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Schizophrenia prediction with the adaboost algorithm
J. Hrdlicka, J. Klema,
Language English Country Netherlands
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
21893814
Knihovny.cz E-resources
- MeSH
- Patient Compliance MeSH
- Algorithms MeSH
- Time Factors MeSH
- Behavior MeSH
- Diagnosis, Computer-Assisted methods MeSH
- Hospitalization MeSH
- Remote Consultation methods MeSH
- Humans MeSH
- Text Messaging MeSH
- Recurrence MeSH
- Program Development methods MeSH
- Schizophrenic Psychology MeSH
- Schizophrenia diagnosis prevention & control MeSH
- Software MeSH
- Decision Support Systems, Clinical MeSH
- Telemedicine methods MeSH
- Patient Readmission MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
This paper presents an adaBoost approach for schizophrenia relapse prediction. The data for the adaBoost are extracted from patients answers to Early Warning Signs questionnaires sent regularly via mobile phone messages. The performance of the adaBoost algorithm is confronted with current ITAREPS system with sensitivity 0.65 and specificity 0.73. AdaBoost has the same sensitivity 0.65 but higher specificity 0.84 and is then ready to became the part of the ITAREPS care program.
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- $a This paper presents an adaBoost approach for schizophrenia relapse prediction. The data for the adaBoost are extracted from patients answers to Early Warning Signs questionnaires sent regularly via mobile phone messages. The performance of the adaBoost algorithm is confronted with current ITAREPS system with sensitivity 0.65 and specificity 0.73. AdaBoost has the same sensitivity 0.65 but higher specificity 0.84 and is then ready to became the part of the ITAREPS care program.
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