Computational Methods for Capturing and Analyzing Personalized Genomic and Medical Data

我的研究集中在计算机科学方法的发展，以帮助解决生物学和医学问题。虽然我的研究应用于医学和基因组学的许多领域，但贯穿我所有工作的统一主线是开发新颖的计算方法，坚定地在计算机科学领域内解决这些问题。在接下来的五年里，我想把重点放在三种计算方法的发展上，这些方法可以结合起来，更准确、更快速地捕捉和分析医疗数据。在所有这些情况下，努力将主要集中在开发新的计算方法和方法上，而不是在医疗环境中部署现有技术，并且开发的许多方法将在医疗环境之外推广。******我的资助目标1将用于开发移动设备和HCI技术，以便从患者就诊中获取数据。病人和医生之间的核心互动包括医生进行观察，做笔记（在电脑或纸上），最后写（或口述）报告。该报告是与患者充分互动的主要记录，也是医生可视询问的无数体征和症状的主要记录（不是所有的症状都可以记录在笔记中，特别是如果不是异常的）。我们建议开发下一代用户界面，在临床医生身上使用可穿戴技术（摄像头和麦克风），结合移动设备（平板电脑），集成到临床工作流程中，并能够捕捉患者互动的完整视觉和音频频谱。******在该资助的目标2中，我们将致力于开发机器学习方法，以从捕获的数据（音频、视频和文本）中识别概念。我们将利用生物医学本体来帮助提高方法的准确性，训练利用文本接近性和生物医学本体空间接近性的模型来帮助训练分类器。我们将致力于将这种方法集成到现有的语音转文本工具中，以帮助改进音频处理，并将这些方法应用于患者检查的音频记录，以测试其准确性。******最后，在提案的目标3中，我们将致力于记录患者数据的数据可视化，目的是为临床医生提供一段时间内易于理解的患者症状摘要，并允许快速比较患者与其他具有类似疾病（用于鉴别诊断）和相同疾病（了解疾病变异性和预后）的患者。

My research centers on the development of Computer Science methods to help solve problems in Biology and Medicine. While my research has application to many areas of medicine and genomics, the unifying thread through all of my work is the development of novel computational methods, firmly within the domain of Computer Science, to solve these problems. In the next five years I want to focus on the development of three computational approaches that can be combined for more accurate and rapid capture and analysis of medical data. In all of these cases the effort will be primarily on developing novel computational methodology and approaches, rather than deployment of existing technologies in the medical setting, and many of the methods developed will be generalizable outside of the medical setting.******Aim 1 of my grant will be on the development of mobile devices and HCI techniques for the capture of data from a patient visit. The core interaction between a patient and their doctor involved the doctor making observations, taking notes (on computer or paper), and finally writing (or dictating) a report. This report serves as the main record of the full interaction with the patient and myriad of signs and symptoms visually interrogated by the doctor (not all of which may be recorded in the notes, especially if not abnormal). We propose to develop a next generation of user interfaces, using wearable technology on the clinician (camera and microphone), combined with mobile devices (tablets), integrated into the clinical workflow, and capable of capturing the full visual and audio spectrum of the patient interaction. ******In Aim 2 of the grant we will work to develop ML methodology to identify concepts from the captured data – audio, video, and text. We will utilize biomedical ontologies to help improve the accuracy of the methods, training models that utilize proximity in text and proximity in biomedical ontology space to help train classifiers. We will work on integrating this approach into existing speech-to-text tools to help improve audio processing, and apply these approaches to audio recording of patient exams to test their accuracy.******Finally, in Aim 3 of the proposal we will work on data visualization of recorded patient data, with the aim of providing the clinician with a easy-to-understand summary of patient symptoms over time, as well as to allow for quick comparison between a patient and others with similar disorders (for differential diagnosis) and same disorder (to understand disease variability and prognosis).