Phil 1.13.16

7:00 – 3:00 VTX

  • More document coding
  • Review today?
  • On to Chapter 6.
  • Thinking about next steps.
    • Server
      • Produce a dictionary from a combination of manual entry and corpus extraction
      • Add word-specific code like stemming, edit distance
      • Look into synonyms. They are dictionary specific (Java as in drink, Java as in Language, Java as in island)
      • Analyze documents using the dictionary to produce the master network of items and associations. This resides on the server.  I think this negates the need for flags, since the Eigenrank of the doctor will be explained by the associations, and the network can be interrogated by looking for explanatory items within some number of hops. The dictionary entry that was used to extract that item is also added to the network as an item
        • PractitionerDictionary finds medical practitioners <membership roles?>. Providers are added to the item table and to the master network
          • Each practitioner is checked for associations like office, hospital, specialty. New items are created as needed and associations are created
        • LegalDictionary finds (disputes and findings?) in legal proceedings, and adds legal items that are associated with items currently in the network. Items that are associated with GUILTY get low (negative?) weight. A directly attributable malpractice conviction should be a marker that is always relevant. Maybe a reference to it is part of the practitioner record directly?
        • SocialDictionary finds rating items from places like Yelp. High ratings provide higher weight, low ratings produce lower weight. The weight of a rating shouldn’t be more important than a conviction, but a lot of ratings should have a cumulative effect.
        • Other dictionaries? Healthcare providers? Diseases? Medical Schools?
        • Link age. Should link weight move back to the default state as a function of time?
        • Matrix calculation. I think we calculate the rank of all items and their adjacency once per day. Queries are run against the matrix
      • Client
        • Corporate
          • The user is presented with an dashboard ordered by pre-specified criteria (“show new bad practitioners?”). This is calculated by the server looking through the eigenrank starting at the top looking for N items that contain text/tags that match the query (high Jacquard index?). It returns the set to eliminate duplication. The dictionary entries that were associated with the creation of the item are also returned.
        • Consumer
          • The user types in a search: “cancer specialist maryland carefirst”
          • The search looks through the eigenrank starting at the top looking for N items that contain text/tags that match the query (high Jacquard index?). It returns the set to eliminate duplication. The dictionary entries that were associated with the creation of the item are also returned.
        • Common
          • In the browser, the section(s) of the network are reproduced, and the words associated with the items are displayed beside search results, along with sliders that adjust their weights on the local browser network. If the user increases the slider items associated with that entry rise (as does the entry in the list?). This allows the user to reorder their results based on interactive refinement of their preferences.
          • When the user clicks on a result, the position of the clicked item, the positions of the other items, and the settings of the entry sliders is recorded on the server (with the user info?). These weights can be fed back into the master network so that the generalized user preferences are reflected. If we just want to adjust things to the particular user, the Eigenrank will have to be recalculated on a per user basis. I think this does not have to include a full network recalculation.
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