Risk mining in healthcare: building a knowledge base of health risk patterns

Mika Timonen, Paula Silvonen, Lauri Seitsonen

Research output: Contribution to conferenceConference articleScientificpeer-review

Abstract

Medical information is spread into countless different data sources such as websites and databases. Some of the sources contain detailed information about a specific disease while others have general information about wellbeing and illnesses. The common factor in the sources is that they contain useful information about health related risks. By integrating the information of several data sources we find indicators and symptoms that can be used in early prediction, disease prevention and health assessment. In this paper we propose a risk mining framework for extracting and modelling health related risk patterns. We use risk mining to populate a knowledge base of health risk patterns to be used in health assessment and early prediction of health risks. Unlike in previous work done in the area of risk mining, we concentrate on risk patterns that include impact information, and not just attributes. This is crucial, as both have an important role when diagnosing patients. The proposed risk mining framework consist of four steps: (1) Risk identification, (2) Risk assessment, (3) Risk factor extraction, and (4) Risk modelling.
Original languageEnglish
Publication statusPublished - 2010
MoE publication typeNot Eligible
EventWorkshop on Data Mining for Healthcare Management held in conjunction with the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, DMHM-PAKDD'10 - Hyderabad, India
Duration: 21 Jun 201024 Jun 2010

Workshop

WorkshopWorkshop on Data Mining for Healthcare Management held in conjunction with the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, DMHM-PAKDD'10
Abbreviated titleDMHM-PAKDD'10
CountryIndia
CityHyderabad
Period21/06/1024/06/10

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Keywords

  • risk mining
  • pattern mining
  • mining imbalanced data
  • anomaly detection
  • health knowledge bases

Cite this

Timonen, M., Silvonen, P., & Seitsonen, L. (2010). Risk mining in healthcare: building a knowledge base of health risk patterns. Paper presented at Workshop on Data Mining for Healthcare Management held in conjunction with the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, DMHM-PAKDD'10, Hyderabad, India.
Timonen, Mika ; Silvonen, Paula ; Seitsonen, Lauri. / Risk mining in healthcare : building a knowledge base of health risk patterns. Paper presented at Workshop on Data Mining for Healthcare Management held in conjunction with the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, DMHM-PAKDD'10, Hyderabad, India.
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abstract = "Medical information is spread into countless different data sources such as websites and databases. Some of the sources contain detailed information about a specific disease while others have general information about wellbeing and illnesses. The common factor in the sources is that they contain useful information about health related risks. By integrating the information of several data sources we find indicators and symptoms that can be used in early prediction, disease prevention and health assessment. In this paper we propose a risk mining framework for extracting and modelling health related risk patterns. We use risk mining to populate a knowledge base of health risk patterns to be used in health assessment and early prediction of health risks. Unlike in previous work done in the area of risk mining, we concentrate on risk patterns that include impact information, and not just attributes. This is crucial, as both have an important role when diagnosing patients. The proposed risk mining framework consist of four steps: (1) Risk identification, (2) Risk assessment, (3) Risk factor extraction, and (4) Risk modelling.",
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Timonen, M, Silvonen, P & Seitsonen, L 2010, 'Risk mining in healthcare: building a knowledge base of health risk patterns' Paper presented at Workshop on Data Mining for Healthcare Management held in conjunction with the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, DMHM-PAKDD'10, Hyderabad, India, 21/06/10 - 24/06/10, .

Risk mining in healthcare : building a knowledge base of health risk patterns. / Timonen, Mika; Silvonen, Paula; Seitsonen, Lauri.

2010. Paper presented at Workshop on Data Mining for Healthcare Management held in conjunction with the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, DMHM-PAKDD'10, Hyderabad, India.

Research output: Contribution to conferenceConference articleScientificpeer-review

TY - CONF

T1 - Risk mining in healthcare

T2 - building a knowledge base of health risk patterns

AU - Timonen, Mika

AU - Silvonen, Paula

AU - Seitsonen, Lauri

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PY - 2010

Y1 - 2010

N2 - Medical information is spread into countless different data sources such as websites and databases. Some of the sources contain detailed information about a specific disease while others have general information about wellbeing and illnesses. The common factor in the sources is that they contain useful information about health related risks. By integrating the information of several data sources we find indicators and symptoms that can be used in early prediction, disease prevention and health assessment. In this paper we propose a risk mining framework for extracting and modelling health related risk patterns. We use risk mining to populate a knowledge base of health risk patterns to be used in health assessment and early prediction of health risks. Unlike in previous work done in the area of risk mining, we concentrate on risk patterns that include impact information, and not just attributes. This is crucial, as both have an important role when diagnosing patients. The proposed risk mining framework consist of four steps: (1) Risk identification, (2) Risk assessment, (3) Risk factor extraction, and (4) Risk modelling.

AB - Medical information is spread into countless different data sources such as websites and databases. Some of the sources contain detailed information about a specific disease while others have general information about wellbeing and illnesses. The common factor in the sources is that they contain useful information about health related risks. By integrating the information of several data sources we find indicators and symptoms that can be used in early prediction, disease prevention and health assessment. In this paper we propose a risk mining framework for extracting and modelling health related risk patterns. We use risk mining to populate a knowledge base of health risk patterns to be used in health assessment and early prediction of health risks. Unlike in previous work done in the area of risk mining, we concentrate on risk patterns that include impact information, and not just attributes. This is crucial, as both have an important role when diagnosing patients. The proposed risk mining framework consist of four steps: (1) Risk identification, (2) Risk assessment, (3) Risk factor extraction, and (4) Risk modelling.

KW - risk mining

KW - pattern mining

KW - mining imbalanced data

KW - anomaly detection

KW - health knowledge bases

M3 - Conference article

ER -

Timonen M, Silvonen P, Seitsonen L. Risk mining in healthcare: building a knowledge base of health risk patterns. 2010. Paper presented at Workshop on Data Mining for Healthcare Management held in conjunction with the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, DMHM-PAKDD'10, Hyderabad, India.