies to collect data from patients. This article aims to point out the weaknesses regarding the privacy of users of the SUS and to propose a theoretical solution, yet to be evaluated, and based on a Personal Data Storages (PDS) infrastructure or on blockchain security. Methods: aA narrative review of national and international literature related to instruments, policies, and cases related to information and communication technologies in health was conducted to point out the weaknesses regarding the privacy of users of this system. Results: there is still a lack of transparency in the treatment of personal data and little accountability on the part of citizens, making it necessary to change the technological and governance strategy. Conclusion: PDS empowers users as it gives greater control and transparency over the treatment of data. However, this solution, in a system like the one used by their Computer Department, can compromise the accuracy of the data used in public policies, while it can compromise some citizens' rights, as this data is saved in records and the metadata is publicly available. The implementation of a solution like this does not yet have the prospect of an optimal result, without any methodological restriction on citizens' rights or the efficiency of the State, but it is a step in civil empowerment and an improvement required by law concerning privacy and protection of personal data. The implementation of the PDS does not yet have the prospect of an optimal result. There are still methodological restrictions regarding the rights of citizens or the efficiency of the State. But it is a step in civil empowerment and an improvement required by law in terms of privacy and the protection of personal data.

Objetivo: el Sistema Único de Salud de Brasil (SUS) ha invertido en tecnologías de Internet de las cosas (IoT) para recopilar datos de los pacientes. Este artículo tiene como objetivo señalar las debilidades con respecto a la privacidad de los usuarios del SUS y proponer una solución teórica, aún por probar basada en una infraestructura basada en Personal Data Storages (PDS) o almacenamiento personal de datos, basado en la seguridad de blockchain. Metodología: se realizó una revisión narrativa de la literatura nacional e internacional relacionada con instrumentos, políticas y casos relacionados con las tecnologías de la información y la comunicación en salud con el fin de señalar las debilidades en cuanto a la privacidad de los usuarios de este sistema. Resultados: se notó, entonces, que aún existe falta de transparencia en el tratamiento de estos datos personales y poca rendición de cuentas por parte de la ciudadanía, por lo que es necesario cambiar la estrategia tecnológica y de gobernanza. Conclusión: se concluye que PDS, de hecho, empodera a los usuarios ya que brinda un mayor control y transparencia sobre el tratamiento de sus datos. Sin embargo, esta solución, en un sistema como el utilizado por el Departamento de Computación del SUS, puede comprometer la precisión de los datos utilizados en las políticas públicas, al mismo tiempo que puede comprometer algunos derechos civiles, ya que estos datos se guardan en registros y metadatos están disponibles públicamente. La implementación de una solución como esta todavía no tiene la perspectiva de un resultado óptimo, sin ninguna restricción metodológica sobre los derechos de los ciudadanos o la eficiencia del Estado, pero es un paso en el empoderamiento civil y una mejora requerida por la ley con respecto a la privacidad y protección de datos personales. La implementación del PDS aún no tiene la perspectiva de un resultado óptimo. Aún existen algunas restricciones metodológicas en cuanto a los derechos de los ciudadanos o la eficiencia del Estado. Pero es un paso en el empoderamiento civil y una mejora requerida por la ley en términos de privacidad y protección de datos personales.

Rev. méd. Urug ; 37(4): e37413, 2021.
Article in Spanish | LILACS-Express | LILACS, BNUY, UY-BNMED | ID: biblio-1389654


Resumen: El screening mamográfico ha ayudado a identificar el cáncer de mama en sus estadíos más tempranos, cuando los tratamientos son más efectivos. El empleo de la inteligencia artificial (IA) en el análisis de los mamogramas ha demostrado ser capaz de superar la habilidad del ojo humano para detectar lesiones en la mama sospechosas de cáncer. El objetivo del presente trabajo es realizar un aporte reflexivo sobre el avance de la tecnología digital y en particular de la IA en los screening mamográficos, desde el punto de vista técnico y bioético. Se analizan ventajas y limitaciones de la IA, explicando cómo se produce el aprendizaje de los sistemas computacionales. Se propone un debate bioético sobre cuestiones tales como la privacidad, la credibilidad, la responsabilidad y la educación permanente. Se resalta la importancia de establecer canales de diálogo entre todas las partes involucradas en la incorporación de las nuevas tecnologías en medicina.

