MetaPred | Clinical Risk Prediction with Limited Patient Electronic | Machine Learning library

 by   sheryl-ai Python Version: Current License: No License

kandi X-RAY | MetaPred Summary

kandi X-RAY | MetaPred Summary

MetaPred is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning applications. MetaPred has no bugs, it has no vulnerabilities and it has low support. However MetaPred build file is not available. You can download it from GitHub.

MetaPred is a meta-learning framework for Clinical Risk Prediction using limited patient Electronic Health Records (EHRs). We given an example in the following figure:.
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            kandi-support Support

              MetaPred has a low active ecosystem.
              It has 31 star(s) with 5 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 1 have been closed. On average issues are closed in 2 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of MetaPred is current.

            kandi-Quality Quality

              MetaPred has 0 bugs and 0 code smells.

            kandi-Security Security

              MetaPred has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              MetaPred code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              MetaPred does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              MetaPred releases are not available. You will need to build from source code and install.
              MetaPred has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              It has 2710 lines of code, 163 functions and 10 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed MetaPred and discovered the below as its top functions. This is intended to give you an instant insight into MetaPred implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Calculate the prediction
            • Return a tf session
            • Evaluate ROC curve
            • Fit the model
            • Get feed data
            • Convert data to array
            • Compute metrics for the ROC curve
            • Inference method
            • Prepare data
            • Loads the data
            • Constructs a cnn tensor
            • Get fixed timesteps
            • Fills a finetuning model
            • Get fixed code size
            • Loads the data matrix
            • Save weights for a given meta - model
            • Reads the cct data
            • Train the MetaPred model
            • Returns a dictionary of classifiers
            • Returns a dictionary of classifiers
            • Loads the data file
            • Embed a neural network
            • Builds the model
            • Build the graph
            • Save the metatest to a file
            Get all kandi verified functions for this library.

            MetaPred Key Features

            No Key Features are available at this moment for MetaPred.

            MetaPred Examples and Code Snippets

            No Code Snippets are available at this moment for MetaPred.

            Community Discussions

            QUESTION

            What do the numeric arguments for meta_predicate mean in SWI-Prolog?
            Asked 2020-Apr-04 at 18:06

            I am writing a Prolog program, and I am trying to incorporate modules into the program design to encapsulate complexity reduce redundant functionality.

            One feature I am having difficulty with is the use of metapredicates. I would like to define a metapredicate in one module and then import it into a different module; this introduces complications. Fortunately, the meta_predicate directive helps resolve module prefixes, but I am having trouble understanding the arguments as described here: https://www.swi-prolog.org/pldoc/man?section=metapred

            Specifically, I am having trouble with the numeric arguments. As per the documentation:

            The argument is a term that is used to reference a predicate with N more arguments than the given argument term. For example: call(0) or maplist(1, +).

            I understand that an argument denoted by the numeric value will be a term that is used to reference a predicate. What I do not understand is how the referenced predicate could have more arguments than the argument term. Can someone offer a more in-depth explanation of when the numeric argument is appropriate, or an example of when it would be appropriate to use it?

            ...

            ANSWER

            Answered 2020-Mar-29 at 08:30

            It is a formulation that is easy to understand only when one knows what it means, and should probably be redone.

            Source https://stackoverflow.com/questions/60908618

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install MetaPred

            You can download it from GitHub.
            You can use MetaPred like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            CLONE
          • HTTPS

            https://github.com/sheryl-ai/MetaPred.git

          • CLI

            gh repo clone sheryl-ai/MetaPred

          • sshUrl

            git@github.com:sheryl-ai/MetaPred.git

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