Getdata Graph Digitizer 226 Key

All data were extracted by two independent reviewers. Data tables were generated to extract all relevant data from texts, tables and figures, including: author, year, country, patient number, detection method, duration of follow-up, T category, N category, distant metastasis, positive rates of CD166 overexpression, as well as overall survival (OS) rate. For articles that only provided survival data in a Kaplan-Meier curve, the software GetData Graph Digitizer 2.24 ( -graph-digitizer.com/) was applied to digitize and extract the data.

Data types commonly extracted from EHRs and imported into registries are patient identifiers, demographics, diagnoses, medications, procedures, laboratory results, vital signs, and utilization events. These are discussed further below.

Coding standards for demographic data have been published but are not always used. Demographic data such as education and nationality are often not coded in a standardized approach. Age data are governed by HIPAA and have sharing limitations if they contain a certain level of granularity (e.g., age represented by the exact date of birth or if ages above a certain limit).17 Demographic data are often used by registries to match patient records across data sources. Thus, legal limitations to sharing demographic data may hinder the development of multi-source/multi-site EHR-based registries that require demographic data for these purposes.

Traditionally in field investigations, a public health agency deploys personnel to the geographic area where the investigation is centered, and the investigation is largely led and managed in the field, with periodic reports sent to headquarters. Although site visits are necessary to identify crucial information and establish relationships necessary for the investigation, a shift is occurring to a new normal in which field response data collection is integrated with existing infrastructure, uses jurisdictional surveillance and informatics staff, and uses or builds on existing surveillance systems, tools, and technologies.

In an outbreak setting, routine data management often changes because of new stressors or novel circumstances, particularly the need to almost immediately gather data, produce reports, and inform decision makers and the public (see also Chapters 2 and 3). To assess population groups at highest risk, geographic extent, and upward or downward trends of disease incidence throughout a confirmed outbreak, investigators can use existing surveillance mechanisms. However, such mechanisms might need to be enhanced; for example, investigators might need to

With broad implementation of EHRs, opportunities exist for improving links between healthcare providers and public health departments, making data collection during field investigations more effective and timely (11). Increasingly, public health agencies have been able to establish agreements with healthcare facilities, often at the local level, to support remote access to EHRs for day-to-day surveillance activities. With such access to EHRs, staff can review medical records remotely to gather additional clinical, exposure, or demographic data about a case whose case report has been received through other channels.

Regardless of collection method, after data are digitized, analytic and statistical software can be used to manipulate the data set in multiple ways to answer diverse questions. Additionally, advanced analytic software enables use of other types of data (e.g., electronic real-time data about air or water quality or data acquisition or remote sensing systems, such as continual or automated collection and transmission). Combining these data with geographic information system data can facilitate overlay of environmental and person-centric information by time and place (11).

Anybody have any experience with software (preferably free, preferably open source) that will take an image of data plotted on cartesian coordinates (a standard, everyday plot) and extract the coordinates of the points plotted on the graph

Other answerers assume that you deal with raster image of a graph. But nowadays the good practice is to publish graphs in vector form. In this case you can achieve much higher exactness of the recovered data and even estimate the recovery error if you work with the code of the vector graph directly, without converting it to raster image.

From this figure you can select (by double-clicking) the path you are looking for, copy graphics selection and paste as new Graphics. For converting it backward to list of points you take the element {1, 1, 1}. Now we have the points not in the coordinate system of the graph but in the coordinate system of the PDF file. We need to establish relationship between them.

From these differences you can see how precise is positioning of the ticks in the PDF file. It gives an estimate of error introduced by converting original datapoints into vector graph included in the PDF file. If there are appreciable errors in ticks positioning you can reduce the error by fitting the coordinates of ticks to a linear model. This linear function now can be used to get original coordinates of points of the path (that is in the coordinate system of the plot).

For R users, the package grImport (on CRAN) can import vector graphics and convert them into objects that R can interpret. It assumes that one can convert PDF (or other vector format of interest) to PostScript format. This can be done for example with Inkscape: import (File > Import) your PDF page with your figure into Inkspace and File > Save As > Save as type: > PostScript *.ps. Once you have your *.ps file fallow the grImport vignette Importing Vector Graphics, more relevant being section '4.1. Scraping data from images'.

Note, if your graph is on a page in a multi page PDF file, then you can split the multi-page document with PDFTK builder. Import your one page PDF file in Ikscape and delete any extra elements (extra text, extra graph elements). This wil ease your work in R when trying to catch the coordinates of the graph elements you are interested in. 1e1e36bf2d