scholarly journals Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app

2020 ◽  
Author(s):  
Carole H. Sudre ◽  
Karla A. Lee ◽  
Mary Ni Lochlainn ◽  
Thomas Varsavsky ◽  
Benjamin Murray ◽  
...  
Keyword(s):  
Time Series ◽  
Personal Characteristics ◽  
Smartphone Application ◽  
Respiratory Support ◽  
Training Dataset ◽  
Symptom Presentation ◽  
Resource Requirements ◽  
Risk Patients ◽  
Independent Replication ◽  
Clinical Prediction Tool

AbstractAs no one symptom can predict disease severity or the need for dedicated medical support in COVID-19, we asked if documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between May 1-May 28th, 2020. Using the first 5 days of symptom logging, the ROC-AUC of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.One sentence summaryLongitudinal clustering of symptoms can predict the need for respiratory support in severe COVID-19.

scholarly journals Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app

2021 ◽  
Vol 7 (12) ◽  
pp. eabd4177
Author(s):  
Carole H. Sudre ◽  
Karla A. Lee ◽  
Mary Ni Lochlainn ◽  
Thomas Varsavsky ◽  
Benjamin Murray ◽  
...  
Keyword(s):  
Time Series ◽  
Area Under The Curve ◽  
Personal Characteristics ◽  
Smartphone Application ◽  
Training Dataset ◽  
Symptom Presentation ◽  
Resource Requirements ◽  
Risk Patients ◽  
Independent Replication ◽  
Characteristic Area

As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic – area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

scholarly journals Shapelet Discovery by Lazy Time Series Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Wei Zhang ◽  
Zhihai Wang ◽  
Jidong Yuan ◽  
Shilei Hao
Keyword(s):  
Time Series ◽  
Training Dataset ◽  
Class Label ◽  
Considerable Research ◽  
Evaluation Dataset ◽  
Differential Ability ◽  
Global And Local ◽  
Insight Into ◽  
Specific Evaluation ◽  
Feature Frequency

As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training dataset, neglecting characteristic information contained in each instance to be classified and the classwise feature frequency information. Hence, the computational complexity of feature extraction is high, and the interpretability is inadequate. To this end, the efficiency of shapelet discovery is improved through a lazy strategy fusing global and local similarities. In the prediction process, the strategy learns a specific evaluation dataset for each instance, and then the captured characteristics are directly used to progressively reduce the uncertainty of the predicted class label. Moreover, a shapelet coverage score is defined to calculate the discriminability of each time stamp for different classes. The experimental results show that the proposed method is competitive with the benchmark methods and provides insight into the discriminative features of each time series and each type in the data.

Self-Tuning Varri Method in Preparing Fatigue Segment

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
M. H. Osman ◽  
Z. M. Nopiah ◽  
S. Abdullah ◽  
A. Lennie
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Training Dataset ◽  
Series Data ◽  
Additional Advantage ◽  
Local Maxima ◽  
Conventional Procedure ◽  
Computational Operation ◽  
Self Tuning ◽  
The Difference

An overlapping segmentation method on time series data is often used for preparing training dataset i.e. the population of instance, for classification data mining. Having large number of redundant instances would burden the training process with heavy computational operation. This would happen if practitioners fail to acknowledge an appropriate amount of overlap when performing the time series segmentation. Fortunately, the risk could be decreased if knowledge preferences can be determined to guide on overlapping criteria in the segmentation algorithm. Thus, this study aims to investigate how the Varri method is able to contribute for better understanding in preparing training dataset consists of irredundant fatigue segment from the loading history (fatigue signal). Generally, the method locates segment boundaries based on local maxima in the difference function which are above the assigned threshold. In the present study, the mean and standard deviation have been used to define the function due to the fact that predicting attributes are the key components in defining instance redundancy. The resulting dataset from the proposed method is trained by three classification algorithms under the supervision of the Genetic algorithms-based feature selection wrapper approach. The average performance index shows an additional advantage of the proposed method as compared to the conventional procedure in preparing training dataset.

scholarly journals Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 925 ◽  
Author(s):  
Stephen Guth ◽  
Themistoklis P. Sapsis
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Extreme Event ◽  
Binary Classification ◽  
Selection Criterion ◽  
Area Under The Curve ◽  
Wave Model ◽  
Training Dataset ◽  
Ocean Engineering ◽  
Selection Of

