Using Machine Learning for Effective Personalized Treatments

医学领域越来越关注“循证个性化医疗”：根据史密斯女士的所有属性，使用基于标记数据集中早期患者的模型，确定最适合她的特定治疗。在过去的15年里，我的大部分研究都直接或间接地集中在这一挑战上。许多项目直接将此任务视为“机器学习”任务，即从相关的标记数据集中学习适当的分类器。虽然标准的机器学习系统足以为其中一些任务学习有用的模型，但其他许多任务需要创新的扩展-例如，以科普本质上非常高维或连续的数据等。使用质谱或NMR光谱法鉴定和定量生物样品中的分子化合物（“代谢物”）的技术。还有一些人探索了基础性的主题--与预测个体治疗效果评分或生存预测有关。除了许多出版物，这些活动也导致了一些部署的医疗应用程序（例如，用于筛查腺瘤的商业工具），以及几个网络应用程序-用于帮助临床医生确定哪些肝脏患者应添加到新肝脏的等待名单中，用于生成有效的生存预测模型（PSSP），以及用于NMR谱（Bayesil）和ESI-MS/MS和EI MS（CFM-ID）的分析。**在接下来的5年里，我计划通过开发有效的模型和生产它们所需的工具，继续在这个令人兴奋的领域工作，以实现基于证据的个性化医疗。和以前一样，我预计一些任务将涉及应用标准技术;这些结果仍然在医学上有用，并将有助于进一步说服医学界，机器学习可以有效地产生有效的个性化治疗方法。然而，我实验室的大部分研究工作将集中在其他计算上有趣的问题上，所有这些都是为了产生有效的预测模型：**（1）改进预测生存时间的方法，从具有审查实例的数据集 **（2）学习预后模型（与简单的诊断模型相比）;这将需要更好地理解反事实推理和其他处理涉及干预的（有偏见的）训练数据的方法。(3)解决多站点数据的问题，包括批次效应和协变量偏移 *（4）使用各种降维技术处理高维数据，包括利用数据结构的工具，如主题模型 ** 这些主题中的每一个都需要理论分析和实证实验。此外，虽然我们的主要应用领域是医学，但每一个领域都代表着一个核心的机器学习挑战，这意味着我们的研究结果将适用于各个领域。

The field of medicine is increasingly focusing on "evidence-based personalized medicine": identifying the specific treatment that is best for Ms Smith, based on all of her attributes, using a model that is based on earlier patients in a labeled dataset. Over the last 15 years, most of my research has focused on this challenge -- either directly or indirectly. Many projects directly viewed this task as a "machine learning" task of learning an appropriate classifier from a relevant labeled datasets. While standard machine learning systems were sufficient to learn useful models for some of these tasks, many others require innovative extensions -- eg, to cope with data that was extremely high dimensional or sequential in nature, etc. Some other projects produced tools that enable these tasks, by providing relevant features -- eg, techniques that identified and quantified the molecular compounds (“metabolites”) within a biological sample, using mass spec or NMR spectroscopy. Yet others explored foundational topics -- related to predicting individual treatment effect scores, or survival prediction. In addition to many publications, these activities have also led to some deployed medical applications (eg, a commercial tool for screening for adenoma), and several web-apps -- for helping clinicians determine which liver-patient should be added to the waitlist for a new liver, for producing effective survival prediction models (PSSP), and for analysis of NMR spectra (Bayesil) and of ESI-MS/MS and EI MS (CFM-ID).******Over the next 5 years, I plan to continue working in this exciting area of enabling evidence-based personalized medicine by developing effective models, and the tools needed for producing them. As before, I anticipate some tasks will involve applying standard techniques; these results will still be medically useful, and will help further convince the medical community that machine learning can be effective, as a way to produce effective personalized treatments. Most of my lab's research efforts, however, will focus on other computationally interesting questions, all towards the goal of producing effective predictive models:******(1) Improving approaches for predicting survival time, from datasets with censored instances******(2) Learning prognostic models (versus simple diagnostic ones); this will require a better understanding of counterfactual reasoning and other approaches of coping with (biased) training data that involves interventions.******(3) Addressing issues from multi-site data, including batch effect and covariate shift******(4) Coping with high-dimensional data using various dimensionality reduction techniques, including tools that exploit the structure of the data, such as topic models******Each of these topics will require both theoretical analysis as well as empirical experiments. Moreover, while our main application domain is medicine, each of these represents a core machine learning challenge, meaning our results will apply across domains.