Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

Alonso, Susel Góngora; Díez, Isabel de la Torre; Zapirain, Begoña García

doi:10.1007/s10916-019-1279-4

Cited by 68 publications

(45 citation statements)

References 68 publications

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“…This review illustrates information on the current state of DBS technology, which can be useful and helpful for biomedical researchers. The prospects of using this technology to assess the metabolomic profile, assessment, diagnosis of communicable diseases are demonstrated.Diagnostics 2020, 10, 248 2 of 16 personalized, and participatory) include the detection of pathology-specific molecular profiles or disease "signatures" [7].Due to the ability to establish a comprehensive view of multimodal disturbances in biological processes, "omics" technologies are of growing interest in identifying the molecular patterns associated with socially weighty diseases. "Omics" technologies implement a holistic view of the molecules that make up a cell, tissue and organism.…”

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confidence: 99%

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Dried Blood Spot in Laboratory: Directions and Prospects

et al. 2020

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Over the past few years, dried blood spot (DBS) technology has become a convenient tool in both qualitative and quantitative biological analysis. DBS technology consists of a membrane carrier (MC) on the surface of which a biomaterial sample becomes absorbed. Modern analytical, immunological or genomic methods can be employed for analysis after drying the sample. DBS has been described as the most appropriate method for biomaterial sampling due to specific associated inherent advantages, including the small volumes of biomaterials required, the absence of a need for special conditions for samples’ storage and transportation, improved stability of analytes and reduced risk of infection resulting from contaminated samples. This review illustrates information on the current state of DBS technology, which can be useful and helpful for biomedical researchers. The prospects of using this technology to assess the metabolomic profile, assessment, diagnosis of communicable diseases are demonstrated.

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confidence: 99%

“…Diagnostics 2020, 10, 248 2 of 16 personalized, and participatory) include the detection of pathology-specific molecular profiles or disease "signatures" [7].…”

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confidence: 99%

Dried Blood Spot in Laboratory: Directions and Prospects

et al. 2020

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“…La TM puede aportar otras muchas opciones en la gestión clínica de pacientes con apneas durante el sueño ( fig. 2), desde la primera entrevista clínica hasta el seguimiento del tratamiento 38 , y todo ello en relación con los conceptos actuales de medicina de precisión y 4P 39,40 . En la tabla 3 se comparan, desde un punto de vista tecnológico global, los procedimientos actuales con el potencial uso Cómo citar este artículo: Montserrat Canal JM, et al Posicionamiento de la Sociedad Española de Neumología y Cirugía Torácica en el uso del la telemedicina en los trastornos respiratorios del sueño y ventilación mecánica.…”

Section: Aspectos Prácticos Y Tecnológicosunclassified

Posicionamiento de la Sociedad Española de Neumología y Cirugía Torácica en el uso de la telemedicina en los trastornos respiratorios del sueño y ventilación mecánica

Canal

Suárez-Girón

Egea

et al. 2021

Archivos de Bronconeumología

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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“…Supervised learning in ML is a set of techniques that allow for making predictions based on behavior or features analyzed from known data (input). A supervised learning algorithm analyzes the input data (characteristics analyzed) and produces an output which is the variable being predicted [6,7]. By selecting the features, relationships and patterns can be established between the data and features about which we wish to make a prediction [5,8].…”

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confidence: 99%

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Cristian

Zúñiga

2020

IJERPH

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The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe.

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Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

Cited by 68 publications

References 68 publications

Dried Blood Spot in Laboratory: Directions and Prospects

Dried Blood Spot in Laboratory: Directions and Prospects

Posicionamiento de la Sociedad Española de Neumología y Cirugía Torácica en el uso de la telemedicina en los trastornos respiratorios del sueño y ventilación mecánica

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

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