Comment les solutions sont-elles utilisées en diagnostic ?

Elles servent à diluer des échantillons ou à administrer des agents de contraste.

Quelles solutions sont utilisées pour les tests sanguins ?

Les solutions salines et les anticoagulants sont couramment utilisés.

Quel rôle des solutions dans l'imagerie médicale ?

Elles améliorent la visibilité des structures internes lors des examens d'imagerie.

Les solutions peuvent-elles aider au diagnostic des infections ?

Oui, certaines solutions permettent d'identifier des agents pathogènes dans des échantillons.

Comment les solutions sont-elles utilisées en microbiologie ?

Elles servent à cultiver des micro-organismes pour les identifier.

Quels symptômes peuvent nécessiter des solutions intraveineuses ?

Déshydratation, déséquilibre électrolytique ou choc hypovolémique.

Les solutions peuvent-elles soulager des douleurs ?

Certaines solutions anesthésiques peuvent être utilisées pour soulager la douleur.

Quels symptômes indiquent une réaction à une solution ?

Rougeur, démangeaisons ou gonflement au site d'injection peuvent survenir.

Comment les solutions affectent-elles l'hydratation ?

Elles aident à rétablir l'équilibre hydrique dans le corps.

Quels symptômes peuvent résulter d'une surhydratation ?

Œdème, hypertension et troubles respiratoires peuvent se manifester.

Comment les solutions peuvent-elles prévenir les infections ?

Les solutions antiseptiques et désinfectantes réduisent le risque d'infection.

Les solutions de vaccination sont-elles efficaces ?

Oui, elles contiennent des agents immunisants pour prévenir diverses maladies.

Quel rôle des solutions dans la prévention de la déshydratation ?

Les solutions de réhydratation orale aident à maintenir l'hydratation, surtout chez les enfants.

Comment les solutions peuvent-elles prévenir les complications post-opératoires ?

Elles aident à maintenir l'équilibre hydrique et électrolytique après une chirurgie.

Les solutions peuvent-elles prévenir les allergies ?

Certaines solutions peuvent être utilisées pour désensibiliser les patients allergiques.

Quelles solutions sont utilisées pour traiter la déshydratation ?

Les solutions de réhydratation orale et intraveineuse sont couramment utilisées.

Comment les solutions salines sont-elles utilisées en médecine ?

Elles sont utilisées pour l'hydratation et comme diluants pour d'autres médicaments.

Les solutions peuvent-elles être utilisées en chimiothérapie ?

Oui, elles servent à administrer des agents chimiothérapeutiques par voie intraveineuse.

Quel est l'usage des solutions antiseptiques ?

Elles sont utilisées pour désinfecter les plaies et prévenir les infections.

Comment les solutions de glucose sont-elles utilisées ?

Elles fournissent une source rapide d'énergie et sont utilisées en cas d'hypoglycémie.

Quelles complications peuvent survenir avec des solutions intraveineuses ?

Infections, phlébite ou surhydratation peuvent se produire.

Comment éviter les complications liées aux solutions ?

Une administration correcte et une surveillance régulière sont essentielles.

Les solutions peuvent-elles causer des réactions allergiques ?

Oui, certaines solutions peuvent provoquer des réactions allergiques chez certains patients.

Quelles complications peuvent résulter d'une surhydratation ?

Œdème pulmonaire et hypertension intracrânienne peuvent survenir.

Comment les solutions mal administrées peuvent-elles affecter la santé ?

Elles peuvent entraîner des déséquilibres électrolytiques et des complications graves.

Quels facteurs augmentent le risque d'infections liées aux solutions ?

Une mauvaise hygiène et des techniques d'administration inappropriées augmentent le risque.

Les allergies aux composants des solutions sont-elles courantes ?

Oui, certaines personnes peuvent être allergiques à des agents présents dans les solutions.

Comment l'âge influence-t-il l'utilisation des solutions ?

Les jeunes enfants et les personnes âgées sont plus sensibles aux effets des solutions.

Quels médicaments peuvent interagir avec les solutions ?

Certains médicaments peuvent provoquer des interactions indésirables avec des solutions.

Les maladies chroniques augmentent-elles les risques liés aux solutions ?

Oui, les patients atteints de maladies chroniques peuvent avoir des risques accrus.

Solutions - Informations médicales vérifiées

Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning.

22 12 2022

DOI: 10.3390/ijerph20010146 PMID: 36612474

The academic curriculum has shown to promote sedentary behavior in college students. This study aimed to profile the physical fitness of physical education majors using unsupervised machine learning a...

Male Female Humans Cross-Sectional Studies Unsupervised Machine Learning +4 autres

Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.

29 Jul 2024

DOI: 10.1136/bmjhci-2023-100963 PMID: 39074912

Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-dri... Observational EHR data from a tertiary paediatric hospital, containing 61 522 unique patients and 3315 unique ICD-10 diagnosis codes was used, after preprocessing. K-means clustering was applied to id... Four age clusters of diseases were identified, broadly aligning to ages between: 0 and 1; 1 and 5; 5 and 13; 13 and 18. Diagnoses, within the clusters, aligned to existing knowledge regarding the prop... Unsupervised ML applied to EHR data identifies clinically relevant age distributions of diagnoses which can augment existing decision making. However, biases within healthcare datasets dramatically im...

