The National Comorbidity Survey: Baseline (NCS-1) was the first large-scale field survey of mental health in the United States. Conducted from 1990–1992, disorders were assessed based on the diagnostic criteria of the then-most current DSM manual, the DSM-III-R (Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised). The study has had large-scale implications on mental health research in the United States, as no widespread data on the prevalence of mental illness was previously available. The National Comorbidity Survey: Reinterview (NCS-2) was a follow up study conducted between 2001 and 2002. The participants in NCS-1 were re-interviewed with the aim to collect information about changes in mental disorders, substance use disorders, and the predictors and consequences of these changes over the ten years between the two surveys. In conjunction with the NCS-2, two other surveys were done. The National Comorbidity Survey Replication (NCS-R) was a study done with 9,282 new participants. And the National Comorbidity Survey: Adolescent Supplement (NCS-A) was a study done between 2000 and 2004 with 10,000 adolescents. (Wikipedia).
Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
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From playlist Statistics (Full Length Videos)
#21. Finding the Sample Size Needed to Estimate a Population Proportion using StatCrunch
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From playlist Statistics Final Exam
Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
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From playlist Statistics (Full Length Videos)
In this video I show you how to do simple descriptive statistics, including calculating the average and standard deviation of variables.
From playlist Healthcare statistics with SPSS
ELLIS against Covid-19 on April 1st
Recording of the ELLIS against Covid-19 workshop on April 1st 2020. Find more info about the workshop here: https://ellis.eu/covid-19 Send questions and comments to the speakers here: https://forms.gle/Nor7CNsNpmngvxP2A
From playlist Video-Empfehlungen
Statistics Lecture 5.4: Finding Mean and Standard Deviation of a Binomial Probability Distribution
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4 Common Misconceptions About OCD
The term "OCD" often gets thrown around lightly by people describing their affinity for neatness, but actual obsessive-compulsive disorder is a serious condition that goes well beyond just cleaning and counting. Hosted by: Brit Garner ---------- Support SciShow by becoming a patron on Pat
From playlist Disorders
Statistics - How to calculate the coefficient of variation
In this video I'll quickly show you how to find the coefficient of variation. There are two formulas for samples and populations, but these are basically the same and involve dividing the standard deviation by the mean and lastly converting to a percent. The coefficient of variation is u
From playlist Statistics
Transfer Learning From Existing Diseases Via Hierarchical Multi-Modal BERT Models to Predict COVID19
Presented by: Khushbu Agarwal - Senior Research Scientist at Pacific Northwest National Laboratory Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we
From playlist Healthcare NLP Summit 2022
Application of Spark NLP for Development of Multi-Modal Prediction Model from EHR | Healthcare NLP
Get your Free Spark NLP and Spark OCR Free Trial: https://www.johnsnowlabs.com/spark-nlp-try-free/ Register for NLP Summit 2021: https://www.nlpsummit.org/2021-events/ Watch all Healthcare NLP Summit 2021 sessions: https://www.nlpsummit.org/ Different data sources such as structured da
From playlist Healthcare NLP Summit 2021
Utilizing NLP for Structure Learning to Understand Cancer Screening Uptake
Install NLP Libraries https://www.johnsnowlabs.com/install/ Register for Healthcare NLP Summit 2023: https://www.nlpsummit.org/#register Watch all NLP Summit 2022 sessions: https://www.nlpsummit.org/nlp-summit-2022-watch-now/ Presented by Matthew Davis, PhD Candidate in Biomedical Dat
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Tracy Lieu, Kaiser Permanente Northern California - Stanford Big Data 2015
Bringing together thought leaders in large-scale data analysis and technology to transform the way we diagnose, treat and prevent disease. Visit our website at http://bigdata.stanford.edu/.
From playlist Big Data in Biomedicine Conference 2015
Agent-based simulators for the study of COVID-19 spread by Rajesh Sundaresan
The talk will provide an overview of epidemiological agent-based models. We will discuss why agent-based models are useful, the components of agent-based models, and the issues related to their validation. We will then turn to COVID-19 and will survey a few agent-based models in use. Subse
From playlist ICTS Colloquia
Statistics Lecture 5.2: A Study of Probability Distributions, Mean, and Standard Deviation
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From playlist Statistics (Full Length Videos)
Covariance (3 of 17) Population vs Sample Variance
Visit http://ilectureonline.com for more math and science lectures! To donate:a http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will learn the difference and calculate the variance of a population and the variance of a sample of a population. Next video in
From playlist COVARIANCE AND VARIANCE
Making the most of health data: Marlous Hall, The University of Leeds
About the event This workshop is being held in association with a new call for applications for joint research awards in Cardiovascular Data Science by the British Heart Foundation (BHF) and The Alan Turing Institute (the Turing). The awards aim to support cross-disciplinary teams of inve
From playlist Exploring novel opportunities for data science in cardiovascular research
Pandemic Exposures: Economy and Society in the Time of Coronavirus
Friends Book Discussion "Pandemic Exposures: Economy and Society in the Time of Coronavirus" edited by Didier Fassin and Marion Fourcade November 9, 2022
From playlist Friends of the Institute
Statistics Lecture 3.4: Finding Z-Score, Percentiles and Quartiles, and Comparing Standard Deviation
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From playlist Statistics (Full Length Videos)
Harvard Medical School derives richer health research through patient app
A professor at Harvard Medical School discusses how researchers obtained much deeper, more valuable information from patients by engaging them in a social network.
From playlist Health IT