The ability to characterize and predict extreme events is a vital topic in fields ranging from finance to ocean engineering. Typically, the most-extreme events are also the most-rare, and it is this property that makes data collection and direct simulation challenging. We consider the problem of deriving optimal predictors of extremes directly from data characterizing a complex system, by formulating the problem in the context of binary classification. Specifically, we assume that a training dataset consists of: (i) indicator time series specifying on whether or not an extreme event occurs; and (ii) observables time series, which are employed to formulate efficient predictors. We employ and assess standard binary classification criteria for the selection of optimal predictors, such as total and balanced error and area under the curve, in the context of extreme event prediction. For physical systems for which there is sufficient separation between the extreme and regular events, i.e., extremes are distinguishably larger compared with regular events, we prove the existence of optimal extreme event thresholds that lead to efficient predictors. Moreover, motivated by the special character of extreme events, i.e., the very low rate of occurrence, we formulate a new objective function for the selection of predictors. This objective is constructed from the same principles as receiver operating characteristic curves, and exhibits a geometric connection to the regime separation property. We demonstrate the application of the new selection criterion to the advance prediction of intermittent extreme events in two challenging complex systems: the Majda–McLaughlin–Tabak model, a 1D nonlinear, dispersive wave model, and the 2D Kolmogorov flow model, which exhibits extreme dissipation events.

scholarly journals Protocolized Post-Extubation Respiratory Support to prevent reintubation: protocol and statistical analysis plan for a clinical trial

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e030476 ◽  
Author(s):  
Jonathan Dale Casey ◽  
Erin R Vaughan ◽  
Bradley D Lloyd ◽  
Peter A Bilas ◽  
Eric J Hall ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Usual Care ◽  
Invasive Mechanical Ventilation ◽  
High Flow ◽  
Respiratory Support ◽  
Invasive Ventilation ◽  
High Flow Nasal Cannula ◽  
Non Invasive ◽  
Non Invasive Ventilation ◽  
Risk Patients

IntroductionFollowing extubation from invasive mechanical ventilation, nearly one in seven critically ill adults requires reintubation. Reintubation is independently associated with increased mortality. Postextubation respiratory support (non-invasive ventilation or high-flow nasal cannula applied at the time of extubation) has been reported in small-to-moderate-sized trials to reduce reintubation rates among hypercapnic patients, high-risk patients without hypercapnia and low-risk patients without hypercapnia. It is unknown whether protocolised provision of postextubation respiratory support to every patient undergoing extubation would reduce the overall reintubation rate, compared with usual care.Methods and analysisThe Protocolized Post-Extubation Respiratory Support (PROPER) trial is a pragmatic, cluster cross-over trial being conducted between 1 October 2017 and 31 March 2019 in the medical intensive care unit of Vanderbilt University Medical Center. PROPER compares usual care versus protocolized post-extubation respiratory support (a respiratory therapist-driven protocol that advises the provision of non-invasive ventilation or high-flow nasal cannula based on patient characteristics). For the duration of the trial, the unit is divided into two clusters. One cluster receives protocolised support and the other receives usual care. Each cluster crosses over between treatment group assignments every 3 months. All adults undergoing extubation from invasive mechanical ventilation are enrolled except those who received less than 12 hours of mechanical ventilation, have ‘Do Not Intubate’ orders, or have been previously reintubated during the hospitalisation. The anticipated enrolment is approximately 630 patients. The primary outcome is reintubation within 96 hours of extubation.Ethics and disseminationThe trial was approved by the Vanderbilt Institutional Review Board. The results will be submitted for publication in a peer-reviewed journal and presented at one or more scientific conferences.Trial registration numberNCT03288311.

scholarly journals Trends, structural changes, and assessment of time series models for forecasting hospital discharge due to death at a Mexican tertiary care hospital

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248277
Author(s):  
Edel Rafael Rodea-Montero ◽  
Rodolfo Guardado-Mendoza ◽  
Brenda Jesús Rodríguez-Alcántar ◽  
Jesús Rubén Rodríguez-Nuñez ◽  
Carlos Alberto Núñez-Colín ◽  
...  
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Structural Changes ◽  
Tertiary Care Hospital ◽  
Tertiary Care ◽  
Training Dataset ◽  
Care Hospital ◽  
Arima Models ◽  
Hospital Discharges ◽  
Total Percentage

Background Data on hospital discharges can be used as a valuable instrument for hospital planning and management. The quantification of deaths can be considered a measure of the effectiveness of hospital intervention, and a high percentage of hospital discharges due to death can be associated with deficiencies in the quality of hospital care. Objective To determine the overall percentage of hospital discharges due to death in a Mexican tertiary care hospital from its opening, to describe the characteristics of the time series generated from the monthly percentage of hospital discharges due to death and to make and evaluate predictions. Methods This was a retrospective study involving the medical records of 81,083 patients who were discharged from a tertiary care hospital from April 2007 to December 2019 (first 153 months of operation). The records of the first 129 months (April 2007 to December 2017) were used for the analysis and construction of the models (training dataset). In addition, the records of the last 24 months (January 2018 to December 2019) were used to evaluate the predictions made (test dataset). Structural change was identified (Chow test), ARIMA models were adjusted, predictions were estimated with and without considering the structural change, and predictions were evaluated using error indices (MAE, RMSE, MAPE, and MASE). Results The total percentage of discharges due to death was 3.41%. A structural change was observed in the time series (March 2009, p>0.001), and ARIMA(0,0,0)(1,1,2)12 with drift models were adjusted with and without consideration of the structural change. The error metrics favored the model that did not consider the structural change (MAE = 0.63, RMSE = 0.81, MAPE = 25.89%, and MASE = 0.65). Conclusion Our study suggests that the ARIMA models are an adequate tool for future monitoring of the monthly percentage of hospital discharges due to death, allowing us to detect observations that depart from the described trend and identify future structural changes.

