BACKGROUND: The Advanced Trauma Life Support classification (ATLS) of hypovolemic shock is a widely used teaching and treatment reference in emergency medicine, but oversimplifies clinical reality. A decade ago, a landmark study compared vital parameters to base deficit (BD) in trauma patients. The investigators concluded that BD had higher accuracy to detect the need for early blood product administration. BD was subsequently introduced in the ATLS shock classification and has since been widely accepted as a laboratory standard for hypovolemia. The aim of this study is to investigate whether a methodological bias may have inadvertently contributed to the study's results and interpretation. METHODS: In the current study, we replicate the original study by simulating a cohort of trauma patients with randomly generated data and applying the same methodological strategies. First, a predefined correlation between all predictor variables (vital parameters and BD) and outcome variable (transfusion) was set at 0.55. Then, in accordance with the methods of the original study we created a composite of ATLS parameters (highest class amongst heart rate, systolic blood pressure, and Glasgow Coma Scale) and compared it with BD for resulting transfusion quantity. Given the preset correlations between predictors and outcome, no predictor should exhibit a stronger association unless influenced by methodological bias. RESULTS: Applying the original imbalanced grouping and composite allocation strategies caused a systematic overestimation of shock class for traditional ATLS parameters, favoring the association between BD and transfusion. This effect persisted when the correlation between BD and transfusion was set substantially worse (rho = 0.3) than the correlation between ATLS parameters and transfusion (rho = 0.8). CONCLUSIONS: In this fully reproducible simulation, we confirm the inadvertent presence of methodological bias. It is physiologically reasonable to include a metabolic parameter to classify hypovolemic shock, but more evidence is needed to support widespread and preferred use of BD.
Imbalanced datasets are prominent in real-world problems. In such problems, the data samples in one class are significantly higher than in the other classes, even though the other classes might be more important. The standard classification algorithms may classify all the data into the majority class, and this is a significant drawback of most standard learning algorithms, so imbalanced datasets need to be handled carefully. One of the traditional algorithms, twin support vector machines (TSVM), performed well on balanced data classification but poorly on imbalanced datasets classification. In order to improve the TSVM algorithm's classification ability for imbalanced datasets, recently, driven by the universum twin support vector machine (UTSVM), a reduced universum twin support vector machine for class imbalance learning (RUTSVM) was proposed. The dual problem and finding classifiers involve matrix inverse computation, which is one of RUTSVM's key drawbacks. In this paper, we improve the RUTSVM and propose an improved reduced universum twin support vector machine for class imbalance learning (IRUTSVM). We offer alternative Lagrangian functions to tackle the primal problems of RUTSVM in the suggested IRUTSVM approach by inserting one of the terms in the objective function into the constraints. As a result, we obtain new dual formulation for each optimization problem so that we need not compute inverse matrices neither in the training process nor in finding the classifiers. Moreover, the smaller size of the rectangular kernel matrices is used to reduce the computational time. Extensive testing is carried out on a variety of synthetic and real-world imbalanced datasets, and the findings show that the IRUTSVM algorithm outperforms the TSVM, UTSVM, and RUTSVM algorithms in terms of generalization performance.
- MeSH
- Algorithms * MeSH
- Support Vector Machine * MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. METHOD: A total of 493 individuals were included: 389 with a ND-GC, mean age = 9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age = 10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. RESULTS: All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. LIMITATIONS: This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. CONCLUSIONS: In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment.
- MeSH
- Child MeSH
- Genomics MeSH
- Cohort Studies MeSH
- Humans MeSH
- Intellectual Disability * MeSH
- Adolescent MeSH
- Autism Spectrum Disorder * MeSH
- Cross-Sectional Studies MeSH
- Machine Learning MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
... - T Heather Herdman, RN, PhD, FNI -- What\'s New in the 2015-2017 Edition of -- Diagnoses and Classification ... ... 35 -- Data analysis 35 -- Figure 2.2 Converting Data to Information 36 -- Subjective versus objective ... ... data 37 -- Clustering of information/seeing a pattern 38 -- Figure 2.3 The Modified Nursing Process ... ... about diagnosis development and review 127 -- Questions about the NANDA-I Definitions and -- Classification ... ... Thermoregulation 426 -- Risk for imbalanced body temperature - 00005 426 -- Hyperthermia - 00007 427 ...
10th ed. 483 s.
