How to validate recommender model in healthcare?












0












$begingroup$


In order to validate a recommender model, a usual approach is create a hold-out set that will provide random suggestions (similar to an A/B testing setup).
However, in healthcare applications, this cannot be possible as a random suggestion can put at risk a patient's life.
Hence, what is a reasonable approach to validate the model?






recommender-system data-product healthcare







share|improve this question











$endgroup$












  • $begingroup$
    Could you provide a little bit more detail about what sort of work you're doing? I'm assuming a lot, like that the randomness relates to group assignment and not the type of treatment itself, but there isn't much detail here.
    $endgroup$
    – Upper_Case
    6 hours ago
















0












$begingroup$


In order to validate a recommender model, a usual approach is create a hold-out set that will provide random suggestions (similar to an A/B testing setup).
However, in healthcare applications, this cannot be possible as a random suggestion can put at risk a patient's life.
Hence, what is a reasonable approach to validate the model?






recommender-system data-product healthcare







share|improve this question











$endgroup$












  • $begingroup$
    Could you provide a little bit more detail about what sort of work you're doing? I'm assuming a lot, like that the randomness relates to group assignment and not the type of treatment itself, but there isn't much detail here.
    $endgroup$
    – Upper_Case
    6 hours ago














0












0








0





$begingroup$


In order to validate a recommender model, a usual approach is create a hold-out set that will provide random suggestions (similar to an A/B testing setup).
However, in healthcare applications, this cannot be possible as a random suggestion can put at risk a patient's life.
Hence, what is a reasonable approach to validate the model?






recommender-system data-product healthcare







share|improve this question











$endgroup$




In order to validate a recommender model, a usual approach is create a hold-out set that will provide random suggestions (similar to an A/B testing setup).
However, in healthcare applications, this cannot be possible as a random suggestion can put at risk a patient's life.
Hence, what is a reasonable approach to validate the model?






recommender-system data-product healthcare




recommender-system data-product healthcare






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited 6 hours ago









Brian Spiering

4,2581129




4,2581129










asked 7 hours ago









tashuhkatashuhka

356310




356310












  • $begingroup$
    Could you provide a little bit more detail about what sort of work you're doing? I'm assuming a lot, like that the randomness relates to group assignment and not the type of treatment itself, but there isn't much detail here.
    $endgroup$
    – Upper_Case
    6 hours ago


















  • $begingroup$
    Could you provide a little bit more detail about what sort of work you're doing? I'm assuming a lot, like that the randomness relates to group assignment and not the type of treatment itself, but there isn't much detail here.
    $endgroup$
    – Upper_Case
    6 hours ago
















$begingroup$
Could you provide a little bit more detail about what sort of work you're doing? I'm assuming a lot, like that the randomness relates to group assignment and not the type of treatment itself, but there isn't much detail here.
$endgroup$
– Upper_Case
6 hours ago




$begingroup$
Could you provide a little bit more detail about what sort of work you're doing? I'm assuming a lot, like that the randomness relates to group assignment and not the type of treatment itself, but there isn't much detail here.
$endgroup$
– Upper_Case
6 hours ago










1 Answer
1






active

oldest

votes


















0












$begingroup$

You should still be able to use a validation set to evaluate the model, whether or not you pursue an experimental approach. (Specific features of your model and investigations may tweak these, but this is based on what's already been posted alone).



There is nothing wrong with A/B group assignment and testing in a medical context, with a few caveats (this list is not exhaustive):




  • The relevant clinical/medical knowledge must be in a state of
    equipoise (it's not already clear that one approach is better than
    another, or which is better is genuinely not known).

  • Individuals should be aware that they are participating in a study, and that they are being routed to
    group A or B, and have the option to decline their assignment (or,
    conversely, they have been made aware of the experimental assignment
    and have consented to participate in advance).

