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Decision Support in Heart Disease Prediction System using Naïve Bayes. Presented by Robert Karam March 19, 2014. Background – EHR & CDSS. Electronic Health Records: Collection of patient or population health information

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Decision support in heart disease prediction system using na ve bayes

Decision Support in Heart Disease Prediction System using Naïve Bayes

Presented by Robert Karam

March 19, 2014


Background ehr cdss
Background – EHR & CDSS Naïve Bayes

  • Electronic Health Records: Collection of patient or population health information

  • Clinical Decision Support System: The intelligent use of EHRfor better patient care, assist healthcare providers at the point of care, either pre- or post-diagnosis, or during treatment

  • CDSS has increased the value of EHR, but it can be improved

  • A major topic in artificial intelligence in medicine


Clinical decision support
Clinical Decision Support Naïve Bayes

  • CDS systems can answer simple questions

    • Basic patient or population statistics

    • Drug interactions / cross referencing

    • E.g. Average age of patient with heart disease? How many surgeries resulted in hospital stays greater than 10 days? Etc.

  • ..but EHRs contain a large amount of data

  • More intelligent CDS systems may capitalize on this!

  • Key: use Data Mining techniques to discover hidden trends, patterns, and useful knowledge within the EHRs


Na ve bayes refresh
Naïve Bayes (Refresh) Naïve Bayes

  • Bayes’ Theorem provides a relationship between the probabilities of events A and B, and the conditional probabilities of A given B, and B given A

  • A Naïve Bayes Classifier assumes total independence between events (or features, in this case)

  • Performs relatively well in common, real-world (i.e. complex) problems despite its simplicity


Overview of decision support system
Overview of Decision Support System Naïve Bayes

  • Implemented as a web application / questionnaire

  • Gathers patient information that may be relevant to diagnosing heart disease

    • e.g. patient age, sex, type of chest pain, blood sugar, EKG abnormalities, maximum heart rate, smoking, serum cholesterol, etc.

    • 15 features used in all

  • Designed to aid physician in determining best course of action

    • Uses a training database of historical heart disease diagnoses

    • Database offers a larger pool of ‘experience’ for that physician (i.e. they may have seen 100 heart disease patients and could make a judgement based on that, but this provides another 1000 samples)

  • Outputs a binary diagnosis (no: < 50% diameter narrowing, or yes: >50% diameter narrowing)


Why nbc
Why NBC? Naïve Bayes

  • Patients are different, so feature independence is important

  • NBC…

    • Works well with high dimensional data

    • Computation is trivial (when independence assumed!)

    • Relatively few samples required for parameter (evidence) estimation

  • However,

    • Quality of diagnosis depends on quality of training data

    • May be outperformed by other classifiers (e.g. decision trees or SVM)

  • 15 features selected – is that enough? Patient history + family history?

  • As a diagnosis tool, it may sufficiently aid healthcare practitioners regardless


Discussion
Discussion Naïve Bayes

  • Diagnosis is a multi-faceted problem, many different aspects involved

  • Knowledge discovery in EHR databases may yield a wealth of information and support

  • Many unanswered questions:

    • No results presented or examples of use (real or synthetic data?)

    • No mention of model performance

    • No description (size?) of training database.

    • No details given why these 15 (of 60+) features were selected

  • All patients are different, but advanced data mining algorithms and techniques may work around this problem to provide smarter tools to aid physicians in targeted patient care


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