Multinomial logistic regression

In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.). Multinomial logistic regression is known by a variety of other names, including polytomous LR, multiclass LR, softmax regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model. Background Multinomial logistic regression is used when the dependent variable in question is nominal (equivalently categorical, meaning that it falls into any one of a set of categories that cannot be ordered in any meaningful way) and for which there are more than two categories. Some examples wo

Point-of-care lung ultrasonography for early identification of mild COVID-19: a prospective cohort of outpatients in a Swiss screening center

Luca Bosso, Jean-Baptiste Francis Marie Juliette Cordonnier, Tanguy Espejo, Mary-Anne Hartley, Jean-Yves Meuwly, Siméon Schaad

Objectives Early identification of SARS-CoV-2 infection is important to guide quarantine and reduce transmission. This study evaluates the diagnostic performance of lung ultrasound (LUS), an affordable, consumable-free point-of-care tool, for COVID-19 screening. Design, setting and participants This prospective observational cohort included adults presenting with cough and/or dyspnoea at a SARS-CoV-2 screening centre of Lausanne University Hospital between 31 March and 8 May 2020. Interventions Investigators recorded standardised LUS images and videos in 10 lung zones per patient. Two blinded independent experts reviewed LUS recording and classified abnormal findings according to prespecified criteria to investigate their predictive value to diagnose SARS-CoV-2 infection according to PCR on nasopharyngeal swabs (COVID-19 positive vs COVID-19 negative). Primary and secondary outcome measures We finally combined LUS and clinical findings to derive a multivariate logistic regression diagnostic score. Results Of 134 included patients, 23% (n=30/134) were COVID-19 positive and 77% (n=103/134) were COVID-19 negative; 85%, (n=114/134) cases were previously healthy healthcare workers presenting within 2-5 days of symptom onset (IQR). Abnormal LUS findings were significantly more frequent in COVID-19 positive compared with COVID-19 negative (45% vs 26%, p=0.045) and mostly consisted of focal pathologic B lines. Combining clinical findings in a multivariate logistic regression score had an area under the receiver operating curve of 80.3% to detect COVID-19, and slightly improved to 84.5% with the addition of LUS features. Conclusions COVID-19-positive patients are significantly more likely to have lung pathology by LUS. However, LUS has an insufficient sensitivity and is not an appropriate screening tool in outpatients. LUS only adds little value to clinical features alone.

BMJ PUBLISHING GROUP2022

Predictive Analysis of Errors During Robot-Mediated Gamified Training

Pierre Dillenbourg, Nevena Dresevic, Arzu Güneysu Özgür, Marija Katanic, Nihal Ezgi Yücetürk

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable features and separated class characteristics between the casual gameplay state and error prune state in the data. Identifying such features provides valuable insight to creating individually tailored, adaptive games as well as possible ways to increase rehabilitation success by patients. Considering the time-series nature of sensory data created by motor actions of patients we employed a predictive analysis strategy on carefully engineered features of sequenced data. We split the data into fixed time windows and explored logistic regression models, decision trees, and recurrent neural networks to predict the likelihood of a patient making an error based on the features from the time window before the error. We achieved an 84.4% F1-score with a 0.76 ROC value in our best model for predicting motion accuracy related errors. Moreover, we computed the permutation importance of the features to explain which ones are more indicative of future errors.

2022

Clustering citation histories in the Physical Review

Giovanni Colavizza, Massimo Franceschet

We investigate publications through their citation histories – the history events are the citations given to the article by younger publications and the time of the event is the date of publication of the citing article. We propose a methodology, based on spectral clustering, to group citation histories, and the corresponding publications, into communities and apply multinomial logistic regression to provide the revealed communities with semantics in terms of publication features. We study the case of publications from the full Physical Review archive, covering 120 years of physics in all its domains. We discover two clear archetypes of publications – marathoners and sprinters – that deviate from the average middle-of-the-roads behaviour, and discuss some publication features, like age of references and type of publication, that are correlated with the membership of a publication into a certain community.

Elsevier Science Bv2016

Point-of-care lung ultrasonography for early identification of mild COVID-19: a prospective cohort of outpatients in a Swiss screening center

Luca Bosso, Jean-Baptiste Francis Marie Juliette Cordonnier, Tanguy Espejo, Mary-Anne Hartley, Jean-Yves Meuwly, Siméon Schaad

BMJ PUBLISHING GROUP2022