Tell me what i don't know - Making the most of social health forums

Jerry Rolia, Wen Yao, Sujoy Basu, Wei Nchih Lee, Sharad Singhal, Akhil Kumar, Sharat R. Sabbella

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We describe a novel approach to helping patients learn about their condition using social health forums. We infer a patient's condition based on her clinical data, and assign her to a clinical state pertaining to that condition. Our system presents the patient with forum posts that are most relevant to her current state. By adjusting a Like Me dial, the patient can modify the results from those that are most popular with patients in the same condition to the most general that apply to patients that map to other states for the condition. The posts are ranked in order of importance to the patient condition and the dial settings and the Top-N results are shown to the patient. The initial rankings of posts for different states are obtained by weights assigned by a medical expert. The patients can also give feedback with a LIKE or a DISLIKE button on whether the post is useful to them. This is incorporated into our method to dynamically change the initial weights. As the system is used in practice the initial rankings are subsumed by the feedback provided by users of the system. Our approach is general but we give results in the context of a diabetes social health forum. The algorithms are given along with preliminary results and an illustration of the user interface. We view this effort as a component of a larger patient navigation and engagement system that can help patients gain a better understanding of their condition.

Original languageEnglish (US)
JournalHP Laboratories Technical Report
Issue number43
StatePublished - Jul 15 2013

Fingerprint

Health
Feedback
Medical problems
User interfaces
Navigation

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Rolia, J., Yao, W., Basu, S., Lee, W. N., Singhal, S., Kumar, A., & Sabbella, S. R. (2013). Tell me what i don't know - Making the most of social health forums. HP Laboratories Technical Report, (43).
Rolia, Jerry ; Yao, Wen ; Basu, Sujoy ; Lee, Wei Nchih ; Singhal, Sharad ; Kumar, Akhil ; Sabbella, Sharat R. / Tell me what i don't know - Making the most of social health forums. In: HP Laboratories Technical Report. 2013 ; No. 43.
@article{eadfc01bf2c9485f993489b32afedeff,
title = "Tell me what i don't know - Making the most of social health forums",
abstract = "We describe a novel approach to helping patients learn about their condition using social health forums. We infer a patient's condition based on her clinical data, and assign her to a clinical state pertaining to that condition. Our system presents the patient with forum posts that are most relevant to her current state. By adjusting a Like Me dial, the patient can modify the results from those that are most popular with patients in the same condition to the most general that apply to patients that map to other states for the condition. The posts are ranked in order of importance to the patient condition and the dial settings and the Top-N results are shown to the patient. The initial rankings of posts for different states are obtained by weights assigned by a medical expert. The patients can also give feedback with a LIKE or a DISLIKE button on whether the post is useful to them. This is incorporated into our method to dynamically change the initial weights. As the system is used in practice the initial rankings are subsumed by the feedback provided by users of the system. Our approach is general but we give results in the context of a diabetes social health forum. The algorithms are given along with preliminary results and an illustration of the user interface. We view this effort as a component of a larger patient navigation and engagement system that can help patients gain a better understanding of their condition.",
author = "Jerry Rolia and Wen Yao and Sujoy Basu and Lee, {Wei Nchih} and Sharad Singhal and Akhil Kumar and Sabbella, {Sharat R.}",
year = "2013",
month = "7",
day = "15",
language = "English (US)",
journal = "HP Laboratories Technical Report",
number = "43",

}

Rolia, J, Yao, W, Basu, S, Lee, WN, Singhal, S, Kumar, A & Sabbella, SR 2013, 'Tell me what i don't know - Making the most of social health forums', HP Laboratories Technical Report, no. 43.

Tell me what i don't know - Making the most of social health forums. / Rolia, Jerry; Yao, Wen; Basu, Sujoy; Lee, Wei Nchih; Singhal, Sharad; Kumar, Akhil; Sabbella, Sharat R.

In: HP Laboratories Technical Report, No. 43, 15.07.2013.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Tell me what i don't know - Making the most of social health forums

AU - Rolia, Jerry

AU - Yao, Wen

AU - Basu, Sujoy

AU - Lee, Wei Nchih

AU - Singhal, Sharad

AU - Kumar, Akhil

AU - Sabbella, Sharat R.

PY - 2013/7/15

Y1 - 2013/7/15

N2 - We describe a novel approach to helping patients learn about their condition using social health forums. We infer a patient's condition based on her clinical data, and assign her to a clinical state pertaining to that condition. Our system presents the patient with forum posts that are most relevant to her current state. By adjusting a Like Me dial, the patient can modify the results from those that are most popular with patients in the same condition to the most general that apply to patients that map to other states for the condition. The posts are ranked in order of importance to the patient condition and the dial settings and the Top-N results are shown to the patient. The initial rankings of posts for different states are obtained by weights assigned by a medical expert. The patients can also give feedback with a LIKE or a DISLIKE button on whether the post is useful to them. This is incorporated into our method to dynamically change the initial weights. As the system is used in practice the initial rankings are subsumed by the feedback provided by users of the system. Our approach is general but we give results in the context of a diabetes social health forum. The algorithms are given along with preliminary results and an illustration of the user interface. We view this effort as a component of a larger patient navigation and engagement system that can help patients gain a better understanding of their condition.

AB - We describe a novel approach to helping patients learn about their condition using social health forums. We infer a patient's condition based on her clinical data, and assign her to a clinical state pertaining to that condition. Our system presents the patient with forum posts that are most relevant to her current state. By adjusting a Like Me dial, the patient can modify the results from those that are most popular with patients in the same condition to the most general that apply to patients that map to other states for the condition. The posts are ranked in order of importance to the patient condition and the dial settings and the Top-N results are shown to the patient. The initial rankings of posts for different states are obtained by weights assigned by a medical expert. The patients can also give feedback with a LIKE or a DISLIKE button on whether the post is useful to them. This is incorporated into our method to dynamically change the initial weights. As the system is used in practice the initial rankings are subsumed by the feedback provided by users of the system. Our approach is general but we give results in the context of a diabetes social health forum. The algorithms are given along with preliminary results and an illustration of the user interface. We view this effort as a component of a larger patient navigation and engagement system that can help patients gain a better understanding of their condition.

UR - http://www.scopus.com/inward/record.url?scp=84879933777&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879933777&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84879933777

JO - HP Laboratories Technical Report

JF - HP Laboratories Technical Report

IS - 43

ER -