Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective

This paper presents a Lisp architecture for a portable NLP system, termed LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard, customized and in-house developed NLP tools. Our system facilitates portability across different institutions and data systems by incorporating an enriched Common Data Model (CDM) to standardize necessary data elements. It utilizes UMLS to perform domain adaptation when integrating generic domain NLP tools. It also features stand-off annotations that are specified by positional reference to the original document. We built an interval tree based search engine to efficiently query and retrieve the stand-off annotations by specifying positional requirements. We also developed a utility to convert an inline annotation format to stand-off annotations to enable the reuse of clinical text datasets with inline annotations. We experimented with our system on several NLP facilitated tasks including computational phenotyping for lymphoma patients and semantic relation extraction for clinical notes. These experiments showcased the broader applicability and utility of LAPNLP.Comment: 6 pages, accepted by IEEE BIBM 2018 as regular pape

Business intelligence and nosocomial infection decision making

Nosocomial infection prevention in healthcare units it is very important to improve patient’s well-being and safety. This prevention can be done by manipulating and analysing real data to identify critical processes and areas inside the healthcare unit, and monitoring indicators generated from data. The main goal of this paper is to evaluate the applicability of the Business Intelligence tools and concepts to healthcare and their performance as a Clinical Decision Support System, analyzing the evolution of nosocomial infection in the Centro Hospitalar do Porto, by defining a set of indicators that can help the nosocomial infection management and inducing Data Mining models to predict the occurrence of nosocomial infections (sensitivity of 91%). A Business Intelligence system composed by the presentation of a set of indicators and a Data Mining part capable of predict the occurrence of infection can provide important information to support healthcare professionals in their decisions.(undefined

Enabling instant- and interval-based semantics in multidimensional data models: the T+MultiDim Model

Time is a vital facet of every human activity. Data warehouses, which are huge repositories of historical information, must provide analysts with rich mechanisms for managing the temporal aspects of information. In this paper, we (i) propose T+MultiDim, a multidimensional conceptual data model enabling both instant- and interval-based semantics over temporal dimensions, and (ii) provide suitable OLAP (On-Line Analytical Processing) operators for querying temporal information. T+MultiDim allows one to design typical concepts of a data warehouse including temporal dimensions, and provides one with the new possibility of conceptually connecting different temporal dimensions for exploiting temporally aggregated data. The proposed approach allows one to specify and to evaluate powerful OLAP queries over information from data warehouses. In particular, we define a set of OLAP operators to deal with interval-based temporal data. Such operators allow the user to derive new measure values associated to different intervals/instants, according to different temporal semantics. Moreover, we propose and discuss through examples from the healthcare domain the SQL specification of all the temporal OLAP operators we define. (C) 2019 Elsevier Inc. All rights reserved

Some Contribution of Statistical Techniques in Big Data: A Review

Big Data is a popular topic in research work. Everyone is talking about big data, and it is believed that science, business, industry, government, society etc. will undergo a through change with the impact of big data.Big data is used to refer to very huge data set having large, more complex, hidden pattern, structured and unstructured nature of data with the difficulties to collect, storage, analysing for process or result. So proper advanced techniques to use to gain knowledge about big data. In big data research big challenge is created in storage, process, search, sharing, transfer, analysis and visualizing. To deeply discuss on introduction of big data, issue, management and all used big data techniques. Also in this paper present a review of various advanced statistical techniques to handling the key application of big data have large data set. These advanced techniques handle the structure as well as unstructured big data in different area

Large spatial datasets: Present Challenges, future opportunities

The key advantages of a well-designed multidimensional database is its ability to allow as many users as possible across an organisation to simultaneously gain access and view of the same data. Large spatial datasets evolve from scientific activities (from recent days) that tends to generate large databases which always come in a scale nearing terabyte of data size and in most cases are multidimensional. In this paper, we look at the issues pertaining to large spatial datasets; its feature (for example views), architecture, access methods and most importantly design technologies. We also looked at some ways of possibly improving the performance of some of the existing algorithms for managing large spatial datasets. The study reveals that the major challenges militating against effective management of large spatial datasets is storage utilization and computational complexity (both of which are characterised by the size of spatial big data which now tends to exceeds the capacity of commonly used spatial computing systems owing to their volume, variety and velocity). These problems fortunately can be combated by employing functional programming method or parallelization techniques

Healthy Transportation Choices with IoT and Smart Nudging

Modern technology has provided people with ease of living but at the same time has given birth to the problems of equally modern nature. For instance, high reliance on private transportation has resulted in unintended consequences such as high level of air pollution and congestion in urban cities. Another main disadvantage that is often overlooked is related to the rise of several noncommunicable diseases that are caused due to excessive dependence on cars and lack of physical activity. This thesis is entirely dedicated to encounter serious hazards of lack of physical activity by choosing unhealthy transportation choices. The interaction between people and the computers has become ubiquitous over the span of years. People interact in digital environment for a number of reasons. From checking weather conditions to running multinational trading businesses, computer driven digital automation has taken over what has always remained a manual handiwork. Cognizant of the potency of computer driven services and its authority, we propose applying nudge theory to encourage users to choose healthy options when it comes to any type of mobility. The first step involves researching about collecting, storing and performing analysis on data from different resources and then suggesting different techniques to manipulate it in order to perform an effective nudge

Study of collective intelligence form a clinical data warehouse as a potential model for clinical decision support

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Includes bibliographical references.Clinical decision support systems (CDSS) are developed primarily from knowledge gleaned from evidence-based research, guidelines, trusted resources and domain experts. While these resources generally represent information that is research proven, time-tested and consistent with current medical knowledge, they lack some qualities that would be desirable in a CDSS. For instance, the information is presented as generalized recommendations that are not specific to particular patients and may not consider certain subpopulations. In addition, the knowledge base that produces the guidelines may be outdated and may not reflect real-world practice. Ideally, resources for decision support should be timely, patient-specific, and represent current practice. Patient-oriented clinical decision support is particularly important in the practice of pediatrics because it addresses a population in constant flux. Every age represents a different set of physiological and developmental concerns and considerations, especially in medication dosing patterns. Patient clinical data warehouses (CDW) may be able to bridge the knowledge gap. CDWs contain the collective intelligence of various contributors (i.e. clinicians, administrators, etc.) where each data entry provides information regarding medical care for a patient in the real world. CDWs have the potential to provide information as current as the latest upload, be focused to specific subpopulations and reflect current clinical practice. In this paper, I study the potential of a well-known patient clinical data warehouse to provide information regarding pediatric levothyroxine dosing as a form of clinical decision support. I study the state of the stored data, the necessary data transformations and options for representing the data to effectively summarize and communicate the findings.(cont.) I also compare the resulting transformed data, representing actual practice within this population, against established dosing recommendations. Of the transformed records, 728 of the 854 (85.2%, [95% confidence interval 82.7:87.6]) medication records contained doses that were under the published recommended range for levothyroxine. As demonstrated by these results, real world practice can diverge from established recommendations. Delivering this information on real-world peer practice medication dosing to clinicians in real-time offers the potential to provide a valuable supplement to established dosing guidelines, enhancing the general and sometimes static dosing recommendations.by Elisabeth Lee Scheufele.S.M