scholarly journals CT Radiomics Nomogram for the Preoperative Prediction of Severe Post-Hepatectomy Liver Failure in Patients with Huge (≥10 cm) Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Fei Xiang ◽  
Xiaoyuan Liang ◽  
Lili Yang ◽  
Xingyu Liu ◽  
Sheng Yan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Decision Tree ◽  
Liver Failure ◽  
Extent Of Resection ◽  
Meld Score ◽  
Training Dataset ◽  
Clinical Predictors ◽  
Multivariable Logistic Regression Model ◽  
Risk Patients ◽  
Radiomics Signature

Abstract Background To establish a radiomics-based nomogram for predicting severe (grade B or C) post-hepatectomy liver failure (PHLF) in patients with huge (≥10 cm) hepatocellular carcinoma (HCC).Methods 186 patients with huge HCC (n = 131 for training dataset and n = 55 for test dataset) who underwent curative hepatic resection were included. The least absolute shrinkage and selection operator approach was applied to develop the radiomics signature for grade B or C PHLF prediction in the training dataset. A multivariable logistic regression model was used by incorporating radiomics signature and other clinical predictors to establish a radiomics nomogram. A decision tree was created to stratify the risk for severe PHLF.Results The radiomics signature consisting of nine features predicted severe PHLF with an AUC of 0.766 and 0.745 in the training and test datasets, respectively. The radiomics nomogram was generated by integrating the radiomics signature, the extent of resection and model for end-stage liver disease (MELD) score. The nomogram exhibited satisfactory discrimination and calibration, with an AUC of 0.842 and 0.863 in the training and test datasets, respectively. Decision tree split patients into 3 risk classes: low-risk patients with radiomics score < -0.247 and MELD score < 10 or，radiomics score ≥ -0.247 and underwent partial resections; intermediate-risk patients with radiomics score < -0.247 but MELD score ≥10; high-risk patients with radiomics score ≥ -0.247 and underwent extended resections.Conclusions The radiomics nomogram was able to predict severe PHLF in huge HCC patients. Decision tree may be useful in surgical decision-making for huge HCC hepatectomy.

Pulmonary disease and anaesthesia

2017 ◽  
Author(s):  
Gary H. Mills
Keyword(s):  
Critical Care ◽  
Respiratory Disease ◽  
Respiratory System ◽  
Respiratory Muscles ◽  
Postoperative Recovery ◽  
Respiratory Support ◽  
Normal Lung ◽  
High Incidence ◽  
Risk Patients ◽  
The Right

Respiratory adverse events are the commonest complications after anaesthesia and have profound implications for the recovery of the patient and their subsequent health. Outcome prediction related to respiratory disease and complications is vital when determining the risk:benefit balance of surgery and providing informed consent. Surgery produces an inflammatory response and pain, which affects the respiratory system. Anaesthesia produces atelectasis, decreases the drive to breathe, and causes muscle weakness. As the respiratory system ages, closing capacity increases and airway closure becomes an increasing issue, resulting in atelectasis. Increasing comorbidity and polypharmacy reduces the patient’s ability to eliminate drugs. The proportion of major operations on older frailer patients is rising and postoperative recovery becomes more complicated and the demand for critical care rises. At the same time, the population is becoming more obese, producing rapid decreases in end-expiratory lung volume on induction, together with a high incidence of sleep-disordered breathing. Despite this, many high-risk patients are not accurately identified preoperatively, and of those that are admitted to critical care, some are discharged and then readmitted to the intensive care unit with complications. Respiratory diseases may lead to increases in pulmonary vascular resistance and increased load on the right heart. Some lung diseases are primarily fibrotic or obstructive. Some are inflammatory, autoimmune, or vasculitic. Other diseases relate to the drive to breathe, the nerve supply to, or the respiratory muscles themselves. The range of types of respiratory disease is wide and the physiological consequences of respiratory support are complex. Research continues into the best modes of respiratory support in theatre and in the postoperative period and how best to protect the normal lung. It is therefore essential to understand the effects of surgery and anaesthesia and how this impacts existing respiratory disease, and the way this affects the balance between load on the respiratory system and its capacity to cope.

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