Expert guided natural language processing using one-class classification.

Introduction: Automatically identifying specific phenotypes in free-text clinical notes is critically important for the reuse of clinical data. In this study, the authors combine expert-guided feature (text) selection with one-class classification for text processing.

Objectives: To compare the performance of one-class classification to traditional binary classification; to evaluate the utility of feature selection based on expert-selected salient text (snippets); and to determine the robustness of these models with respects to irrelevant surrounding text.

Twinlist: novel user interface designs for medication reconciliation.

Medication reconciliation is an important and complex task for which careful user interface design has the potential to help reduce errors and improve quality of care. In this paper we focus on the hospital discharge scenario and first describe a novel interface called Twinlist. Twinlist illustrates the novel use of spatial layout combined with multi-step animation, to help medical providers see what is different and what is similar between the lists (e.

Optimized dual threshold entity resolution for electronic health record databases--training set size and active learning.

Clinical databases may contain several records for a single patient. Multiple general entity-resolution algorithms have been developed to identify such duplicate records. To achieve optimal accuracy, algorithm parameters must be tuned to a particular dataset.

SYFSA: a framework for systematic yet flexible systems analysis.

Although technological or organizational systems that enforce systematic procedures and best practices can lead to improvements in quality, these systems must also be designed to allow users to adapt to the inherent uncertainty, complexity, and variations in healthcare. We present a framework, called Systematic Yet Flexible Systems Analysis (SYFSA) that supports the design and analysis of Systematic Yet Flexible (SYF) systems (whether organizational or technical) by formally considering the tradeoffs between systematicity and flexibility. SYFSA is based on analyzing a task using three related problem spaces: the idealized space, the natural space, and the system space.

A benchmark comparison of deterministic and probabilistic methods for defining manual review datasets in duplicate records reconciliation.

Introduction: Clinical databases require accurate entity resolution (ER). One approach is to use algorithms that assign questionable cases to manual review. Few studies have compared the performance of common algorithms for such a task.

Graph-based signal integration for high-throughput phenotyping.

Background: Electronic Health Records aggregated in Clinical Data Warehouses (CDWs) promise to revolutionize Comparative Effectiveness Research and suggest new avenues of research. However, the effectiveness of CDWs is diminished by the lack of properly labeled data. We present a novel approach that integrates knowledge from the CDW, the biomedical literature, and the Unified Medical Language System (UMLS) to perform high-throughput phenotyping.

Deterministic binary vectors for efficient automated indexing of MEDLINE/PubMed abstracts.

The need to maintain accessibility of the biomedical literature has led to development of methods to assist human indexers by recommending index terms for newly encountered articles. Given the rapid expansion of this literature, it is essential that these methods be scalable. Document vector representations are commonly used for automated indexing, and Random Indexing (RI) provides the means to generate them efficiently.

Collaborative knowledge acquisition for the design of context-aware alert systems.

Objective: To present a framework for combining implicit knowledge acquisition from multiple experts with machine learning and to evaluate this framework in the context of anemia alerts.

Materials And Methods: Five internal medicine residents reviewed 18 anemia alerts, while 'talking aloud'. They identified features that were reviewed by two or more physicians to determine appropriate alert level, etiology and treatment recommendation.

[Biomedical informatics].

Biomedical Informatics is a new discipline that arose from the need to incorporate information technologies to the generation, storage, distribution and analysis of information in the domain of biomedical sciences. This discipline comprises basic biomedical informatics, and public health informatics. The development of the discipline in Chile has been modest and most projects have originated from the interest of individual people or institutions, without a systematic and coordinated national development.

Automated medication reconciliation and complexity of care transitions.

Medication reconciliation is a National Patient Safety Goal (NPSG) from The Joint Commission (TJC) that entails reviewing all medications a patient takes after a health care transition. Medication reconciliation is a resource-intensive, error-prone task, and the resources to accomplish it may not be routinely available. Computer-based methods have the potential to overcome these barriers.

Medication Reconciliation: Work Domain Ontology, prototype development, and a predictive model.

Medication errors can result from administration inaccuracies at any point of care and are a major cause for concern. To develop a successful Medication Reconciliation (MR) tool, we believe it necessary to build a Work Domain Ontology (WDO) for the MR process. A WDO defines the explicit, abstract, implementation-independent description of the task by separating the task from work context, application technology, and cognitive architecture.

Probabilistic techniques for obtaining accurate patient counts in Clinical Data Warehouses.

Proposal and execution of clinical trials, computation of quality measures and discovery of correlation between medical phenomena are all applications where an accurate count of patients is needed. However, existing sources of this type of patient information, including Clinical Data Warehouses (CDWs) may be incomplete or inaccurate. This research explores applying probabilistic techniques, supported by the MayBMS probabilistic database, to obtain accurate patient counts from a Clinical Data Warehouse containing synthetic patient data.

Predicting biomedical document access as a function of past use.

Objective: To determine whether past access to biomedical documents can predict future document access.

Materials And Methods: The authors used 394 days of query log (August 1, 2009 to August 29, 2010) from PubMed users in the Texas Medical Center, which is the largest medical center in the world. The authors evaluated two document access models based on the work of Anderson and Schooler.

MEDRank: using graph-based concept ranking to index biomedical texts.

Background: As the volume of biomedical text increases exponentially, automatic indexing becomes increasingly important. However, existing approaches do not distinguish central (or core) concepts from concepts that were mentioned in passing. We focus on the problem of indexing MEDLINE records, a process that is currently performed by highly trained humans at the National Library of Medicine (NLM).

Sequential incoherence in a multi-party synchronous computer mediated communication for an introductory Health Informatics course.

Online courses will play a key role in the high-volume Informatics education required to train the personnel that will be necessary to fulfill the health IT needs of the country. Online courses can cause feelings of isolation in students. A common way to address these feelings is to hold synchronous online "chats" for students.

A day in the life of PubMed: analysis of a typical day's query log.

Objective: To characterize PubMed usage over a typical day and compare it to previous studies of user behavior on Web search engines.

Design: We performed a lexical and semantic analysis of 2,689,166 queries issued on PubMed over 24 consecutive hours on a typical day.

Measurements: We measured the number of queries, number of distinct users, queries per user, terms per query, common terms, Boolean operator use, common phrases, result set size, MeSH categories, used semantic measurements to group queries into sessions, and studied the addition and removal of terms from consecutive queries to gauge search strategies.

Using incomplete citation data for MEDLINE results ranking.

Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important.

Using hit curves to compare search algorithm performance.

Databases continue to grow but the metrics available to evaluate information retrieval systems have not changed. Large collections such as MEDLINE and the World Wide Web contain many relevant documents for common queries. Ranking is therefore increasingly important and successful information retrieval systems, such as Google, have emphasized ranking.

Using citation data to improve retrieval from MEDLINE.

To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology.