Obstetric outcomes among women with and without suicidal behavior during delivery hospitalizations (N = 23,507,597).

<p>Obstetric outcomes among women with and without suicidal behavior during delivery hospitalizations (N = 23,507,597).</p

Additional file 1: of Adverse obstetric and neonatal outcomes complicated by psychosis among pregnant women in the United States

Table S1. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes, Diagnosis-Related Group (DRG) codes used to determine delivery-related hospitalizations. Table S2. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes used to determine selected baseline characteristics and obstetric and neonatal outcomes. Table S3. Obstetric and neonatal outcomes among women with and without psychosis during delivery hospitalizations (N=23,507,597). Table S4. Obstetric and neonatal outcomes among women with and without psychosis during singleton delivery hospitalizations (N = 23,076,251). (DOCX 19 kb

Unifying the US National Health and Nutrition Examination Survey: a database of human exposomes and phenomes

The National Health and Nutrition Examination Survey (NHANES) is an epidemiological survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of a representative population of the United States. While publicly available, analysts need to spend a significant amount of time merging and stitching together numerous separate and proprietary formatted data files to incorporate the data in their research. This Data Descriptor describes a single unified and universally accessible data file, merging across 251 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available in .csv and .Rdata format

Socio-demographic and baseline characteristics of women with and without suicidal behavior at delivery hospitalizations (N = 23,507,597).

<p>Socio-demographic and baseline characteristics of women with and without suicidal behavior at delivery hospitalizations (N = 23,507,597).</p

Characteristics of hospitals where women with and without suicidal behavior related-hospitalizations being hospitalized (N = 23,507,597).

<p>Characteristics of hospitals where women with and without suicidal behavior related-hospitalizations being hospitalized (N = 23,507,597).</p

Screenshot of BigQ-NGS Plugin with user interactions highlighted.

<p>(1) The user creates a query by dragging and dropping different blocks inside the plugin’s workspace. Each block represents a query on a single attribute that will be performed by the NoSQL-NGS Cell. After the blocks are connected to each other, the query is defined. (2) A patient set, previously created with a standard i2b2 query, is dragged and dropped on the Patient Result Set Drop (PRS Drop) block to define the patients whose exomes will be queried. (3) By double-clicking the standard query blocks (in yellow), it is possible to specify their query logic and query parameters. (4) Afterwards, the query process can start, and each block executes its query sequentially, calling the NoSQL-NGS Cell. (5) When all blocks have performed their query, the user can visualize the results by double-clicking the Patient Result Set Table (PRS Table) block.</p

Display of counts per population in two subgroups in i2b2/tranSMART (use case 3).

<p>Display of counts per population in two subgroups in i2b2/tranSMART (use case 3).</p

Overview of the three different approaches.

<p>1) Using i2b2 by adding patient facts that have concepts coded per the Genome Sequence Ontology, 2) using i2b2/tranSMART by adding patient facts represented by a unique ontology allowing greater variant exploration, 3) using i2b2 by generating a patient set from i2b2 Star Schema database contained phenotypes and then using an alternate NoSQL-NGS variant storage to complete the genomic part of the query.</p

Classical i2b2 user interface for use case 1.

<p>Which individuals with a lower mode of HLA-DQB1 protein levels (i.e., HLA-DQB1 log protein ratio < 0) have missense or nonsense mutations in that gene? The available ontologies are displayed on the left side and the phenotypic and genotypic concepts used to build the query are shown on the right.</p

System components and their inter-relationships.

<p>The Data annotation/upload process requires the user to provide one or more VCF files that are functionally annotated with ANNOVAR and used to create one JSON document for each variant belonging to a single patient; these JSONs are stored inside CouchDB to be queried by the BigQ-NGS Cell. On the client-side, the BigQ-NGS Plugin allows the user to create a genetic query with drag-and-drop interactions within the i2b2 Webclient; afterwards the plugin communicates with the cell to run the query and collect the results that are shown to the user.</p