Adaptive Semi-Supervised Clustering Algorithm with Label Propagation

Leng, Mingwei; Wang, Jinjin; Cheng, Jianjun; Zhou, Hanhai; Chen, Xiaoyun

doi:10.3923/jse.2014.14.22

Cited by 7 publications

(13 citation statements)

References 5 publications

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“…The selection of the optimal clustering algorithm was motivated by the highest ratio of between-cluster to total variance and the best stability measured by mean classification error in 20-fold cross-validation (CV) (Figure 6 -figure supplement 1AB, Figure 7 -figure supplement 1AB) (37). The optimal cluster number was determined by the bend of the within-cluster sum-of-squares curve (function fviz_nbclust(), package factoextra) and by the stability in 20-fold CV (Figure 6 figure supplement 1CD, Figure 7 -figure supplement 1DF) (37,38) 3) (10,39). Cluster assignment visualization in a 4-dimensional principal analysis score plot was done with the PCAproj() tool (package pcaPP) (40).…”

Section: Cluster Analysismentioning

confidence: 99%

“…Participant clustering and machine learning classifiers trained in the CovILD cohort were implemented in an open-source online pulmonary assessment R shiny app (https://im2ibk.shinyapps.io/CovILD/, code: https://github.com/PiotrTymoszuk/COVILD-recovery-assessmentapp). Prediction of the cluster assignment based on the user-provided patient data is done by the kNN label propagation algorithm (10,39).…”

Section: Pulmonary Recovery Assessment Appmentioning

confidence: 99%

“…The six-month follow-up outcome variables were incorporated in the cluster structure using knearest-neighbors prediction (39). Long-term symptom persistence was associated with acute and long-lasting COVID-19 symptoms in cluster 3, whereas pulmonary outcome parameters grouped with cluster 2 features (Figure 6A, Figure 6 -figure supplement 2, Appendix 1 -table 3).…”

Section: Clusters Of Clinical Features Linked To Persistent Symptoms ...mentioning

confidence: 99%

“…figure supplement 1CD, Figure7-figure supplement 1DF)(37,38) as well as by a visual inspection of the SOM node clustering dendrograms Figure 7 -figure supplement 1E. Assignment of 180-day follow-up outcome features to the clusters of clinical parameters was accomplished with a k-nearest neighbor (kNN) label propagation algorithm (Appendix 1 -table3)(10,39). Cluster assignment visualization in a 4-dimensional…”

mentioning

confidence: 99%

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Investigating phenotypes of pulmonary COVID-19 recovery: A longitudinal observational prospective multicenter trial

Sonnweber

Tymoszuk

Sahanic

et al. 2022

eLife

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Background: The optimal procedures to prevent, identify, monitor and treat long-term pulmonary sequelae of COVID-19 are elusive. Here, we characterized the kinetics of respiratory and symptom recovery following COVID-19.Methods: We conducted a longitudinal, multi-center observational study in ambulatory and hospitalized COVID-19 patients recruited in early 2020 (n = 145). Pulmonary computed tomography (CT) and lung function (LF) readouts, symptom prevalence, clinical and laboratory parameters were collected during acute COVID-19 and at 60-, 100- and 180-days follow-up visits. Recovery kinetics and risk factors were investigated by logistic regression. Classification of clinical features and participants was accomplished by unsupervised and semi-supervised multi-parameter clustering and machine learning.Results: At the six-month follow-up, 49% of participants reported persistent symptoms. The frequency of structural lung CT abnormalities ranged from 18% in the mild outpatient cases to 76% in the ICU convalescents. Prevalence of impaired LF ranged from 14% in the mild outpatient cases to 50% in the ICU survivors. Incomplete radiological lung recovery was associated with increased anti-S1/S2 antibody titer, IL-6 and CRP levels at the early follow-up. We demonstrated that the risk of perturbed pulmonary recovery could be robustly estimated at early follow-up by clustering and machine learning classifiers employing solely non-CT and non-LF parameters.Conclusion: The severity of acute COVID-19 and protracted systemic inflammation is strongly linked to persistent structural and functional lung abnormality. Automated screening of multi-parameter health record data may assist at the prediction of incomplete pulmonary recovery and optimize COVID-19 follow-up management.Funding: The State of Tyrol (GZ 71934), Boehringer Ingelheim/Investigator initiated study (IIS 1199-0424).Trial Registration: ClinicalTrials.gov: NCT04416100

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Section: Cluster Analysismentioning

confidence: 99%

Section: Pulmonary Recovery Assessment Appmentioning

confidence: 99%

Section: Clusters Of Clinical Features Linked To Persistent Symptoms ...mentioning

confidence: 99%

mentioning

confidence: 99%

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Investigating phenotypes of pulmonary COVID-19 recovery: A longitudinal observational prospective multicenter trial

Sonnweber

Tymoszuk

Sahanic

et al. 2022

eLife

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“…Clusters of the training AT cohort individuals were defined with the self-organized map procedure (SOM, 13 × 13 unit hexagonal grid, Manhattan distance, package Kohonen ) and subsequent hierarchical clustering (Ward D2 algorithm, Manhattan distance) ( 20 , 21 , 31 ). Assignment of the test IT cohort participants to the clusters was done with the k-nearest neighbor label propagation algorithm ( 12 , 32 ). Details of statistical analysis are provided in Supplementary Methods .…”

