Using Machine Learning for Effective Personalized Treatments

医学界越来越关注“基于证据的个性化医疗”：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方法。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个挑战上。许多项目直接将此任务视为从相关标记数据集中学习适当分类器的“机器学习”任务。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，但许多其他任务需要创新的扩展，例如，处理极高维度或顺序的数据等。其他一些项目通过提供相关功能，例如，利用质谱或核磁共振光谱技术，识别和量化生物样本中的分子化合物（“代谢物”），从而实现这些任务。还有一些人探讨了一些基础话题——与预测个体治疗效果评分或生存预测有关。除了许多出版物之外，这些活动还导致了一些部署的医疗应用程序（例如，用于筛选腺瘤的商业工具）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该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），以及用于分析核磁共振光谱（贝叶斯）和ESI-MS/MS和EI MS （CFM-ID）。在接下来的5年里，我计划继续在这个令人兴奋的领域工作，通过开发有效的模型和生产它们所需的工具来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到应用标准技术；这些结果仍将在医学上有用，并将有助于进一步说服医学界，机器学习作为一种产生有效个性化治疗的方法是有效的。然而，我的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，所有这些问题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，从剔除实例的数据集(2)学习预测模型（相对于简单的诊断模型）；这需要更好地理解反事实推理和处理涉及干预的（有偏见的）训练数据的其他方法。(3)解决来自多站点数据的问题，包括批效应和协变量移位(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了一个核心的机器学习挑战，这意味着我们的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.