Hrdlicka Jan, Klema Jiri
Department of Cybernetics, Czech Technical University in Prague, The Czech Republic.
Stud Health Technol Inform. 2011;169:574-8.
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.
本文提出了一种用于精神分裂症复发预测的adaBoost方法。adaBoost的数据是从患者对通过手机短信定期发送的早期预警信号问卷的回答中提取的。adaBoost算法的性能与当前灵敏度为0.65、特异性为0.73的ITAREPS系统进行了对比。adaBoost具有相同的灵敏度0.65，但特异性更高，为0.84，因此准备成为ITAREPS护理计划的一部分。
