Method To Estimate Network Availability

A distributed network makes network services available to end users at various nodes or connection points throughout the distributed network’s geographic area. A network administrator monitors the performance, capability, and availability of the distributed network to provide the network services. However, the network administrator may be limited to network traffic or other network-side parameters that may not provide an accurate or a conclusive representation of the state of the distributed network. For example, diminished or decreased network traffic could indicate a malfunction in the distributed network or be a natural consequence of a decreased number of end users. Cost, infrastructure requirements, and other limitations prevent installation and operation of a secondary network, which could be used to conclusively determine the conditions of the area within the distributed network. Instead, machine-learning algorithms may monitor and model some features of the distributed network, which may supplement service availability composite metrics, and allow the network administrator to better evaluate the condition of the distributed network without the need of the secondary network

A study of lexical behavior of sentences in chest radiology reports

Statistical natural language processors have been the focus of much research during the past decade. The UCLA Medical Imaging Informatics Group (MII) has developed a statistical NLP for the domain of radiology. We report a study of syntactic and semantic behavior of sentences in the domain of chest radiology

A Field Theoretical Approach to Medical Natural Language Processing

Abstract—A parser for medical free text reports has been developed that is based on a chemistry/physics inspired “field theory ” for word–word sentence-level dependencies. The transition from the linguistic world to the world of interacting particles with potential energies is guided by a psycholinguistics thought experiment related to the amount of “work ” required to bring a reference word into an anchored configuration of words. Calibration experiments involving four and five grams were conducted. Data from these experiments were used as a knowledge source for estimating field conditions for words in sentences sampled from a corpus of medical reports. The result of the parser is a dependency tree that represents the global minimum energy state of the system of words for a given sentence. The system was trained and tested on a corpus of radiology reports. Preliminary performance, as quantified by link recall and precision statistics, is 84.9 % and 89.9%, respectively. Index Terms—Knowledge representation, natural language processing (NLP), structured medical reporting. I

Problem-centric Organization and Visualization of Patient Imaging and Clinical Data

A patient’s electronic medical record contains a large amount of unstructured textual information. As patient records become increasingly dense owing to an aging population and increased occurrence of chronic diseases, a tool is needed to help organize and navigate patient data in a way that facilitates a clinician’s ability to understand this information and that improves efficiency. A system has been developed for physicians that summarizes clinical information from a patient record. This system provides a gestalt view of the patient’s record by organizing information about each disease along four dimensions (axes): time (eg, disease progression over time), space (eg, tumor in left frontal lobe), existence (eg, certainty of existence of a finding), and causality (eg, response to treatment). A display is generated from information provided by radiology reports and discharge summaries. Natural language processing is used to identify clinical abnormalities (problems, symptoms, findings) from these reports as well as associated properties and relationships. This information is presented in an integrated format that organizes extracted findings into a problem list, depicts the information on a timeline grid, and provides direct access to relevant reports and images. The goal of this system is to improve the structure of clinical information and its presentation to the physician, thereby simplifying the information retrieval and knowledge discovery necessary to bridge the gap between acquiring raw data and making an informed diagnosis