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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    title = "Risk mining in healthcare: building a knowledge base of health risk patterns",
    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.",
    keywords = "risk mining, pattern mining, mining imbalanced data, anomaly detection, health knowledge bases",
    author = "Mika Timonen and Paula Silvonen and Lauri Seitsonen",
    note = "Project code: 35012; 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, DMHM-PAKDD'10 ; Conference date: 21-06-2010 Through 24-06-2010",
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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

    N1 - Project code: 35012

    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.