Secondary Analysis of Electronic Health Records Quotes

“Fundamental to the feasibility of multidimensional collaborations is the ability to ensure accuracy of large-scale data and integrate it across multiple health record technologies and platforms. Efforts to ensure data quality and accessibility must be promoted”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Viewing the Dataset There are several commands in R that are very useful for getting a ‘feel’ of your datasets and see what they look like before you start manipulating them. View the first and last”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Setting Working Directory This step tells R where to read in the source files. Command: setwd(“directory_path”) Example: (If all data files are saved in directory “MIMIC_data_files”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“NAN – ‘not a number’, only applying to numeric vectors. NULL – ‘empty’ value or set. Often returned by expressions where the value is undefined. Inf – value for ‘infinity’ and”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“R usually stores these tables as ‘data-frames’ when they are read into R. Special Values in R NA – ‘not available’, usually a default placeholder for missing values.”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“table with rows (observations) and columns (variables) Lists and data-frames are treated as their own ‘class’ in R. Query output from MIMIC commonly will be in the form of data tables with different”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“also usually does not allow mixing of data types for a variable, except in a: List – as a one dimensional vector, e.g. c(“vancomycin”, 1.618, “red”) Data-frame – as a”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Integer – e.g. -1, 0, 1, 2, 3 Character – e.g. “vancomycin”, “metronidazole” Logical – TRUE, FALSE Factors/categorical – e.g. male or female under”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“strictly stores data in several different data types, called ‘classes’: Numeric – e.g.”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“12.3 PART 2—Examples of Data Pre-processing in R There are many tools for doing data pre-processing available, such as R, STATA, SAS, and Python; each differs in”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Tidy” datasets have the advantage of being more easily visualized and manipulated for later statistical analysis. Datasets exported from MIMIC usually are fairly “tidy” already; therefore, rule 2 is hardly ever broken. However,”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“columns and rows of a dataset so that it conforms with the following 3 rules of a “tidy” dataset [2, 3]: 1. Each variable forms a column 2. Each observation forms a row 3. Each value has its”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“One common MIMIC database example is collapsing the ICD9 codes into broad clinical categories or variables of interest and assigning patients to them.”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“more effective representation of the dataset without compromising the integrity of the original data. The objective of this step is to provide a version of the dataset on which the subsequent statistical analysis will be more effective. Data reduction”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“complete dataset it will be necessary to integrate the patient’s full set of lab values (including those not associated with the same MIMIC ICUSTAY identifier) with the record of that ICU admission without repeating or missing records. Using shared”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Data integration is the process of combining data derived from various data sources (such as databases, flat files, etc.) into a consistent dataset. There are a number of issues to consider”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“EHR result from information being entered into the database by thousands of individual clinicians and hospital staff members, as well as captured from a variety of automated interfaces between the EHR and everything from”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Knowledge engineering tools may also be used to detect the violation of known data constraints. For example, known functional dependencies among attributes can be used to find values contradicting the functional”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Inconsistent Data There may be inconsistencies or duplications in the data. Some of them may be corrected manually using external references. This is the case,”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“may be detected by clustering, that is by grouping a set of values in such a way that the ones in the same group (i.e., in the same cluster) are more similar to each other than to those in other groups. Machine learning. Data can be smoothed”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Binning methods. Binning methods”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Clinicians are often aware of the source of error and can repeat the measurement then ignore the known incorrect outlier value when planning care. However, clinicians cannot remove the erroneous measurement from the medical record”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“aware of the source of error and can repeat the measurement then ignore the known incorrect outlier value when planning care. However, clinicians cannot remove the erroneous measurement from the medical record”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Noisy Data We term noise a random error or variance in an observed variable—a common”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Use an expected value. The missing values can be filled in with predicted values (e.g. using the mean of the available data or some prediction method). It must be”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Determine and fill in the missing value manually. In general, this approach is the most accurate but it is also time-consuming and often is not feasible in a large dataset with many missing”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Here, we describe three possible ways to deal with missing data [1]: Ignore the record. This method is not very effective, unless the record (observation/row) contains”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Understand the requirements for a “clean” database that is “tidy” and ready for use in statistical analysis. Understand the steps of cleaning raw data, integrating data, reducing and reshaping”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Be able to apply basic techniques for dealing with common problems with raw data including missing data inconsistent data, and data from multiple sources.”
― Mit Critical Data, Secondary Analysis of Electronic Health Records

“Github provide a simple mechanism for backing up content, helping to reduce the risk of data loss, and also provide tools for tracking issues and tasks [8, 9].”
― Mit Critical Data, Secondary Analysis of Electronic Health Records