Medical Knowledge graph

ES, version 2024-03-25 /

• Introduction:
  • A large amount of biomedical knowledge is already available but ln too many different sources and formats. However this expertise is not always applied in practice.
• Objectives:
  • A medical knowledge base suitable for decision support and including quantitative evaluations.
• Approaches:
  • Steering for decision support:
    • Decision support require a very comprehensive representation of the knowledge. Therefore graphs can represent concepts and the very many relationships between these concepts.
    • Steering information intended to be process-able by the decision support engine.
    • Normally medical experts will work mainly here on the content of knowledge graphs and much less inside the software of the decision engine, except when a new type of rule would appear necessary.
  • Multi-directional navigation:
    
    • From a symptom to potential problems AND from a problem to usual symptoms with their relative importance.
    • From a problem to possible treatment AND from a treatment to all his possible effects, both positive or negative.
    • From a diagnose to frequent complications.
    • .....
  • Concepts:
    • Identification:
      • In medical graphs, what most matter is  a "concept" rather than a "language". As far as possible concepts and relationships should be seen as logical notions.
        
      • Languages:
      • Of course linguistic is necessary in order to manage concepts. But it remains an approximation due to cultural contexts and ambiguities.
      • Medical concepts are in principle independent from any spoken language. For example:
      • The same symptom of "increased central body temperature" as 发 烧, высокая температура, " حمى", fièvre, koorts, Fieber, fiebre, ... although not always exactly one on one translations.
        
      • The problem of gastric ulcer, as 胃 溃 疡, язва желудка, úlcera gástrica, Magengeschwür, maagzweer, قرحة المعدة, ulcère gastrique, ...
      • As far as possible keep compatibility with existing ontologies. At international level one of the most comprehensive identification system seems to be the ULMS, Unified Medical Language System, www.nlm.nih.gov/research/umls/ , including other ontologies and translated in many languages. Or anyhow in order to remain neutral, a prefix indicating which identification system.
        
      • However keep in mind that every existing ontology has been intended for a specific goals, mainly for epidemiology, e.g. IDC from the WHO, or insurance purposes, or for retrieval in scientific literature. Decision support could require a little different approach.
    • Standardization:
    • Raw data should be transformed in standardized format. For example about the concept of " increased internal body temperature " we need a function providing  meaningful coefficient indicating if it true that it is increased and how far, regardless of units (Fahrenheit, Celsius, ...), taking account of meaningful physiologic values.
    • Raw data available as classes should also be standardized as a kind of coefficient. For example pain scales from 1 to 10 and why not from 0.0 to 1.0 .
    • GWT, "Global Workspace Theory":
    • Visual graph representing a small selection of the relevant concepts in the current context. This can be seen as a kind of awareness or consciousnesses.
      
  • Relations between concepts:
    • Identification:
      • A set of relationships types between concepts will be defined, for example "suggest", "confirm", "exclude", "recommend", etc...
  • Attributes of objects:
    • Introduction:
    • In general objects, both concepts and relationships, have at least a kind of "weight" attribute, but many more attributes should be considered.
    • In many cases attributes values are optional and not exclusive.
    • Fuzzy Logic could require a standardization from 0. to 1. 
    • Likelihood:
      • Medical decision cannot be better than based on likelhoods.
        
      • Representation of likelihood in 3 different ways:
      • Precise numerical figures intended to be used inside the decision engine.
      • Textual expressions intended for traditional reports. Here the coefficients are rounded, for example 0.9 as "evident", 0.7 as "probable", .5 as "possible", 0.2 as "not to exclude". ... or in time dimension 1 as "permanent", 0.7 as "frequent", 0.3 as "occasional", 0.1 as "very rare".
      • Graphs visual attributes intended for quick understanding of the situation, as for example relative size of visual object in a graph, color scale from red to blue.
    • Specificity:
    • Sensitivity:
    • Positive or negative relation:
    • Potential consequences, dangerousity ?
    • Evolution, as acute or chronic.
      
    • ...
  • Synthesis of knowledge:
    • In order to provide Assisted Intelligence in Decision Support, we need the best possible current synthesis of medical knowledge. The sources of knowledge are:
      • Courses of professors teaching medicine in universities.
      • CME, Continuing Medical Education networks, to be converted and restructured as graphs.
      • Agreed common opinions in scientific associations of specialists, e.g. in cardiology, pneumology, gastro, etc...
        
      • Textbooks, if up to date.
    • Healthcare professionals in practice have not much time nor interest to delve directly in the details of scientific publications. Most of the time they relay on opinions of trusted experts.
    • A separate chapter is dealing with research issues, about improvement of the current synthesis of medical knowledge, particularly improvement of coefficients of relationships.
      
  • Procedures:
    • Nodes may contain reference to procedures in the decision support engine.
• Data Model:
  • Most essential concepts:
  • Knowledge base:
    • Definition of 3 sets of concepts:
      • Some  "Observations", eg symptoms, lab tests.
      • Some  "Problems", eg issues or diagnoses. An abnormal observation may be also a problem.
        
      • Some  "Actions", seeking more information or recommend treatment.  For example which questions should be asked, which lab test should be ordered.
    • Relations between these 3 sets:
      • What most matter here is the relations between concepts.  Relations will be qualified with a type, relative weights, etc...
    • Here nodes represent knowledge, e.g. knowledge abourt fever.
      
  • Patients:
    • A few experimental patient instances with:
      • A few profile information, name sex, DoB, ..
      • Having a few instance of results of Observations.
      • Will get a few instances of "Problems" with their current relative likelihood.
      • May get a list of recommended actions, sorted on their relative priority. For example in emergency situation what to do next.
    • Here nodes are owned by a particular patient and represent the value of an observation at a given time, e.g. 39 °celsius. Of course value nodes are closely related to knowledge nodes.
      
  • Care provider:
    
  • ...
    
• Knowledge sources:
  • Many biomedical knowledge bass already exist:
    • Traditional data bases:
      • As far as possible extraction of knowledge by means of NLP.
        
    • Graphs:
      • Some knowledge bases are already formatted as graph but could need adaptation and extensions. For example schemas of coagulation factors are already a graph.
    • Initial values of coefficients may need to be guessed by experts and tuned later on.
    • Multi-modal, accepting any kinds of information and transformation to logical concepts.
      
  • Anyhow medical experts have to validate the knowledge:
    • A good human interface is here very important.
• Expected results:
  • Make assisted Intelligence possible.
  • Support of teaching and training of medical students.
• Work in progress:
  • Results:
    • Graph extracted from natural language sources are already created from RxNorm, MED-RT and MeSH.
    • A preliminary list of common relation types found in these sources.
  • Next steps:
    • At a begin stage experimental work on relatively small graphs in only a few limited medical domains. Every contribution is welcomed even drawings on paper.
    • More sources of knowledge must be explored as medicine courses and textbooks.
    • Seek more about the weights of relationships.
    • Experimental setup of information about some specific challenges, for example starting from a symptoms like dyspnoea, icterus, antibiotic, ... with directly related knowledge.
• Basic schemas examples:
  • Starting from a given symptom, what could be the Problem(s) ?
  • Starting from every Problem, what are usual symptoms which should be investigated ?
  • In next versions attributes as likelihood, severity, etc ... will have visual styles like shape, size, color, thickness, etc...
    
• Contact persons:
  • etienne@saliez.be , ....