When Harry Met Sally, or When Machine Learning Met Chronic Obstructive Pulmonary Disease.
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ABSTRACT:
SUBMITTER: Agusti A
PROVIDER: S-EPMC6999092 | biostudies-literature | 2020 Feb
REPOSITORIES: biostudies-literature
Publications
When Harry Met Sally, or When Machine Learning Met Chronic Obstructive Pulmonary Disease.
American journal of respiratory and critical care medicine 20200201 3
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Project description: Not available
| S-EPMC10106241 | biostudies-literature
Project description:Background and objectivesInterest in reminiscence activities for older adults has grown in recent years, but the benefits of co-reminiscence are not well-known. Drawing from a narrative identity framework, this study examined older adult spouses' co-reminiscence about their first encounters. We hypothesized that perceived closeness and support increase when spouses co-reminisce and that greater perceptions of closeness and support after reminiscing relate to lower depressive symptoms and greater marital satisfaction in daily life.Research design and methodsOne hundred and one couples completed questionnaires measuring marital satisfaction and depressive symptoms at home and then participated in a laboratory session in which they co-reminisced about their first encounters. Self-reported perceived support and relationship closeness were obtained before and after reminiscence. t Tests and the Actor Partner Interdependence Model were used to examine hypotheses.ResultsAs hypothesized, closeness and perceived support increased from pre- to postreminiscence for husbands and wives. In addition, one's own relationship closeness after reminiscence was positively associated with own marital satisfaction (actor effect). One's perceived support after reminiscence was positively related to spouse's marital satisfaction and negatively associated with their spouse's depressive symptoms (partner effects).Discussion and implicationsFindings suggest that co-reminiscence about early relationship development can boost feelings of closeness and support for older adults. Benefiting from co-reminiscence in this way also appears to indicate broader relationship and individual well-being. Brief co-reminiscence activities may nurture late-life relational well-being.
| S-EPMC9710240 | biostudies-literature
Project description:COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also the top performer in sensitivity, specificity, and ppv when predicting a patient's need for emergency care.
| S-EPMC5699810 | biostudies-literature
Project description:Diaphragm muscles in Chronic Obstructive Pulmonary Disease (COPD) patients undergo an adaptive fast to slow transformation that includes cellular adaptations. This project studies the signaling mechanisms responsible for this transformation. Keywords: other
2003-07-16 | GSE475 | GEO
Project description:Investigation of whole genome gene expression level changes of the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10, compared to the normal people and stable COPD patients. A five chip study using total RNA recovered from Peripheral Blood Mononuclear Cell of Peripheral Blood.Evaluating the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10 after the hospital admission, to compared with healthy controls or patients with stable COPD. Slides were scanned at 5 μm/pixel resolution using an Axon GenePix 4000B scanner (Molecular Devices Corporation) piloted by GenePix Pro 6.0 software (Axon). Scanned images (TIFF format) were then imported into NimbleScan software (version 2.5) for grid alignment and expression data analysis. Expression data were normalized through quantile normalization and the Robust Multichip Average (RMA) algorithm included in the NimbleScan software. The Probe level (*_norm_RMA.pair) files and Gene level (*_RMA.calls) files were generated after normalization.
2014-08-14 | E-GEOD-60399 | biostudies-arrayexpress
Project description:Chronic obstructive pulmonary disease (COPD) Metagenome
| PRJNA647843 | ENA
Project description:Investigation of whole genome gene expression level changes of the dynamic gene profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD) on day1, 3 and 10, compared to the normal people and stable COPD patients.
2014-08-14 | GSE60399 | GEO
Project description:BackgroundChronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes.ObjectiveThe aim of this study is to develop a more accurate model to predict severe COPD exacerbations.MethodsWe examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD.ResultsThe final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347).ConclusionsOur model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes.International registered report identifier (irrid)RR2-10.2196/13783.
| S-EPMC8778560 | biostudies-literature
Project description:BACKGROUND:Chronic obstructive pulmonary disease (COPD) is combination of progressive lung diseases. The diagnosis of COPD is generally based on the pulmonary function testing, however, difficulties underlie in prognosis of smokers or early stage of COPD patients due to the complexity and heterogeneity of the pathogenesis. Computational analyses of omics technologies are expected as one of the solutions to resolve such complexities. METHODS:We obtained transcriptomic data by in vitro testing with exposures of human bronchial epithelial cells to the inducers for early events of COPD to identify the potential descriptive marker genes. With the identified genes, the machine learning technique was employed with the publicly available transcriptome data obtained from the lung specimens of COPD and non-COPD patients to develop the model that can reflect the risk continuum across smoking and COPD. RESULTS:The expression levels of 15 genes were commonly altered among in vitro tissues exposed to known inducible factors for earlier events of COPD (exposure to cigarette smoke, DNA damage, oxidative stress, and inflammation), and 10 of these genes and their corresponding proteins have not previously reported as COPD biomarkers. Although these genes were able to predict each group with 65% accuracy, the accuracy with which they were able to discriminate COPD subjects from smokers was only 29%. Furthermore, logistic regression enabled the conversion of gene expression levels to a numerical index, which we named the "potential risk factor (PRF)" index. The highest significant index value was recorded in COPD subjects (0.56 at the median), followed by smokers (0.30) and non-smokers (0.02). In vitro tissues exposed to cigarette smoke displayed dose-dependent increases of PRF, suggesting its utility for prospective risk estimation of tobacco products. CONCLUSIONS:Our experimental-based transcriptomic analysis identified novel genes associated with COPD, and the 15 genes could distinguish smokers and COPD subjects from non-smokers via machine-learning classification with remarkable accuracy. We also suggested a PRF index that can quantitatively reflect the risk continuum across smoking and COPD pathogenesis, and we believe it will provide an improved understanding of smoking effects and new insights into COPD.
| S-EPMC6998147 | biostudies-literature
Project description:BackgroundPatients with chronic obstructive pulmonary disease (COPD) have a high risk of developing lung cancer. Due to the high rates of complications from invasive diagnostic procedures in this population, detecting circulating tumor DNA (ctDNA) as a non-invasive method might be useful. However, clinical characteristics that are predictive of ctDNA mutation detection remain incompletely understood. This study aimed to investigate factors associated with ctDNA detection in COPD patients with lung cancer.MethodsHerein, 177 patients with COPD and lung cancer were prospectively recruited. Plasma ctDNA was genotyped using targeted deep sequencing. Comprehensive clinical variables were collected, including the emphysema index (EI), using chest computed tomography. Machine learning models were constructed to predict ctDNA detection.ResultsAt least one ctDNA mutation was detected in 54 (30.5%) patients. After adjustment for potential confounders, tumor stage, C-reactive protein (CRP) level, and milder emphysema were independently associated with ctDNA detection. An increase of 1% in the EI was associated with a 7% decrease in the odds of ctDNA detection (adjusted odds ratio =0.933; 95% confidence interval: 0.857-0.999; P=0.047). Machine learning models composed of multiple clinical factors predicted individuals with ctDNA mutations at high performance (AUC =0.774).ConclusionsctDNA mutations were likely to be observed in COPD patients with lung cancer who had an advanced clinical stage, high CRP level, or milder emphysema. This was validated in machine learning models with high accuracy. Further prospective studies are required to validate the clinical utility of our findings.
| S-EPMC10891398 | biostudies-literature