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  • 1. Mathiassen, Svend Erik
    et al.
    Wahlström, Jens
    Forsman, Mikael
    Bias and imprecision in posture percentile variables estimated from short exposure samples2012In: BMC Medical Research Methodology, E-ISSN 1471-2288, Vol. 12, no 1, p. 1-14Article in journal (Refereed)
    Abstract [en]

    Background

    Upper arm postures are believed to be an important risk determinant for musculoskeletal disorder development in the neck and shoulders. The 10th and 90th percentiles of the angular elevation distribution have been reported in many studies as measures of neutral and extreme postural exposures, and variation has been quantified by the 10th-90th percentile range. Further, the 50th percentile is commonly reported as a measure of "average" exposure. These four variables have been estimated using samples of observed or directly measured postures, typically using sampling durations between 5 and 120 min.

    Methods

    The present study examined the statistical properties of estimated full-shift values of the 10th, 50th and 90th percentile and the 10th-90th percentile range of right upper arm elevation obtained from samples of seven different durations, ranging from 5 to 240 min. The sampling strategies were realized by simulation, using a parent data set of 73 full-shift, continuous inclinometer recordings among hairdressers. For each shift, sampling duration and exposure variable, the mean, standard deviation and sample dispersion limits (2.5% and 97.5%) of all possible sample estimates obtained at one minute intervals were calculated and compared to the true full-shift exposure value.

    Results

    Estimates of the 10th percentile proved to be upward biased with limited sampling, and those of the 90th percentile and the percentile range, downward biased. The 50th percentile was also slightly upwards biased. For all variables, bias was more severe with shorter sampling durations, and it correlated significantly with the true full-shift value for the 10th and 90th percentiles and the percentile range. As expected, shorter samples led to decreased precision of the estimate; sample standard deviations correlated strongly with true full-shift exposure values.

    Conclusions

    The documented risk of pronounced bias and low precision of percentile estimates obtained from short posture samples presents a concern in ergonomics research and practice, and suggests that alternative, unbiased exposure variables should be considered if data collection resources are restricted.

  • 2.
    Stenwig, Eline
    et al.
    Norwegian Univ Sci & Technol, Dept Circulat & Med Imaging, Trondheim, Norway..
    Salvi, Giampiero
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH. Norwegian Univ Sci & Technol, Dept Elect Syst, Trondheim, Norway..
    Rossi, Pierluigi Salvo
    Norwegian Univ Sci & Technol, Dept Elect Syst, Trondheim, Norway..
    Skjaervold, Nils Kristian
    Norwegian Univ Sci & Technol, Dept Circulat & Med Imaging, Trondheim, Norway.;St Olavs Univ Hosp, Clin Anaesthesia & Intens Care Med, Trondheim, Norway..
    Comparison of correctly and incorrectly classified patients for in-hospital mortality prediction in the intensive care unit2023In: BMC Medical Research Methodology, E-ISSN 1471-2288, Vol. 23, no 1, article id 102Article in journal (Refereed)
    Abstract [en]

    Background

    The use of machine learning is becoming increasingly popular in many disciplines, but there is still an implementation gap of machine learning models in clinical settings. Lack of trust in models is one of the issues that need to be addressed in an effort to close this gap. No models are perfect, and it is crucial to know in which use cases we can trust a model and for which cases it is less reliable.

    Methods

    Four different algorithms are trained on the eICU Collaborative Research Database using similar features as the APACHE IV severity-of-disease scoring system to predict hospital mortality in the ICU. The training and testing procedure is repeated 100 times on the same dataset to investigate whether predictions for single patients change with small changes in the models. Features are then analysed separately to investigate potential differences between patients consistently classified correctly and incorrectly.

    Results

    A total of 34 056 patients (58.4%) are classified as true negative, 6 527 patients (11.3%) as false positive, 3 984 patients (6.8%) as true positive, and 546 patients (0.9%) as false negatives. The remaining 13 108 patients (22.5%) are inconsistently classified across models and rounds. Histograms and distributions of feature values are compared visually to investigate differences between groups.ConclusionsIt is impossible to distinguish the groups using single features alone. Considering a combination of features, the difference between the groups is clearer. Incorrectly classified patients have features more similar to patients with the same prediction rather than the same outcome.

  • 3. Stenwig, Eline
    et al.
    Salvi, Giampiero
    Salvo Rossi, Pierluigi
    Skjærvold, Nils Kristian
    Comparative analysis of explainable machine learning prediction models for hospital mortality2022In: BMC Medical Research Methodology, E-ISSN 1471-2288, Vol. 22, no 1, article id 53Article in journal (Refereed)
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