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1.
Rev. bioét. derecho ; (50): 315-331, nov. 2020.
Artículo en Español | IBECS | ID: ibc-191360

RESUMEN

La inteligencia artificial y el Big Data se articulan para poder lidiar con diferentes problemas relacionados con el análisis de datos masivos, en particular información de la COVID-19. En el presente artículo se muestran algunos proyectos de investigación relacionados con el aprendizaje profundo, el aprendizaje automático, el Big Data y la ciencia de datos, tendientes a dar soluciones plausibles bien en el monitoreo, detección, diagnóstico y tratamiento de las enfermedades asociadas con el virus. Con esto en mente, se muestra la correspondencia entre las tecnologías disruptivas y la información crítica, creando sinergias que permiten elaborar sistemas más avanzados de estudio y análisis facilitando la obtención de datos relevantes para la toma de decisiones sanitarias


Artificial intelligence and Big Data are articulated to be able to deal with different problems related to the analysis of big data, in particular, information from the COVID-19. In this sense, this article shows some research projects related to deep learning, machine learning, Big Data and data science, aimed to provide plausible solutions in monitoring, detection, diagnosis and treatment of diseases associated with the virus. The correspondence between disruptive technologies and critical information is shown, creating synergies that allow the development of more advanced systems of study and analysis, facilitating the obtaining of relevant data for health decision-making


La Intel·ligència Artificial I el Big Data s'articulen per poder fer front a diferents problemes relacionats amb l'anàlisi de dades massiu, concretament, informació relativa a la COVID-19. En aquest sentit, en el present article es mostren alguns projectes d'investigació relacionats amb l'aprenentatge profund, l'aprenentatge automàtic, el Big Data I la ciència de dades, capaços de donar solucions plausibles en el monitoratge, detecció, diagnòstic I tractament de les malalties associades amb el virus. Amb això en ment, es mostra la correspondència entre les tecnologies disruptives I la informació crítica, creant sinergies que permeten elaborar sistemes més avançats d'estudi I anàlisi facilitant l'obtenció de dades rellevants per a la presa de decisions sanitàries


Asunto(s)
Humanos , Inteligencia Artificial , Macrodatos , Infecciones por Coronavirus/epidemiología , Neumonía Viral/epidemiología , Pandemias , Toma de Decisiones , Betacoronavirus , Predicción
2.
Zhonghua Yi Xue Za Zhi ; 100(40): 3157-3160, 2020 Nov 03.
Artículo en Chino | MEDLINE | ID: mdl-33142398

RESUMEN

Objective: To investigate the feasibility and clinical significance of a continuous auscultation recorder of bowel sounds based on artificial intelligence in monitoring the bowel sounds. Methods: From November 1,2018 to August 12,2019, a continuous auscultation recorder of bowel sounds was applied to monitor the perioperative bowel sounds of 31 patients undergoing colorectal surgery, in order to discovery underlying rules which might be used to guide clinical practice. Results: After the operation, the bowel sounds continued to exist for (1.8±0.8) h, and then gradually weakened or disappeared, and recovered gradually after (11.2±3.5) h. The first exhaust and the first defecation were detected at the time of (22.7±5.8) h and (28.7±6.9) h after surgery, respectively. The bowel sounds rate increased after eating, and decreased significantly after exhaust/defecation. Conclusions: The continuous auscultation recorder of bowel sounds based on artificial intelligence was safe and effective, which can afford help to clinical evaluation.


Asunto(s)
Inteligencia Artificial , Auscultación , Motilidad Gastrointestinal , Humanos , Monitoreo Fisiológico
3.
Orthod Fr ; 91(1-2): 101-114, 2020 Jun 01.
Artículo en Inglés | MEDLINE | ID: mdl-33146125

