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31.
A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity. 相似文献
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Xinran Liu James Anstey Ron Li Chethan Sarabu Reiri Sono Atul J. Butte 《Applied clinical informatics》2021,12(2):407
Background Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers. 相似文献
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Molly Orcutt Wendy C. King Melissa A. Kalarchian Michael J. Devlin Marsha D. Marcus Luis Garcia Kristine J. Steffen James E. Mitchell 《Surgery for obesity and related diseases》2019,15(2):295-303
Background
A history of childhood maltreatment and psychopathology are common in adults with obesity.Objectives
To report childhood maltreatment and to evaluate associations between severity and type of childhood maltreatment and lifetime history of psychopathology among adults with severe obesity awaiting bariatric surgery.Setting
Four clinical centers of the Longitudinal Assessment of Bariatric Surgery Research Consortium.Methods
The Childhood Trauma Questionnaire, which assesses presence/severity (i.e., none, mild, moderate, severe) of physical abuse, mental abuse, physical neglect, mental neglect, and sexual abuse, was completed by 302 female and 66 male bariatric surgery patients. Presurgery lifetime history of psychopathology and suicidal ideation/behavior were assessed with the Structured Clinical Interview for DSM-IV and the Suicidal Behavioral Questionnaire-Revised, respectively. Presurgery lifetime history of antidepressant use was self-reported.Results
Two thirds (66.6%) of females and 47.0% of males reported at least 1 form of childhood trauma; 42.4% and 24.2%, respectively, at greater than or equal to moderate severity. Among women, presence/greater severity of childhood mental or physical abuse or neglect was associated with a higher risk of history of psychopathology (i.e., major depressive disorder, posttraumatic stress disorder, other anxiety disorder, alcohol use disorder, binge eating disorder), suicidal ideation/behavior and antidepressant use (P for all ≤ .02). These associations were independent of age, race, education, body mass index, and childhood sexual abuse. Childhood sexual abuse was independently associated with a history of suicidal ideation/behavior and antidepressant use only (P for both ≤ .05). Statistical power was limited to evaluate these associations among men.Conclusion
Among women with obesity, presence/severity of childhood trauma was positively associated with relatively common psychiatric disorders. 相似文献38.
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