Humans Unsupervised Machine Learning Child Electronic Health Records Child, Preschool +7 autres

Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters.

06 12 2022

DOI: 10.1039/d2lc00902a PMID: 36398860

Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in c...

Unsupervised Machine Learning Rheology

Clustering using unsupervised machine learning to stratify the risk of immune-related liver injury.

Feb 2023

DOI: 10.1111/jgh.16038 PMID: 36302734

Immune-related liver injury (liver-irAE) is a clinical problem with a potentially poor prognosis.... We retrospectively collected clinical data from patients treated with immune checkpoint inhibitors between September 2014 and December 2021 at the Nagoya University Hospital. Using an unsupervised mac... This study included a total of 702 patients. Among them, 492 (70.1%) patients were male, and the mean age was 66.6 years. During the mean follow-up period of 423 days, severe liver-irAEs (Common Termi... The combined assessment of multiple markers and body temperature may help stratify high-risk groups for developing liver-irAE....

Humans Male Aged Female Antineoplastic Agents, Immunological +4 autres

Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach.

21 02 2023

DOI: 10.1192/j.eurpsy.2023.13 PMID: 36804948

Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and over... We assessed a total of 43 symptoms in a discovery sample of 6,602 participants of the population-based Rotterdam Study between 2009 and 2013, and in a replication sample of 3,005 participants between ... First, clustering analyses of the questionnaire items suggested a three-cluster solution representing clusters of "mixed" symptoms, "depressed affect and nervousness", and "troubled sleep and interper... We identified three clusters of psychiatric symptoms that most commonly co-occur in a population-based sample. These symptoms clustered stable over samples, but across the topics of depression, anxiet...

Humans Unsupervised Machine Learning Anxiety Anxiety Disorders Sleep Initiation and Maintenance Disorders +1 autres

Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.

06 Dec 2023

DOI: 10.1021/jasms.3c00295 PMID: 37897440

To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. Th...

Humans Unsupervised Machine Learning Workflow Software Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization +1 autres

An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia.

01 11 2022

DOI: 10.1016/j.jad.2022.08.013 PMID: 35964770

Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical ... The present analyses utilized raw minute-level actigraphy data from three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respe... We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we fo... However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information.... Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifica...

Cluster Analysis Depression Depressive Disorder, Major Humans Schizophrenia +1 autres

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19.

2023

DOI: 10.1371/journal.pone.0285719 PMID: 37200352

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe tran...

Humans Unsupervised Machine Learning Algorithms Pandemics COVID-19 +1 autres

Unsupervised machine learning model to predict cognitive impairment in subcortical ischemic vascular disease.

08 2023

DOI: 10.1002/alz.12971 PMID: 36786521

It is challenging to predict which patients who meet criteria for subcortical ischemic vascular disease (SIVD) will ultimately progress to subcortical vascular cognitive impairment (SVCI).... We collected clinical information, neuropsychological assessments, T1 imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging from 83 patients with SVCI and 53 age-m... The accuracy, sensitivity, and specificity of the unsupervised machine learning model were 86.03%, 79.52%, and 96.23% and 80.52%, 71.11%, and 93.75% for internal and external cohort, respectively.... We developed an accurate and accessible clinical tool which requires only data from routine imaging to predict patients at risk of progressing from SIVD to SVCI.... Our unsupervised machine learning model provides an accurate and accessible clinical tool to predict patients at risk of progressing from subcortical ischemic vascular disease (SIVD) to subcortical va...

Humans Diffusion Tensor Imaging Unsupervised Machine Learning Cognitive Dysfunction Vascular Diseases +2 autres

The unsupervised machine learning to analyze the use strategy of statins for ischaemic stroke patients with elevated transaminase.

09 2023

DOI: 10.1016/j.clineuro.2023.107900 PMID: 37478641

Statins could elevate hepatic transaminase in ischemic stroke patients. There needed to be more evidence on which method stopped statins or adjusting the dose of statins was better for patients. And n... We collected ischaemic stroke patients with elevated hepatic transaminase when they take statins. The outcome was a recurrent stroke rate, transaminase value after stopping or adjusted, mortality, and... The patients stopping statins had a higher stroke recurrence and rate of FFO (mRS 0-2), a lower mean value of transaminase, and mortality. By difference unsupervised machine learning group, the km2 gr... For ischemic patients with elevated hepatic transaminase, stopping statins temporarily was a better choice of treatment strategy. These patients who were younger, male, with a lesser NIHSS score at ad...

Humans Male Hydroxymethylglutaryl-CoA Reductase Inhibitors Stroke Brain Ischemia +4 autres

Solutions : Questions médicales fréquentes

Termes MeSH sélectionnés :

Sous-catégories

Solution hypertonique

Solution hypotonique

Solution isotonique

Solution conservation organe

Solutions réhydratation

Solution hypertonique glucose

Solution saline hypertonique

Cristalloïdes

Solution de Ringer

Solution de Ringer au lactate

Solution physiologique salée

Sources (10000 au total)