RESUMEN

We could study Cone Beam documents of patients consulting in ORL with standard Angle Class I occlusion (45 ND), patients consulting in orthodontics with an orthodontic Class II (51 APNS) and patients with a surgical Class II (83 APS). The used 3D biometry calculates systematically a 164 set of parameters able to take into account all kinds of disharmonies; among which 38 parameters are specifically devoted to anterior-posterior "off asymmetry" pathologies. Then the specific Artificial Intelligence (AI) programs treat morphological data and give textual diagnoses. Analysis of the global sample aims to control the efficiency, separating different sub-samples one each other: t test appreciates efficiency of each parameter to recognize clinical sub-sample. The correlation coefficient, r, between each parameter and pseudo Angle molars Class II (GMMy-Gmmy) give the importance of its tie with Class II pathology. Presentation of parameters medium values in each sub-group gives the medium profiles. By direct comparison of patient's parameters values with medium profile, it is possible to locate patient's pathology. So we can take in account new parameters like arches upper/lower gap, anterior bases upper/lower gap, compensatingparameters... It is then possible to make more secure the clinical decision.


Asunto(s)
Inteligencia Artificial , Maloclusión de Angle Clase II , Biometría , Cefalometría , Humanos , Maloclusión de Angle Clase II/diagnóstico por imagen , Maloclusión de Angle Clase II/terapia , Cráneo
4.
Orthod Fr ; 91(1-2): 139-144, 2020 Jun 01.
Artículo en Inglés | MEDLINE | ID: mdl-33146128

RESUMEN

Artificial Intelligence (AI) consists of setting different technics together aimed at allowing machines to simulate human cognitive fonctions, mimic human brain functions, sometime its logic, when it comes to answer to an interrogation, to take decisions or to anticipate events. This new fonction, after being used in numerous daily life domains (geo-guides, personal assistants, administratif procedures) comes now in the medical area. The press exaggerations on those systems doesn't have any wise and thoughtful judgment. This article will talk about the question of the real uses and expertise capacities which the AI should be able to provide in our area. Through the history of cognitive science and ideas, the recension of important works on the AI developments, we want to put in perspective the promises and opportunities provided to modify or complete the expertise in orthodontics. The willingness to extend cognitive and action abilities is older than what the comma historiography of the AI let us think. The recent development of computer systems, algorithmic science and databases allowed the development of a branch of the artificial intelligent giving, in some cases, seemingly undeniable results which should not be extrapolated because of the weakness of our databases, the current economic model and their real use.


Asunto(s)
Inteligencia Artificial , Inteligencia , Humanos
5.
Nat Commun ; 11(1): 5668, 2020 11 09.
Artículo en Inglés | MEDLINE | ID: mdl-33168827

RESUMEN

Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to model transportability. Here, we leverage the US PCORnet platform to develop an AKI prediction model and assess its transportability across six independent health systems. Our work demonstrates that cross-site performance deterioration is likely and reveals heterogeneity of risk factors across populations to be the cause. Therefore, no matter how accurate an AI model is trained at the source hospital, whether it can be adopted at target hospitals is an unanswered question. To fill the research gap, we derive a method to predict the transportability of AI models which can accelerate the adaptation process of external AI models in hospitals.


Asunto(s)
Lesión Renal Aguda/etiología , Inteligencia Artificial , Aprendizaje Automático , Lesión Renal Aguda/sangre , Adolescente , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Curva ROC , Medición de Riesgo , Factores de Riesgo , Adulto Joven
6.
Front Immunol ; 11: 585647, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-33133104

RESUMEN

Cytokine storm resulting from SARS-CoV-2 infection is one of the leading causes of acute respiratory distress syndrome (ARDS) and lung fibrosis. We investigated the effect of inflammatory molecules to identify any marker that is related to lung fibrosis in coronavirus disease 2019 (COVID-19). Seventy-six COVID-19 patients who were admitted to Youan Hospital between January 21 and March 20, 2020 and recovered were recruited for this study. Pulmonary fibrosis, represented as fibrotic volume on chest CT images, was computed by an artificial intelligence (AI)-assisted program. Plasma samples were collected from the participants shortly after admission, to measure the basal inflammatory molecules levels. At discharge, fibrosis was present in 46 (60.5%) patients whose plasma interferon-γ (IFN-γ) levels were twofold lower than those without fibrosis (p > 0.05). The multivariate-adjusted logistic regression analysis demonstrated the inverse association risk of having lung fibrosis and basal circulating IFN-γ levels with an estimate of 0.43 (p = 0.02). Per the 1-SD increase of basal IFN-γ level in circulation, the fibrosis volume decreased by 0.070% (p = 0.04) at the discharge of participants. The basal circulating IFN-γ levels were comparable with c-reactive protein in the discrimination of the occurrence of lung fibrosis among COVID-19 patients at discharge, unlike circulating IL-6 levels. In conclusion, these data indicate that decreased circulating IFN-γ is a risk factor of lung fibrosis in COVID-19.