Section: Methodsmentioning

confidence: 99%

Who Is at Risk of Poor Mental Health Following Coronavirus Disease-19 Outpatient Management?

et al. 2022

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BackgroundCoronavirus Disease-19 (COVID-19) convalescents are at risk of developing a de novo mental health disorder or worsening of a pre-existing one. COVID-19 outpatients have been less well characterized than their hospitalized counterparts. The objectives of our study were to identify indicators for poor mental health following COVID-19 outpatient management and to identify high-risk individuals.MethodsWe conducted a binational online survey study with adult non-hospitalized COVID-19 convalescents (Austria/AT: n = 1,157, Italy/IT: n = 893). Primary endpoints were positive screening for depression and anxiety (Patient Health Questionnaire; PHQ-4) and self-perceived overall mental health (OMH) and quality of life (QoL) rated with 4 point Likert scales. Psychosocial stress was surveyed with a modified PHQ stress module. Associations of the mental health and QoL with socio-demographic, COVID-19 course, and recovery variables were assessed by multi-parameter Random Forest and Poisson modeling. Mental health risk subsets were defined by self-organizing maps (SOMs) and hierarchical clustering algorithms. The survey analyses are publicly available (https://im2-ibk.shinyapps.io/mental_health_dashboard/).ResultsDepression and/or anxiety before infection was reported by 4.6% (IT)/6% (AT) of participants. At a median of 79 days (AT)/96 days (IT) post-COVID-19 onset, 12.4% (AT)/19.3% (IT) of subjects were screened positive for anxiety and 17.3% (AT)/23.2% (IT) for depression. Over one-fifth of the respondents rated their OMH (AT: 21.8%, IT: 24.1%) or QoL (AT: 20.3%, IT: 25.9%) as fair or poor. Psychosocial stress, physical performance loss, high numbers of acute and sub-acute COVID-19 complaints, and the presence of acute and sub-acute neurocognitive symptoms (impaired concentration, confusion, and forgetfulness) were the strongest correlates of deteriorating mental health and poor QoL. In clustering analysis, these variables defined subsets with a particularly high propensity of post-COVID-19 mental health impairment and decreased QoL. Pre-existing depression or anxiety (DA) was associated with an increased symptom burden during acute COVID-19 and recovery.ConclusionOur study revealed a bidirectional relationship between COVID-19 symptoms and mental health. We put forward specific acute symptoms of the disease as “red flags” of mental health deterioration, which should prompt general practitioners to identify non-hospitalized COVID-19 patients who may benefit from early psychological and psychiatric intervention.Clinical Trial Registration[ClinicalTrials.gov], identifier [NCT04661462].

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Distinct smell and taste disorder phenotype of post-acute COVID-19 sequelae

Rass,

Tymoszuk,

Sahanic

et al. 2023

Eur Arch Otorhinolaryngol

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Purpose Olfactory dysfunction (OD) commonly accompanies coronavirus disease 2019 (COVID-19). We investigated the kinetics of OD resolution following SARS-CoV-2 infection (wild-type and alpha variant) and its impact on quality of life, physical and mental health. Methods OD prevalence was assessed in an ambulatory COVID-19 survey (n = 906, ≥ 90 days follow-up) and an observational cohort of ambulatory and hospitalized individuals (n = 108, 360 days follow-up). Co-occurrence of OD with other symptoms and effects on quality of life, physical and mental health were analyzed by multi-dimensional scaling, association rule mining and semi-supervised clustering. Results Both in the ambulatory COVID-19 survey study (72%) and the observational ambulatory and hospitalized cohort (41%) self-reported OD was frequent during acute COVID-19. Recovery from self-reported OD was slow (survey: median 28 days, observational cohort: 90 days). By clustering of the survey data, we identified a predominantly young, female, comorbidity-free group of convalescents with persistent OD and taste disorders (median recovery: 90 days) but low frequency of post-acute fatigue, respiratory or neurocognitive symptoms. This smell and taste disorder cluster was characterized by a high rating of physical performance, mental health, and quality of life as compared with convalescents affected by prolonged fatigue or neurocognitive complaints. Conclusion Our results underline the heterogeneity of post-acute COVID-19 sequelae calling for tailored management strategies. The persistent smell and taste disorder phenotype is characterized by good clinical, physical, and mental recovery and may pose a minor challenge for public health. Study registration ClinicalTrials.gov: NCT04661462 (survey study), NCT04416100 (observational cohort).

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Adaptive Semi-Supervised Clustering Algorithm with Label Propagation

Cited by 7 publications

References 5 publications

Investigating phenotypes of pulmonary COVID-19 recovery: A longitudinal observational prospective multicenter trial

Investigating phenotypes of pulmonary COVID-19 recovery: A longitudinal observational prospective multicenter trial

Who Is at Risk of Poor Mental Health Following Coronavirus Disease-19 Outpatient Management?

Distinct smell and taste disorder phenotype of post-acute COVID-19 sequelae

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