This repostitory contains tools to make the rigorous analysis of large clinical datases more accesible to the general community. A working knowledge of basic statistics and Python is assumed. By basic stats knowledge I mean the concepts of mean, median, and p-values. By basic Python I mean the ability to install a Python module and its dependencies on a local machine and type one-line Python commands in Terminal.

###Installation

###Quickstart

###Pipeline Observations HR BP RR etc. -----------------> | Patient | data | ===> DataFrame ---> Model Development <--> Model Validation
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Clinic takes Patient data, in the form of an XLS, XLSX, or CSV file, as its input. Each row represents a patient. Each column represents a type of data, such as heart rate, blood pressure, or respiratory rate. (See the Wiki for details on handling missing values and detecting data types.)

Clinic converts these data into a DataFrame, that contains the Patient data and metadata that Clinic needs to process and store its analysis.

From DataFrame, Clinic develops a model to predict the outcome measure and validates that model.