Abstract: Mammographic screening has helped to identify breast cancer in its earliest stages, when treatment is most effective. The use of Artificial Intelligence in the analysis of mammograms has proved to be able to excel the human eye in detecting lesions in the breast that may be suspicious for cancer. The objective of this study is to make a reflective contribution on the advancement of digital technology and in particular, Artificial Intelligence in mammographic screening, from the technical and bioethical points of view. Advantages and limitations of Artificial Intelligence are analyzed explaining how machine learning occurs. A bioethical debate is proposed on issues such as privacy, credibility, accountability and continuous education. The importance of establishing channels of dialogue between all stakeholders in the incorporation of new technologies in medicine is highlighted.

Resumo: O rastreamento mamográfico ajuda a identificar o câncer de mama em seus estágios iniciais, quando os tratamentos são mais eficazes. O uso da Inteligência Artificial (AI) na análise de mamografias tem se mostrado capaz de superar a capacidade do olho humano em detectar lesões na mama suspeitas de câncer. O objetivo deste trabalho é fazer uma contribuição reflexiva sobre o avanço da tecnologia digital e, em particular, a AI em mamografia, do ponto de vista técnico e bioético. As vantagens e limitações da AI são analisadas explicando como o aprendizado de sistemas computacionais é feito. Propõe-se um debate bioético sobre questões como privacidade, credibilidade, responsabilidade e educação ao longo da vida. Destaca-se a importância do estabelecimento de canais de diálogo entre todas as partes envolvidas na incorporação de novas tecnologias na medicina.

Bioethics , Breast Neoplasms/diagnostic imaging , Artificial Intelligence/ethics , Mammography , Mass Screening
Article in Chinese | WPRIM | ID: wpr-880470


Based on 18 hospitals including the Chinese People's Liberation Army General Hospital and Peking University People's Hospital, and based on the "Specifications for Perioperative Data", explore the construction and application of perioperative multi-center data centers in the era of medical big data. The use of data ferry technology avoids hidden safety hazards in hospitals, realizes the integration and sharing of perioperative medical data of various medical institutions, and forms a complete data chain combining patient medical data and follow-up data.

Hospitals, Military , Humans , Military Personnel , United States
Article in Chinese | WPRIM | ID: wpr-876477


Objective To investigate the epidemiological characteristics of acute myocardial infarction (AMI) in Yichang City in the last 5 years, and to provide a basis for targeted prevention and treatment. Methods The annual estimated percentage was used to evaluate the trend of morbidity and mortality of AMI by using the monitoring data from 2015 to 2019 from the health big data platform of Yichang. Results There were 1 976 new cases of AMI in Yichang from 2015 to 2019, with a crude morbidity of 41.96/100 000, and standardized morbidity of 87.52/100 000. Among them the crude incidence rate in males was 57.69/100 000, and 29.84/100 000 in females. The difference was statistically significant (χ2=15.76, P2=45.65, P<0.001). The morbidity and mortality of males and females were increased with age. Conclusion From 2015 to 2019, the morbidity of AMI in Yichang was at a moderately low level in China, but the mortality was higher than the national average. The morbidity showed an upward trend, with men and elderly people aged ≥60 being more serious. Appropriate intervention measures should be taken for different groups of people to reduce the incidence of AMI.

Journal of Medical Biomechanics ; (6): E984-E989, 2021.
Article in Chinese | WPRIM | ID: wpr-920714


Objective To study the research progress of big data analysis in gait biomechanics. Methods Based on the scientific and technological literature related to big data analysis in gait biomechanics during the year 2011-2020 as the research object, content analysis method was used to analyze and discuss from four aspects, including topic structure, hierarchy level, model type and analysis technology. On this basis, the future research of gait biomechanics big data analysis was prospected. Results The application of big data analysis in gait biomechanics mainly involves five research directions, namely, intervention and rehabilitation, exercise training, prosthesis design and evaluation, understanding of etiology and diagnosis, understanding of human movement characteristics. Big data analysis in gait biomechanics is divided into three levels, of which descriptive analysis is the most used type, accounting for about 41%. The models and specific techniques of big data analysis in gait biomechanics field were reviewed. Topological data analysis is a promising big data exploration tool for future research. Conclusions Big data technology has great potential in gait biomechanics and clinical medicine research.