Combining Markov Models and Association Analysis for Disease Prediction

机译：结合马尔可夫模型和关联分析进行疾病预测

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An approach for disease prediction that combines clustering, Markov models and association analysis techniques is proposed. Patient medical records are clustered and a Markov model for each cluster is generated to perform prediction of illnesses a patient could likely be affected in the future. However, when the probability of the most likely state in the Markov models is not sufficiently high, it resorts to sequential association analysis, by considering the items induced by high confidence rules generated by recurring sequential disease patterns. Experimental results show that the combination of different models enhances predictive accuracy and is a feasible way to diagnose diseases.

机译：提出了一种结合聚类，马尔可夫模型和关联分析技术的疾病预测方法。对患者病历进行聚类，并为每个聚类生成马尔可夫模型，以预测患者将来可能会受到影响的疾病。但是，当马尔可夫模型中最可能出现状态的概率不够高时，它会通过考虑由反复出现的连续疾病模式产生的高置信度规则所诱发的项目，而采用连续关联分析。实验结果表明，不同模型的组合可以提高预测准确性，是诊断疾病的可行方法。

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《Information technology in bio- and medical informatics》|2011年|p.39-52|共14页
会议地点 Toulouse(FR);Toulouse(FR)
作者
Francesco Folino; Clara Pizzuti;
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作者单位

Institute for High Performance Computing and Networking (ICAR) National Research Council of Italy (CNR) Via Pietro Bucci, 41C 87036 Rende (CS), Italy;

Institute for High Performance Computing and Networking (ICAR) National Research Council of Italy (CNR) Via Pietro Bucci, 41C 87036 Rende (CS), Italy;

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正文语种 eng
中图分类生物医学工程;
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机译：我们开发了一种科学创造力模型，并在稀有疾病领域进行测试。我们的模型基于对Rett综合征的深入案例研究的结果。档案分析，文献计量技术和专家调查与网络分析相结合，以确定最具创造力的科学家。首先，比较生成和组合创造力的替代措施。然后，我们推广了我们的结果，并提出了社会语义网络演化的随机模型。模型预测用多个罕见疾病专业网络进行测试。我们发现，一个领域的专家之间的新的科学合作增强了组合创造力。相反，新手的高入门率与生成创造力负相关。通过扩展这组有用的概念，创造性的科学家获得了中心地位。同时，通过增加科学界的中心地位，科学家们可以复制和推广他们的结果，从而促进科学范式的发展。

Combining Markov Models and Association Analysis for Disease Prediction

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