  • An institutional review board should evaluate your proposed
    experiment and signed off on it. This, of course, presupposes that
    you have access to such a board composed of members able to make
    those assessments.


Those can be a tall order, but you don't necessarily have to perform a prospective, double-blind experimental study in order to glean some information. A retrospective study could provide some insight as well, and your process for the validation set would be something like:




  1. Prepare your recommender model

  2. Feed your data through the model, without looking at actual outcomes

  3. Match your model output to actual outcomes to see whether or not
    people followed the recommendation (whether they ever saw that
    recommendation or not)

  4. Compare the results of people that ended up going with each
    recommended approach (A vs. B), as well as those who "followed" the
    recommendations or not (Recommended-A-did-A vs. Recommended-A-did-B,
    etc.)


Retrospective studies are generally not as good as well-designed, well-executed prospective experimental studies, but they can still provide a lot of information. In situations where prospective experimentation is impossible or undesirable, the information a retrospective study provides may be the very best you can actually get.






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    1 Answer
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    1 Answer
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    0












    $begingroup$

    You should still be able to use a validation set to evaluate the model, whether or not you pursue an experimental approach. (Specific features of your model and investigations may tweak these, but this is based on what's already been posted alone).



    There is nothing wrong with A/B group assignment and testing in a medical context, with a few caveats (this list is not exhaustive):




    • The relevant clinical/medical knowledge must be in a state of
      equipoise (it's not already clear that one approach is better than
      another, or which is better is genuinely not known).

    • Individuals should be aware that they are participating in a study, and that they are being routed to
      group A or B, and have the option to decline their assignment (or,
      conversely, they have been made aware of the experimental assignment
      and have consented to participate in advance).

    • An institutional review board should evaluate your proposed
      experiment and signed off on it. This, of course, presupposes that
      you have access to such a board composed of members able to make
      those assessments.


    Those can be a tall order, but you don't necessarily have to perform a prospective, double-blind experimental study in order to glean some information. A retrospective study could provide some insight as well, and your process for the validation set would be something like:




    1. Prepare your recommender model

    2. Feed your data through the model, without looking at actual outcomes

    3. Match your model output to actual outcomes to see whether or not
      people followed the recommendation (whether they ever saw that
      recommendation or not)

    4. Compare the results of people that ended up going with each
      recommended approach (A vs. B), as well as those who "followed" the
      recommendations or not (Recommended-A-did-A vs. Recommended-A-did-B,
      etc.)


    Retrospective studies are generally not as good as well-designed, well-executed prospective experimental studies, but they can still provide a lot of information. In situations where prospective experimentation is impossible or undesirable, the information a retrospective study provides may be the very best you can actually get.






    share|improve this answer









    $endgroup$


















      0












      $begingroup$

      You should still be able to use a validation set to evaluate the model, whether or not you pursue an experimental approach. (Specific features of your model and investigations may tweak these, but this is based on what's already been posted alone).



      There is nothing wrong with A/B group assignment and testing in a medical context, with a few caveats (this list is not exhaustive):




      • The relevant clinical/medical knowledge must be in a state of
        equipoise (it's not already clear that one approach is better than
        another, or which is better is genuinely not known).

      • Individuals should be aware that they are participating in a study, and that they are being routed to
        group A or B, and have the option to decline their assignment (or,
        conversely, they have been made aware of the experimental assignment
        and have consented to participate in advance).

      • An institutional review board should evaluate your proposed
        experiment and signed off on it. This, of course, presupposes that
        you have access to such a board composed of members able to make
        those assessments.


      Those can be a tall order, but you don't necessarily have to perform a prospective, double-blind experimental study in order to glean some information. A retrospective study could provide some insight as well, and your process for the validation set would be something like:




      1. Prepare your recommender model

      2. Feed your data through the model, without looking at actual outcomes

      3. Match your model output to actual outcomes to see whether or not
        people followed the recommendation (whether they ever saw that
        recommendation or not)

      4. Compare the results of people that ended up going with each
        recommended approach (A vs. B), as well as those who "followed" the
        recommendations or not (Recommended-A-did-A vs. Recommended-A-did-B,
        etc.)