Asunto(s)
Infecciones por Coronavirus/complicaciones , Interferón gamma/sangre , Neumonía Viral/complicaciones , Fibrosis Pulmonar/etiología , Anciano , Inteligencia Artificial , Biomarcadores/sangre , Estudios de Cohortes , Infecciones por Coronavirus/sangre , Infecciones por Coronavirus/diagnóstico por imagen , Infecciones por Coronavirus/inmunología , Estudios Transversales , Femenino , Humanos , Inflamación/inmunología , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/sangre , Neumonía Viral/diagnóstico por imagen , Neumonía Viral/inmunología , Fibrosis Pulmonar/sangre , Fibrosis Pulmonar/diagnóstico por imagen , Factores de Riesgo , Tomografía Computarizada por Rayos X
7.
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue ; 32(10): 1155-1159, 2020 Oct.
Artículo en Chino | MEDLINE | ID: mdl-33198854

RESUMEN

Through the big data intelligent algorithm and application of artificial intelligence in critically ill patients, the value of the combination of clinical real-time warning and artificial intelligence in critical care medicine was explored. Artificial intelligence was used to simulate human thinking by studying, calculating, and analyzing a large amount of critical illness data in the medical work, and integrate a large number of clinical monitoring and treatment data generated in critical care medicine. The necessity, feasibility, relevance, data learning and application architecture of the application of artificial intelligence in the early warning of critical illness in medical work were analyzed, thus to promote the pioneering application of real-time warning of critical illness in clinical medicine. The development of critical care medicine in medical work requires the integration of big data and artificial intelligence. Through real-time early warning, accurate and scientific intelligent application of medical data, the life threatening uncertainties in the diagnosis and treatment of critically ill patients can be more effectively reduced and the success rate of the treatment of critically ill patients can be improved. The perfect combination of artificial intelligence technology and big data of critical care medicine can provide a favorable guarantee for the pioneering application of real-time warning of critical care medicine in clinical work.


Asunto(s)
Inteligencia Artificial , Enfermedad Crítica , Macrodatos , Cuidados Críticos , Humanos
8.
Rhinology ; 58(5): 522-523, 2020 Oct 01.
Artículo en Inglés | MEDLINE | ID: mdl-33130830

RESUMEN

Social distancing with the aim of avoiding infections and pre-serve critical care capacities during the COVID-19 pandemic has been implemented in Germany according to World Health Organization (WHO) recommendations from early March onwards. Limitations of physical contacts to reduce exposure to SARS-CoV-2 infected individuals were handled strictly, particularly in medical centers dealing with airway diseases, like rhinology and pneumology clinics. Such measures and reluctance to visit out- and inpatient services resulted in a 82% decrease in consultations to the 12 German oto-rhino-laryngological (ORL) centres forming our database during the 50 days following March 09 in 2020 if compared to the same period in 2019. Our data on CRS care underline reports on undertreatment of non-COVID-19 individuals with several different diseases during the current pandemic. We should try to reduce the toll these patients have to pay as much as possible. We established telemedicine, e-Health and artificial intelligence-supported triage for selecting the right patients for onsite-consultations and to advise patients in several demands.


