Browsing by Author "Chen, Zhuo"
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Item Automating Disease Management Using Answer Set Programming(Dagstuhl Publishing, 2018-09-24) Chen, Zhuo; Chen, ZhuoManagement of chronic diseases such as heart failure, diabetes, and chronic obstructive pulmonary disease (COPD) is a major problem in health care. A standard approach that the medical community has devised to manage widely prevalent chronic diseases such as chronic heart failure (CHF) is to have a committee of experts develop guidelines that all physicians should follow. These guidelines typically consist of a series of complex rules that make recommendations based on a patient's information. Due to their complexity, often the guidelines are either ignored or not complied with at all, which can result in poor medical practices. It is not even clear whether it is humanly possible to follow these guidelines due to their length and complexity. In the case of CHF management, the guidelines run nearly 80 pages. In this paper we describe a physician-advisory system for CHF management that codes the entire set of clinical practice guidelines for CHF using answer set programming. Our approach is based on developing reasoning templates (that we call knowledge patterns) and using these patterns to systemically code the clinical guidelines for CHF as ASP rules. Use of the knowledge patterns greatly facilitates the development of our system. Given a patient's medical information, our system generates a recommendation for treatment just as a human physician would, using the guidelines. Our system will work even in the presence of incomplete information. Our work makes two contributions: (i) it shows that highly complex guidelines can be successfully coded as ASP rules, and (ii) it develops a series of knowledge patterns that facilitate the coding of knowledge expressed in a natural language and that can be used for other application domains. © Zhuo Chen.Item Automating Disease Management Using Answer Set Programming: Heart Failure(2017-12) Chen, Zhuo; Gupta, Gopal; Tamil, Lakshman S.Management of chronic diseases such as heart failure (HF), diabetes and chronic obstructive pulmonary disease is a major health care problem. A standard approach that the medical community has devised to manage widely prevalent chronic diseases such as heart failure is to have a committee of experts develop guidelines that all physicians should follow. These guidelines typically consist of a series of complex rules that make recommendations based on a patient's information. Due to their complexity, often the guidelines are either ignored or not complied with at all, which can result in poor medical practices. It is not even clear whether it is humanly possible to follow these clinical guidelines due to their length and complexity. In the case of heart failure management, the guidelines run nearly 80 pages. In this dissertation we describe a physician advisory system that codes the entire set of clinical practice guidelines for heart failure management using answer set programming (ASP). ASP is a form of declarative programming geared toward solving NP-hard search problems. Our approach is based on developing reasoning templates that we call knowledge patterns and using these patterns to systematically code the clinical guideline for HF management as ASP rules. Use of the knowledge patterns greatly facilitates the development of the physician advisory system. Given a patient's medical information, the system generates a set of guideline-compliant recommendations just as a human physician would. The system works even in the presence of incomplete information. Abductive reasoning is implemented in the system to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. The physician advisory system is validated by using data of representative patients with heart failure.Item Developing a Drug Delivery and Imaging System on a Virus-Like Particle Platform(2018-08) Chen, Zhuo; Gassensmith, Jeremiah J.Nanoparticle based therapeutics have been proved to improve therapeutic efficacy and reduce the off-target toxicity. However, poor monodispersity and long term bioaccumulation toxicity have been the obstacles for the biomedical applications. Viruses-like particles (VLPs) have emerged as promising natural nanoparticles, which are monodisperse, non-infectious and biodegradable. Each VLP is usually composed of hundreds of identical subunits, leading to a highly ordered quaternary structure and repetitive particle surface. These unique characteristics allow VLP to be chemically functionalized precisely and periodically. The proteinaceous viral capsids are a robust platform, and solvent exposed amino acids such as lysine, cysteine and tyrosine can be orthogonally modified with variety of bioconjugation techniques. Bacteriophage Qβ is one of the well-studied VLPs, which is 28 nm in diameter and composed of 180 identical coat proteins. In my study, Qβ was used as a robust platform for conjugation-induced fluorescent labelling for the application of in vitro cell tracking and developing a photocaged carrier for stimuli-responsive drug release.