Probabilistic scoring of validated insights for personal health services

Aki Harma; Rim Helaoui

doi:10.1109/SSCI.2016.7849892

Probabilistic scoring of validated insights for personal health services

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

In connected health services automatic discovery of recurring patterns and correlations, or insights, provides many interesting opportunities for the personalization of the services. In this paper the focus is on insight mining for a health coaching service. The basic idea in the proposed method is to generate a large number of insight candidates which have been pre-validated with domain experts and to score them using the data. The dynamic performance of the scoring is studied with a collection of lifestyle sensor data from volunteers. The proposed method is compared to a conventional data mining approach based on the Apriori algorithm. We demonstrate that the proposed method gives significantly more variability among the subjects and types of insights it finds which may reflect better the underlying statistics of individual lifestyle patterns of the different subjects.

Original language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Publisher	IEEE
ISBN (Electronic)	9781509042401
DOIs	https://doi.org/10.1109/SSCI.2016.7849892
Publication status	Published - 9 Feb 2017
Externally published	Yes
Event	2016 IEEE Symposium Series on Computational Intelligence - Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016 http://ssci2016.cs.surrey.ac.uk/

Conference

Conference	2016 IEEE Symposium Series on Computational Intelligence
Abbreviated title	SSCI 2016
Country/Territory	Greece
City	Athens
Period	6/12/16 → 9/12/16
Internet address	http://ssci2016.cs.surrey.ac.uk/

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10.1109/SSCI.2016.7849892

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title = "Probabilistic scoring of validated insights for personal health services",

abstract = "In connected health services automatic discovery of recurring patterns and correlations, or insights, provides many interesting opportunities for the personalization of the services. In this paper the focus is on insight mining for a health coaching service. The basic idea in the proposed method is to generate a large number of insight candidates which have been pre-validated with domain experts and to score them using the data. The dynamic performance of the scoring is studied with a collection of lifestyle sensor data from volunteers. The proposed method is compared to a conventional data mining approach based on the Apriori algorithm. We demonstrate that the proposed method gives significantly more variability among the subjects and types of insights it finds which may reflect better the underlying statistics of individual lifestyle patterns of the different subjects.",

author = "Aki Harma and Rim Helaoui",

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year = "2017",

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doi = "10.1109/SSCI.2016.7849892",

language = "English",

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AU - Helaoui, Rim

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Y1 - 2017/2/9

N2 - In connected health services automatic discovery of recurring patterns and correlations, or insights, provides many interesting opportunities for the personalization of the services. In this paper the focus is on insight mining for a health coaching service. The basic idea in the proposed method is to generate a large number of insight candidates which have been pre-validated with domain experts and to score them using the data. The dynamic performance of the scoring is studied with a collection of lifestyle sensor data from volunteers. The proposed method is compared to a conventional data mining approach based on the Apriori algorithm. We demonstrate that the proposed method gives significantly more variability among the subjects and types of insights it finds which may reflect better the underlying statistics of individual lifestyle patterns of the different subjects.

AB - In connected health services automatic discovery of recurring patterns and correlations, or insights, provides many interesting opportunities for the personalization of the services. In this paper the focus is on insight mining for a health coaching service. The basic idea in the proposed method is to generate a large number of insight candidates which have been pre-validated with domain experts and to score them using the data. The dynamic performance of the scoring is studied with a collection of lifestyle sensor data from volunteers. The proposed method is compared to a conventional data mining approach based on the Apriori algorithm. We demonstrate that the proposed method gives significantly more variability among the subjects and types of insights it finds which may reflect better the underlying statistics of individual lifestyle patterns of the different subjects.

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DO - 10.1109/SSCI.2016.7849892

M3 - Conference article in proceeding

AN - SCOPUS:85016072556

BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

PB - IEEE

T2 - 2016 IEEE Symposium Series on Computational Intelligence

Y2 - 6 December 2016 through 9 December 2016

ER -

Probabilistic scoring of validated insights for personal health services

Abstract

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