• Corpus ID: 16744834

Markov Logic Networks in Health Informatics

@inproceedings{Ghosh2011MarkovLN,
  title={Markov Logic Networks in Health Informatics},
  author={Somnath Ghosh and Nisha Shankar and Sam Owre and Sean P. David and Gary E. Swan and Patrick Lincoln},
  year={2011}
}
Health informatics is a fertile source of applications for data-intensive computing. In this position paper, we discuss some problems in health informatics and present high-level ideas about possible approaches using the framework of probabilistic relational models, in particular Markov Logic Networks (MLNs). 
csl.sri.com
3 Citations
Background Citations
1
Methods Citations
2

Tables from this paper

3 Citations

Probabilistic Modeling of Failure Dependencies Using Markov Logic Networks

We present a methodology for the probabilistic modeling of failure dependencies in large, complex systems using Markov Logic Networks (MLNs), a state-of-the-art probabilistic relational modeling…

Representing Knowledge for Radiation Therapy Planning with Markov Logic Networks

The initial results show the MLNs prediction of risk is in the range of risk suggested in guidelines and can be generalized to represent and reason with uncertain knowledge in radiation oncology for decision support.

Combining Subjective Probabilities and Data in Training Markov Logic Networks

This work shows how one can learn weights in an MLN by combining subjective probabilities and training data, without requiring that the domain expert provides consistent knowledge, and provides a formalism for capturing conditional subjective probabilities.

10 References

Markov logic networks

Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach to combining first-order logic and probabilistic graphical models in a single representation.

The Unified Medical Language System (UMLS): integrating biomedical terminology

The Unified Medical Language System is a repository of biomedical vocabularies developed by the US National Library of Medicine and includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap).

Learning the structure of Markov logic networks

An algorithm for learning the structure of MLNs from relational databases is developed, combining ideas from inductive logic programming (ILP) and feature induction in Markov networks.

Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants

A physiological assessment score for preterm newborns, akin to an electronic Apgar score, based on standard signals recorded noninvasively on admission to a neonatal intensive care unit, and can be quickly calculated by computer as early as 3 hours into the infant’s life.

A proposal for a new method of evaluation of the newborn infant.

    V. Apgar
    Medicine
    Current researches in anesthesia & analgesia
  • 1953
The purpose of this paper is the reestablishment of simple, clear classification or “grading” of newborn infants which can be used as a basis for discussion and comparison of the results of obstetric practices, types of maternal pain relief and the effects of resuscitation.

Technical manual, ver 1.0. CSL, SRI International

    Technical manual, ver 1.0. CSL, SRI International
  • 2009

Online maxmargin weight learning for markov logic networks

    SDM
  • 2011

PCE User Guide , Technical manual , ver 1 . 0 . CSL , SRI International , July 2009 . Richardson , Matthew and Domingos , Pedro . Markov logic networks

    Machine Learning in Health Care Applications

      Machine Learning in Health Care Applications
    • 2008

    PCE User Guide, Technical manual, ver 1.0

      CSL, SRI International,
    • 2009