      Retrospective studies are generally not as good as well-designed, well-executed prospective experimental studies, but they can still provide a lot of information. In situations where prospective experimentation is impossible or undesirable, the information a retrospective study provides may be the very best you can actually get.






      share|improve this answer









      $endgroup$
















        0












        0








        0





        $begingroup$

        You should still be able to use a validation set to evaluate the model, whether or not you pursue an experimental approach. (Specific features of your model and investigations may tweak these, but this is based on what's already been posted alone).



        There is nothing wrong with A/B group assignment and testing in a medical context, with a few caveats (this list is not exhaustive):




        • The relevant clinical/medical knowledge must be in a state of
          equipoise (it's not already clear that one approach is better than
          another, or which is better is genuinely not known).

        • Individuals should be aware that they are participating in a study, and that they are being routed to
          group A or B, and have the option to decline their assignment (or,
          conversely, they have been made aware of the experimental assignment
          and have consented to participate in advance).

        • An institutional review board should evaluate your proposed
          experiment and signed off on it. This, of course, presupposes that
          you have access to such a board composed of members able to make
          those assessments.


        Those can be a tall order, but you don't necessarily have to perform a prospective, double-blind experimental study in order to glean some information. A retrospective study could provide some insight as well, and your process for the validation set would be something like:




        1. Prepare your recommender model

        2. Feed your data through the model, without looking at actual outcomes

        3. Match your model output to actual outcomes to see whether or not
          people followed the recommendation (whether they ever saw that
          recommendation or not)

        4. Compare the results of people that ended up going with each
          recommended approach (A vs. B), as well as those who "followed" the
          recommendations or not (Recommended-A-did-A vs. Recommended-A-did-B,
          etc.)


        Retrospective studies are generally not as good as well-designed, well-executed prospective experimental studies, but they can still provide a lot of information. In situations where prospective experimentation is impossible or undesirable, the information a retrospective study provides may be the very best you can actually get.






        share|improve this answer









        $endgroup$



        You should still be able to use a validation set to evaluate the model, whether or not you pursue an experimental approach. (Specific features of your model and investigations may tweak these, but this is based on what's already been posted alone).



        There is nothing wrong with A/B group assignment and testing in a medical context, with a few caveats (this list is not exhaustive):




        • The relevant clinical/medical knowledge must be in a state of
          equipoise (it's not already clear that one approach is better than
          another, or which is better is genuinely not known).

        • Individuals should be aware that they are participating in a study, and that they are being routed to
          group A or B, and have the option to decline their assignment (or,
          conversely, they have been made aware of the experimental assignment
          and have consented to participate in advance).

        • An institutional review board should evaluate your proposed
          experiment and signed off on it. This, of course, presupposes that
          you have access to such a board composed of members able to make
          those assessments.


        Those can be a tall order, but you don't necessarily have to perform a prospective, double-blind experimental study in order to glean some information. A retrospective study could provide some insight as well, and your process for the validation set would be something like:




        1. Prepare your recommender model

        2. Feed your data through the model, without looking at actual outcomes

        3. Match your model output to actual outcomes to see whether or not
          people followed the recommendation (whether they ever saw that
          recommendation or not)

        4. Compare the results of people that ended up going with each
          recommended approach (A vs. B), as well as those who "followed" the
          recommendations or not (Recommended-A-did-A vs. Recommended-A-did-B,
          etc.)


        Retrospective studies are generally not as good as well-designed, well-executed prospective experimental studies, but they can still provide a lot of information. In situations where prospective experimentation is impossible or undesirable, the information a retrospective study provides may be the very best you can actually get.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 5 hours ago









        Upper_CaseUpper_Case

        1563




        1563






























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