A novel and dynamic prediction engine for practicing precision medicine to prevent chemotherapy-induced nausea and vomiting

Cancer remains the second major cause of death in the United States over the last decade. Chemotherapy is a core component of nearly every cancer treatment plan. Chemotherapy-Induced Nausea and Vomiting (CINV) are the two most dreadful and unpleasant side-effects of chemotherapy for cancer patients. The consequences of CINV include: (1) impaired life quality, (2) poor social life, (3) burden on economy due to loss of workdays, (4) increased healthcare cost, and (5) denial of chemotherapy due to unendurable CINV. There are three clinical guidelines (ASCO, NCCN, and MASCC/ESMO) for the management of CINV. Several patient-specific factors affect the risk of CINV. However, none of the guidelines consider those factors. Not all of patients have the similar emetic risk of CINV. Despite the improvements in CINV management, as many as two-thirds of chemotherapy patients still experience some degree of CINV. As a result, physicians use their personal experiences for CINV treatment, which leads to inconsistent managements of CINV. The overall objective of this study is to improve the prevention of CINV using precise, personalized and evidence-based antiemetic treatment before chemotherapy. Physicians receive feedback about CINV risks of patients from a CINV decision support system based on patient-specific factors. This objective was achieved by accomplishing clinical innovations through the discovery of combined relationships of various patient-specific factors for causing CINV, and informatics innovations through the development of a novel, precise and dynamic prediction engine for practicing precision and personalized medicine in CINV prevention. The approach presented in this dissertation can be applied to any other clinical predictions

A Study on Pubmed Search Tag Usage Pattern: Association Rule Mining of a Full-day Pubmed Query Log

Abstract Background The practice of evidence-based medicine requires efficient biomedical literature search such as PubMed/MEDLINE. Retrieval performance relies highly on the efficient use of search field tags. The purpose of this study was to analyze PubMed log data in order to understand the usage pattern of search tags by the end user in PubMed/MEDLINE search. Methods A PubMed query log file was obtained from the National Library of Medicine containing anonymous user identification, timestamp, and query text. Inconsistent records were removed from the dataset and the search tags were extracted from the query texts. A total of 2,917,159 queries were selected for this study issued by a total of 613,061 users. The analysis of frequent co-occurrences and usage patterns of the search tags was conducted using an association mining algorithm. Results The percentage of search tag usage was low (11.38% of the total queries) and only 2.95% of queries contained two or more tags. Three out of four users used no search tag and about two-third of them issued less than four queries. Among the queries containing at least one tagged search term, the average number of search tags was almost half of the number of total search terms. Navigational search tags are more frequently used than informational search tags. While no strong association was observed between informational and navigational tags, six (out of 19) informational tags and six (out of 29) navigational tags showed strong associations in PubMed searches. Conclusions The low percentage of search tag usage implies that PubMed/MEDLINE users do not utilize the features of PubMed/MEDLINE widely or they are not aware of such features or solely depend on the high recall focused query translation by the PubMed’s Automatic Term Mapping. The users need further education and interactive search application for effective use of the search tags in order to fulfill their biomedical information needs from PubMed/MEDLINE.</p

Evaluating the benefits of Octree-based indexing for LiDAR data

In recent years the geospatial domain has seen a significant increase in the availability of very large three dimensional (3D) point datasets. These datasets originate from a variety of sources, such as for example Light Detection and Ranging (LiDAR) or meteorological weather recordings. Increasingly, a desire within the geospatial community has been expressed to exploit these types of 3D point data in a meaningful engineering context that goes beyond mere visualization. However, current Spatial Information Systems (SISs) provide only limited support for vast 3D point datasets. Even those systems that advertise their support for in-built 3D data types provide very limited functionality to manipulate such data types. In particular, an effective means of indexing large 3D point datasets is yet missing, however it is crucial for effective analysis. Next to the large size of 3D point datasets they may also be information rich, for example they may contain color information or some other associated semantic. This paper presents an alternative spatial indexing technique, which is based on an octree data structure. We show that it outperforms R-tree index, while being able to group 3D points based on their attribute values at the same time. This paper presents an evaluation employing this octree spatial indexing technique and successfully highlights its advantages for sparse as well as uniformly distributed data on the basis of an extensive LiDAR dataset.Deposited by bulk impor

Evaluating the Benefits of Octree-based Indexing for LiDAR Data

Very large true three-dimensional (3D) point datasets, as opposed to the previously common 2.5D data sets, are progressively more common nowadays, such as from Light Detection and Ranging (LiDAR). Increasingly, attempts are made to exploit these 3D point data sets beyond mere visualization. However, current Spatial Information Systems provide only limited 3D support. Even commercial systems advertising in-built, 3D data types provide only minimal functionality. Particularly, there is no effective means of indexing large 3D point datasets, which is crucial for efficient analysis and engineering usage. Also many datasets are information rich (e.g. contain color or some other associated semantic information), which has yet to be fully exploited. This paper presents the implementation in a commercial spatial database of a spatial indexing technique using an octree data structure and highlights its advantages for sparse, as well as uniformly distributed, aerial LiDAR data. The implementation outperforms an existing r-tree index within the software, and offers additional functionality of attribute-based 3D grouping

A Smartphone-Based Decision Support Tool for Predicting Patients at Risk of Chemotherapy-Induced Nausea and Vomiting: Retrospective Study on App Development Using Decision Tree Induction

BackgroundChemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. ObjectiveThis study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. MethodsData were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. ResultsThe decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. ConclusionsThis study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs

Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS

Transportation Research Board 89th Annual MeetingA large proportion of today's digital datasets have a spatial component. The effective storage and management of which poses particular challenges, especially with light detection and ranging (LiDAR), where datasets of even small geographic areas may contain several hundred million points. While in the last decade 2.5-dimensional data were prevalent, true 3-dimensional data are increasingly commonplace via LiDAR. They have gained particular popularity for urban applications including generation of city-scale maps, baseline data disaster management, and utility planning. Additionally, LiDAR is commonly used for flood plane identification, coastal-erosion tracking, and forest biomass mapping. Despite growing data availability, current spatial information systems do not provide suitable full support for the data's true 3D nature. Consequently, one system is needed to store the data and another for its processing, thereby necessitating format transformations. The work presented herein aims at a more cost-effective way for managing 3D LiDAR data that allows for storage and manipulation within a single system by enabling a new index within existing spatial database management technology. Implementation of an octree index for 3D LiDAR data atop Oracle Spatial 11g is presented, along with an evaluation showing up to an eight-fold improvement compared to the native Oracle R-tree index.Deposited by bulk impor