Asunto(s)
Inteligencia Artificial , Infecciones por Coronavirus , Pandemias , Neumonía Viral , Rinitis/diagnóstico , Rinitis/terapia , Sinusitis/diagnóstico , Sinusitis/terapia , Betacoronavirus , Enfermedad Crónica , Alemania/epidemiología , Humanos , Otolaringología/tendencias , Telemedicina
9.
Zhonghua Bing Li Xue Za Zhi ; 49(11): 1120-1125, 2020 Nov 08.
Artículo en Chino | MEDLINE | ID: mdl-33152815

RESUMEN

Objective: To establish an artificial intelligence (AI)-assisted diagnostic system for lung cancer via deep transfer learning. Methods: The researchers collected 519 lung pathologic slides from 2016 to 2019, covering various lung tissues, including normal tissues, adenocarcinoma, squamous cell carcinoma and small cell carcinoma, from the Beijing Chest Hospital, the Capital Medical University. The slides were digitized by scanner, and 316 slides were used as training set and 203 as the internal test set. The researchers labeled all the training slides by pathologists and establish a semantic segmentation model based on DeepLab v3 with ResNet-50 to detect lung cancers at the pixel level. To perform transfer learning, the researchers utilized the gastric cancer detection model to initialize the deep neural network parameters. The lung cancer detection convolutional neural network was further trained by fine-tuning of the labeled data. The deep learning model was tested by 203 slides in the internal test set and 1 081 slides obtained from TCIA database, named as the external test set. Results: The model trained with transfer learning showed substantial accuracy advantage against the one trained from scratch for the internal test set [area under curve (AUC) 0.988 vs. 0.971, Kappa 0.852 vs. 0.832]. For the external test set, the transferred model achieved an AUC of 0.968 and Kappa of 0.828, indicating superior generalization ability. By studying the predictions made by the model, the researchers obtained deeper understandings of the deep learning model. Conclusions: The lung cancer histopathological diagnostic system achieves higher accuracy and superior generalization ability. With the development of histopathological AI, the transfer learning can effectively train diagnosis models and shorten the learning period, and improve the model performance.


Asunto(s)
Aprendizaje Profundo , Neoplasias Pulmonares , Inteligencia Artificial , Bases de Datos Factuales , Humanos , Neoplasias Pulmonares/diagnóstico , Redes Neurales de la Computación
10.
Gan To Kagaku Ryoho ; 47(10): 1399-1404, 2020 Oct.
Artículo en Japonés | MEDLINE | ID: mdl-33130728

RESUMEN

With the development and diversification of medical care, the importance of precision medicine, which selects a suitable treatment for the individual patient from a huge number of options, is increasing. It is often difficult to explain multifactorial diseases such as cancer and chronic inflammatory diseases by a single hypothesis. In such case, a data-driven approach is essential to construct individualized models based on comprehensive observation of the target disease. The data-driven approach utilizes artificial intelligence to extract, predict, and classify patterns of data, considering different types of variables and complex dependencies between variables. In this paper, we introduce the basic idea, typical methods, and application examples of artificial intelligence and its core technology, machine learning. We would like to discuss a new framework of medical research toward the next generation medicine, while reviewing how machine learning is used in precise prediction and data-driven redefinition of diseases.


Asunto(s)
Inteligencia Artificial , Neoplasias , Humanos , Aprendizaje Automático , Neoplasias/diagnóstico , Neoplasias/terapia , Medicina de Precisión
11.
Sci Rep ; 10(1): 19012, 2020 11 04.
Artículo en Inglés | MEDLINE | ID: mdl-33149198

RESUMEN

To combat the pandemic of the coronavirus disease 2019 (COVID-19), numerous governments have established phone hotlines to prescreen potential cases. These hotlines have struggled with the volume of callers, leading to wait times of hours or, even, an inability to contact health authorities. Symptoma is a symptom-to-disease digital health assistant that can differentiate more than 20,000 diseases with an accuracy of more than 90%. We tested the accuracy of Symptoma to identify COVID-19 using a set of diverse clinical cases combined with case reports of COVID-19. We showed that Symptoma can accurately distinguish COVID-19 in 96.32% of clinical cases. When considering only COVID-19 symptoms and risk factors, Symptoma identified 100% of those infected when presented with only three signs. Lastly, we showed that Symptoma's accuracy far exceeds that of simple "yes-no" questionnaires widely available online. In summary, Symptoma provides unparalleled accuracy in systematically identifying cases of COVID-19 while also considering over 20,000 other diseases. Furthermore, Symptoma allows free text input, furthered with disease-specific follow up questions, in 36 languages. Combined, these results and accessibility give Symptoma the potential to be a key tool in the global fight against COVID-19. The Symptoma predictor is freely available online at https://www.symptoma.com .


