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PrositNG - A Machine Learning Supported Disease Model Generation Software.

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Summary
This summary is machine-generated.

Generating decision models (DM) for medical interventions is complex. PrositNG software uses machine learning with real-world data to automate Markov Model creation, streamlining economic evaluations.

Keywords:
electronic health recordsmachine learningmarkov processesmedical economicsreal-world data

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Area of Science:

  • Health economics
  • Medical informatics
  • Machine learning

Background:

  • Decision models (DM), particularly Markov Models, are crucial for the economic evaluation of novel medical interventions.
  • The creation of these models typically demands extensive medical expertise and significant time investment.
  • Automating this process is essential for efficient healthcare research and decision-making.

Purpose of the Study:

  • To introduce PrositNG, a novel software solution designed for automated decision model generation.
  • To demonstrate the capability of PrositNG to connect with and utilize real-world routine care data.
  • To leverage machine learning algorithms for deriving model structures directly from database entries.

Main Methods:

  • PrositNG software was developed using the Java programming language.
  • Machine learning algorithms were employed to infer decision model structures from database content.
  • The software was tested using two distinct real-world data sources.

Main Results:

  • PrositNG successfully generated decision models by analyzing real-world routine care data.
  • The software's ability to connect to database systems was validated.
  • The study confirmed the feasibility of using routine care data for automated model generation.

Conclusions:

  • PrositNG offers a promising approach to automate the generation of decision models for economic evaluations.
  • The integration of machine learning with real-world data sources can significantly reduce the time and expertise required for model development.
  • Effective utilization of PrositNG necessitates a thorough understanding of local documentation practices and the software itself.