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。除了许多出版物外，这些活动还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）和几个网络应用程序-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，用于产生有效的生存预测模型（PSSP），以及用于分析NMR光谱（Bayesil）和ESI-MS/MS和EI MS（CFM-ID）。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，从具有删失实例的数据集（2）学习预后模型（相对于简单的诊断）;这将需要更好地理解反事实推理和处理涉及干预措施的（有偏见的）训练数据的其他方法。(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，如主题模型每个主题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.