Asunto(s)
Inteligencia Artificial , Infecciones por Coronavirus/diagnóstico , Tamizaje Masivo/métodos , Neumonía Viral/diagnóstico , Programas Informáticos , Telemedicina/métodos , Infecciones por Coronavirus/epidemiología , Humanos , Tamizaje Masivo/normas , Pandemias , Neumonía Viral/epidemiología , Telemedicina/normas
12.
Bone Joint J ; 102-B(11): 1574-1581, 2020 Nov.
Artículo en Inglés | MEDLINE | ID: mdl-33135455

RESUMEN

AIMS: The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. METHODS: In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into 'dislocation' (dislocation and subluxation) and 'non-dislocation' (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. RESULTS: In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). CONCLUSION: The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574-1581.


Asunto(s)
Inteligencia Artificial , Diagnóstico por Computador , Luxación Congénita de la Cadera/diagnóstico por imagen , Preescolar , Femenino , Luxación Congénita de la Cadera/diagnóstico , Humanos , Interpretación de Imagen Asistida por Computador , Lactante , Recién Nacido , Masculino
13.
Eur Respir Rev ; 29(157)2020 Sep 30.
Artículo en Inglés | MEDLINE | ID: mdl-33004526

RESUMEN

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.


Asunto(s)
Algoritmos , Inteligencia Artificial , Betacoronavirus , Infecciones por Coronavirus/diagnóstico , Prestación de Atención de Salud/métodos , Aprendizaje Automático , Neumonía Viral/diagnóstico , Neumología/métodos , Humanos , Pandemias
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1863-1866, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018363

RESUMEN

The deterioration of the retina center could be the main reason for vision loss. Older people usually ranging from 50 years and above are exposed to age-related macular degeneration (AMD) disease that strikes the retina. The lack of human expertise to interpret the complexity in diagnosing diseases leads to the importance of developing an accurate method to detect and localize the targeted infection. Approaching the performance of ophthalmologists is the consistent main challenge in retinal disease segmentation. Artificial intelligence techniques have shown enormous achievement in various tasks in computer vision. This paper depicts an automated end-to-end deep neural network for retinal disease segmentation on optical coherence tomography (OCT) scans. The work proposed in this study shows the performance difference between convolution operations and atrous convolution operations. Three deep semantic segmentation architectures, namely U-net, Segnet, and Deeplabv3+, have been considered to evaluate the performance of varying convolution operations. Empirical outcomes show a competitive performance to the human level, with an average dice score of 0.73 for retinal diseases.


Asunto(s)
Enfermedades de la Retina , Tomografía de Coherencia Óptica , Anciano , Anciano de 80 o más Años , Inteligencia Artificial , Humanos , Redes Neurales de la Computación , Retina/diagnóstico por imagen , Enfermedades de la Retina/diagnóstico por imagen
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2421-2424, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018495

RESUMEN

During common surgical tasks related to orthopedic applications, it is necessary to carefully manipulate a mobile C-arm device to achieve the desired position. In this work, we propose the application of learning conflicts analysis to improve the performance of an artificial neural network to compute the inverse kinematics of a C-arm device. Using the forward kinematics equations of a C-arm device (and the respective patient table) a training set for machine learning was generated. However, as an inverse kinematics problem may have multiple solutions, it is likely that training a neural network using forward kinematics data may generate machine learning conflicts. In this sense, we show that it is possible to eliminate those C-arm positions that may represent a learning conflict for the neural network, and thus, improve the accuracy of the model. Finally, we randomly generated a suitable validation set to verify the performance of our proposed model with data different from those used for training.


