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Individualized therapy for cystic fibrosis using artificial intelligence.

機(jī)譯：使用人工智能對(duì)囊性纖維化進(jìn)行個(gè)性化治療。

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摘要

Optimal clinical management of inherited chronic diseases, such as Cystic Fibrosis (CF), requires a dynamic approach which updates treatments to cope with the evolving course of illness and to tailor medicines and dosages for individual patients. The chronic progressive nature of CF and heterogeneity across patients lead to challenges of developing optimal regimens. An adaptive individualized therapy provides a solution and a means toward these goals. In this dissertation, we examine the problem of computing optimal adaptive individualized therapy for CF patients. A temporal difference reinforcement learning method called fitted Q-iteration is utilized to discover the optimal treatment regimen directly from clinical data. We propose multi-state discrete-time Markov process to model the disease dynamic for cystic fibrosis patients with Pseudomonas aeruginosa infection with the model parameters tuned and estimated from the published data in Wisconsin CF neonatal screening project. Our study results indicate that reinforcement learning and the clinical reinforcement trial framework can be an effective tool in discovering and developing personalized therapy which optimises the benefit-risk trade off in multi-stage decision making and improves long term outcomes in chronic diseases.
機(jī)譯：遺傳性慢性疾病（如囊性纖維化（CF））的最佳臨床管理需要一種動(dòng)態(tài)的方法，該方法需要更新治療方法以應(yīng)對(duì)不斷發(fā)展的疾病進(jìn)程，并為個(gè)別患者量身定制藥物和劑量。 CF和患者異質(zhì)性的慢性進(jìn)行性導(dǎo)致了開發(fā)最佳治療方案的挑戰(zhàn)。適應(yīng)性的個(gè)體化治療為實(shí)現(xiàn)這些目標(biāo)提供了解決方案和手段。本文探討了計(jì)算CF患者最佳適應(yīng)性個(gè)體化治療的問題。利用時(shí)域差異強(qiáng)化學(xué)習(xí)方法（稱為擬合Q迭代）直接從臨床數(shù)據(jù)中發(fā)現(xiàn)最佳治療方案。我們提出多狀態(tài)離散時(shí)間馬爾可夫過程來建模與銅綠假單胞菌感染的囊性纖維化患者的疾病動(dòng)態(tài)，其模型參數(shù)根據(jù)威斯康星州CF新生兒篩查項(xiàng)目中已發(fā)布的數(shù)據(jù)進(jìn)行調(diào)整和估算。我們的研究結(jié)果表明，強(qiáng)化學(xué)習(xí)和臨床強(qiáng)化試驗(yàn)框架可以成為發(fā)現(xiàn)和開發(fā)個(gè)性化療法的有效工具，該療法可以優(yōu)化多階段決策中的利益風(fēng)險(xiǎn)權(quán)衡并改善慢性病的長(zhǎng)期結(jié)果。

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  • 作者

    Tang, Yiyun.;

  • 作者單位

    The University of North Carolina at Chapel Hill.;

  • 授予單位 The University of North Carolina at Chapel Hill.;
  • 學(xué)科 Biology Biostatistics.;Health Sciences Medicine and Surgery.;Statistics.
  • 學(xué)位 Ph.D.
  • 年度 2010
  • 頁(yè)碼 92 p.
  • 總頁(yè)數(shù) 92
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類
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