EHRtemporalVariability: delineating temporal data-set shifts in electronic health records

Sáez, Carlos; Gutiérrez-Sacristán, Alba; Kohane, Isaac S.; García‐Gómez, Juan Miguel; Avillach, Paul

doi:10.1093/gigascience/giaa079

Cited by 26 publications

(28 citation statements)

References 22 publications

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“…Temporal variability might occur due to, eg, changes in clinical practice or coding. 17 , 19 , 20 Temporal dataset shifts set out similar biases than source-related shifts for ML and model generalization: shall we train a model with data from all the available timespan or select recent data? Could a model in routine clinical use become obsolete?…”

Section: Resultsmentioning

confidence: 99%

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Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

Sáez

Romero

Conejero

et al. 2020

Journal of the American Medical Informatics Association

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Objective Lack of representative COVID-19 data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, where source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. Materials and Methods We used the publicly available nCov2019 dataset, including patient level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities. Results Cases from the two countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting. Conclusion Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.

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Section: Resultsmentioning

confidence: 99%

“…Given the rapidly evolving knowledge and practice changes in COVID-19, using change detection methods or tools like the EHRtemporalVariability could be of great benefit to assess temporal variability in COVID-19 DRNs. 20 , 21 …”

Section: Resultsmentioning

confidence: 99%

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

Sáez

Romero

Conejero

et al. 2020

Journal of the American Medical Informatics Association

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“…The temporal variability 16 assessment of the statistical distributions of the data showed a variable transient state in the distributions of some variables from January to April, possibly associated with the smaller sample size at these moths. Thus, we decided to keep the data from all the period of the study.…”

Section: Methodsmentioning

confidence: 95%

“…After assessment of potential temporal biases using temporal variability statistical methods 16 , and considering not significant temporal changes, we decided keeping the data from all the period of the study. The source variability assessment 17 by comparing differences in data between Mexican states and the type of clinical institutions (TCIs) where patients received medical attention was left as a primary task for this study and is described in section 3.3.…”

Section: Methodsmentioning

confidence: 99%

“…The data processing and analyses were performed using RStudio (version 3.6) and Python (version 3.8). Temporal and source variability –DQ analyses– were performed using the EHRtemporalVariability 16 and EHRsourceVariability 17,24,25 packages. The methods developed in this work are available in our GitHub repository https://github.com/bdslab-upv/covid19-metaclustering.…”

Section: Methodsmentioning

confidence: 99%

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Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

Zhou¹,

Romero²,

Martı́nez-Miranda

et al. 2021

Preprint

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Objective To describe COVID-19 subphenotypes regarding severity patterns including prognostic, ICU and morbimortality outcomes, through stratification based on gender and age groups, as described by inter-patient variability patterns in clinical phenotypes and demographic features. Materials and methods We used the COVID-19 open data from the Mexican Government including patient-level epidemiological and clinical data from 778 692 SARS-CoV-2 patients from January 13, 2020 to September 30, 2020. Inter-patient variability was analyzed by combining dimensionality reduction and hierarchical clustering methods. We produced cluster analyses for all combinations of gender and age groups (<18, 18-49, 50-64, and >64). For each group, the optimum number of clusters was selected combining a quantitative approach using the Silhouette coefficient, and a qualitative approach through a subgroup expert inspection via visual analytics. Using the features of the resultant age-gender clusters, we performed a meta-clustering analysis to provide an overall description of the population. Results We observed a total of 56 age-gender clusters, grouped in 11 clinically distinguishable meta-clusters with different outcomes. Meta-clusters 1 to 3 showed the highest recovery rates (90.27-95.22%). These clusters include: (1) healthy patients of all ages, (2) children with comorbidities who had priority in medical resources, (3) young patients with obesity and smoking habit. Meta-clusters 4 and 5 showed moderate recovery rates (81.3-82.81%): (4) patients with hypertension or diabetes of all ages, (5) typical obese patients with three highly correlated conditions, namely, pneumonia, hypertension and diabetes. Meta-clusters 6 to 11 had very low recovery rates (53.96-66.94%) which include: (6) immunosuppressed patients with the highest comorbidity rate in many diseases, (7) CKD patients with the worse survival length and recovery, (8) elderly smoker with mild COPD, (9) severe diabetic elderly with hypertension, (10, 11) oldest obese smokers with severe COPD and mild cardiovascular disease with the latter (11) showing a relatively higher age and smoke rate, severe COPD and shorter survival length, reinforcing a high correlation between smoking habit and COPD among elderly. Additionally, the source Mexican state and type of clinical institution proved to be an important factor for heterogeneity in severity. Discussion The proposed unsupervised learning approach successfully uncovered discriminative COVID-19 severity patterns for both genders and all age groups from clinical phenotypes and demographic features. A careful read of group outcomes showed consistent results regarding recent literature. Regarding the Mexican population, our results suggest that habits and comorbidities may play a key role in predicting mortality in older patients. Centenarians tended to fall in the groups with better outcomes repeatedly. Additionally, immunosuppression was not found as a relevant factor for severity alone but did when present along with chronic kidney disease. Further useful correlations could be found by evaluating the duration of unhealthy habits, demographic features, comorbidities, the time since diagnosis, recovery progress, readmission record, and the effect of source variability. Conclusion The resultant eleven meta-clusters provide bases to comprehend the classification of patients with COVID-19 based on comorbidities, habits, demographic characteristics, geographic data and type of clinical institutions, as well as revealing the correlations between the above characteristics thereby help to anticipate the possible clinical outcomes for every specifically characterized patient. These subphenotypes can establish target groups for automated stratification or triage systems to provide personalized therapies or treatments. Code available at: https://github.com/bdslab-upv/covid19-metaclustering Dynamic results visualization at: http://covid19sdetool.upv.es/?tab=mexicoGov

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Measuring Variability in Acute Myocardial Infarction Coding Using a Statistical Process Control and Probabilistic Temporal Data Quality Control Approaches

Souza

Caballero

Santos

et al. 2021

Advances in Intelligent Systems and Computing

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EHRtemporalVariability: delineating temporal data-set shifts in electronic health records

Cited by 26 publications

References 22 publications

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

Subphenotyping of COVID-19 patients at pre-admission towards anticipated severity stratification: an analysis of 778 692 Mexican patients through an age-gender unbiased meta-clustering technique

Measuring Variability in Acute Myocardial Infarction Coding Using a Statistical Process Control and Probabilistic Temporal Data Quality Control Approaches

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