Asunto(s)
Inteligencia Artificial , Ortopedia , Fenómenos Biomecánicos , Humanos , Aprendizaje Automático , Redes Neurales de la Computación
16.
Stud Health Technol Inform ; 273: 23-37, 2020 Sep 04.
Artículo en Inglés | MEDLINE | ID: mdl-33087590

RESUMEN

The paper describes the concept of the Industry 4.0 and its reflection in health care. Industry 4.0 connects intelligent production concepts with external factors, including those linked with the production and those linked more with human, as for example intelligent homes or social web systems. Communication, data and information play an important role in the whole system. After explaining basic characteristics of the Industry 4.0 concept and its main parts, we show how they can be utilized in the health care sector and what their advantages are. Key technologies and techniques include Internet of Things, big data, artificial intelligence, data integration, robotization, virtual reality, and 3D printing. Finally, we identify the main challenges and research directions. Among the most important ones are interoperability, standardization, reliability, security and privacy, ethical and legal issues.


Asunto(s)
Inteligencia Artificial , Prestación de Atención de Salud , Macrodatos , Humanos , Industrias , Reproducibilidad de los Resultados
17.
Stud Health Technol Inform ; 273: 163-169, 2020 Sep 04.
Artículo en Inglés | MEDLINE | ID: mdl-33087607

RESUMEN

The demographic change is no longer a prognosis, but a reality seen in everyday life situations and requires mechanisms to make the public and private space elderly-adequate. These required mechanisms need to consider the varying aging process for each individual as well as adapt to the dynamic daily life of individuals characterized by spatial, temporal and activity variance. Developing assistance systems that are user-adaptive within dynamic environments is a challenging task. AI-based cyber-physical assistance systems enable such adaptive, flexible and individual assistance by processing acquired data from the physical environment using cyber resources and delivering intelligent assistance as well as interfaces to further medical services. This contribution discusses a flexible, reusable, and user-specific concept for AI-based assistance systems. Relying on distributed and heterogeneous data, the user's context is continuously modeled and reasoned over to infer actionable knowledge within a middleware between the data layer and the application layer. To demonstrate the applicability of the concept, the use case of intelligently supporting patients' medication adherence is shown.


Asunto(s)
Inteligencia Artificial , Ambiente , Anciano , Humanos
18.
Stud Health Technol Inform ; 273: 203-208, 2020 Sep 04.
Artículo en Inglés | MEDLINE | ID: mdl-33087613

RESUMEN

A broad range of aspects are needed to be taken into consideration in the design and development of personalized coaching systems based on artificial intelligence methodologies. This research presents the initial phase of joining different professional and stakeholder perspectives on behavior change technologies into a flexible design proposal for a digital coaching system. The diversity and sometimes opposed views on content, behavior, purposes and context were managed using a structured argument-based design approach, which also feed into the behavior of the personalized system. Results include a set of personalization strategies that will be further elaborated with the target user group to manage sensitive issues such as ethics, social norms, privacy, motivation, autonomy and social relatedness.


Asunto(s)
Inteligencia Artificial , Tutoría , Motivación , Atención Primaria de Salud , Privacidad
20.
Rev Prat ; 70(6): 594-598, 2020 Jun.
Artículo en Francés | MEDLINE | ID: mdl-33058596

RESUMEN

New in radiotherapy of solid tumors. The new irradiation techniques integrate the latest technological developments in medical imaging and computer science, dosimetry, and linacs into the treatment procedure. They raise new hopes for the treatment of solid tumor pathologies. Three techniques seem particularly promising: intensity modulated radiotherapy, respiratory gating radiotherapy, and stereotactic radiotherapy. The emergence of artificial intelligence, and particularly its applications in the field of imaging, opens up a new field of research. The purpose of these different innovations is to achieve very high precision radiotherapy, which makes it possible to better adapt the radiation fields to the tumor and thus protect the critical organs.


Asunto(s)
Neoplasias , Radiocirugia , Radioterapia de Intensidad Modulada , Inteligencia Artificial , Humanos , Neoplasias/radioterapia
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