Towards Predicting Smoking Events for Just-in-time Interventions.

Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor to detect and predict cigarette smoking events in naturalistic settings with several machine learning approaches. We have collected and analyzed data for 28 participants observed over a two-week period.

Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition.

Objective: : Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks.

Materials And Methods: : We implemented 4 state-of-the-art text generation models, namely CharRNN, SegGAN, GPT-2, and CTRL, to generate clinical text for the History and Present Illness section.

Comparing Existing Resources to Represent Dietary Supplements.

Dietary supplements, often considered as food, are widely consumed despite of limited knowledge around their safety/efficacy and any well-established regulatory policies, unlike their drug counterparts. Informatics methods may be useful in filling this knowledge gap, however, the lack of standardized representation of DS hinders this progress. In this pilot study, five electronic DS resources, i.

Detecting Signals of Interactions Between Warfarin and Dietary Supplements in Electronic Health Records.

Drug and supplement interactions (DSIs) have drawn widespread attention due to their potential to affect therapeutic response and adverse event risk. Electronic health records provide a valuable source where the signals of DSIs can be identified and characterized. We detected signals of interactions between warfarin and seven dietary supplements, viz.

Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data.

Abbreviation disambiguation in clinical texts is a problem handled well by fully supervised machine learning methods. Acquiring training data, however, is expensive and would be impractical for large numbers of abbreviations in specialized corpora. An alternative is a semi-supervised approach, in which training data are automatically generated by substituting long forms in natural text with their corresponding abbreviations.

Use of an automated mobile application to assess effects of nicotine withdrawal on verbal fluency: A pilot study.

Mild-to-moderate impairment in frontally mediated functions such as sustained attention, working memory, and inhibition have been found to occur during tobacco withdrawal and may present a barrier to successful cessation. These findings have led to studies evaluating cessation treatments that target nicotine withdrawal related cognitive impairment. The instruments currently used to assess cognitive function provide detailed and specific information but have limitations including being time consuming, cumbersome, anxiety provoking, and having poor ecological validity.

NLP-PIER: A Scalable Natural Language Processing, Indexing, and Searching Architecture for Clinical Notes.

Many design considerations must be addressed in order to provide researchers with full text and semantic search of unstructured healthcare data such as clinical notes and reports. Institutions looking at providing this functionality must also address the big data aspects of their unstructured corpora. Because these systems are complex and demand a non-trivial investment, there is an incentive to make the system capable of servicing future needs as well, further complicating the design.

Corpus domain effects on distributional semantic modeling of medical terms.

Motivation: Automatically quantifying semantic similarity and relatedness between clinical terms is an important aspect of text mining from electronic health records, which are increasingly recognized as valuable sources of phenotypic information for clinical genomics and bioinformatics research. A key obstacle to development of semantic relatedness measures is the limited availability of large quantities of clinical text to researchers and developers outside of major medical centers. Text from general English and biomedical literature are freely available; however, their validity as a substitute for clinical domain to represent semantics of clinical terms remains to be demonstrated.

A comparative observational study of inpatient clinical note-entry and reading/retrieval styles adopted by physicians.

Objective: The objective of this study is to understand physicians' usage of inpatient notes by (i) ascertaining different clinical note-entry and reading/retrieval styles in two different and widely used Electronic Health Record (EHR) systems, (ii) extrapolating potential factors leading to adoption of various note-entry and reading/retrieval styles and (iii) determining the amount of time to task associated with documenting different types of clinical notes.

Methods: In order to answer "what" and "why" questions on physicians' adoption of certain-note-entry and reading/retrieval styles, an ethnographic study entailing Internal Medicine residents, with a mixed data analysis approach was performed. Participants were observed interacting with two different EHR systems in inpatient settings.

Using Automatic Speech Recognition to Assess Spoken Responses to Cognitive Tests of Semantic Verbal Fluency.

Cognitive tests of verbal fluency (VF) consist of verbalizing as many words as possible in one minute that either start with a specific letter of the alphabet or belong to a specific semantic category. These tests are widely used in neurological, psychiatric, mental health, and school settings and their validity for clinical applications has been extensively demonstrated. However, VF tests are currently administered and scored manually making them too cumbersome to use, particularly for longitudinal cognitive monitoring in large populations.

Analyzing Operative Note Structure in Development of a Section Header Resource.

Operative notes contain essential details of surgical procedures and are an important form of clinical documentation. Sections within operative notes segment provide high level note structure. We evaluated the HL7 Implementation Guide for Clinical Document Architecture Release 2.

Evaluation of Herbal and Dietary Supplement Resource Term Coverage.

The use of Complementary and Alternative Medicine (CAM) is increasingly popular in places like North America and Europe where western medicine is primarily practiced. People are consuming herbal and dietary supplements along with western medications simultaneously. Sometimes, supplements and drugs react with one another via antagonistic or potentiation actions of the drug or supplement resulting in an adverse event.

Evaluating Term Coverage of Herbal and Dietary Supplements in Electronic Health Records.

Herbal and dietary supplement consumption has rapidly expanded in recent years. Due to pharmacological and metabolic characteristics of some supplements, they can interact with prescription medications, potentially leading to clinically important and potentially preventable adverse reactions. Electronic health record (EHR) system provides a valuable source from which drug-supplement interactions can be mined and assessed for their clinical effects.

Pharmacokinetic-pharmacodynamic modelling of intravenous and oral topiramate and its effect on phonemic fluency in adult healthy volunteers.

Aims: The aim was to develop a quantitative approach that characterizes the magnitude of and variability in phonemic generative fluency scores as measured by the Controlled Oral Word Association (COWA) test in healthy volunteers after administration of an oral and a novel intravenous (IV) formulation of topiramate (TPM).

Methods: Nonlinear mixed-effects modelling was used to describe the plasma TPM concentrations resulting from oral or IV administration. A pharmacokinetic-pharmacodynamic (PK-PD) model was developed sequentially to characterize the effect of TPM concentrations on COWA with different distributional assumptions.

Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs.

In this study, we report on the performance of an automated approach to discovery of potential prostate cancer drugs from the biomedical literature. We used the semantic relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations using SemRep, to extract potential relationships using knowledge of cancer drugs pathways. Two cancer drugs pathway schemas were constructed using these relationships extracted from SemMedDB.

Language networks associated with computerized semantic indices.

Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters.

Using semantic predications to uncover drug-drug interactions in clinical data.

In this study we report on potential drug-drug interactions between drugs occurring in patient clinical data. Results are based on relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations (titles and abstracts) using SemRep. The core of our methodology is to construct two potential drug-drug interaction schemas, based on relationships extracted from SemMedDB.

Using language models to identify relevant new information in inpatient clinical notes.

Redundant information in clinical notes within electronic health record (EHR) systems is ubiquitous and may negatively impact the use of these notes by clinicians, and, potentially, the efficiency of patient care delivery. Automated methods to identify redundant versus relevant new information may provide a valuable tool for clinicians to better synthesize patient information and navigate to clinically important details. In this study, we investigated the use of language models for identification of new information in inpatient notes, and evaluated our methods using expert-derived reference standards.

U-path: An undirected path-based measure of semantic similarity.

In this paper, we present the results of a method using undirected paths to determine the degree of semantic similarity between two concepts in a dense taxonomy with multiple inheritance. The overall objective of this work was to explore methods that take advantage of dense multi-hierarchical taxonomies that are more graph-like than tree-like by incorporating the proximity of concepts with respect to each other within the entire is-a hierarchy. Our hypothesis is that the proximity of the concepts regardless of how they are connected is an indicator to the degree of their similarity.

Effects of time constraints on clinician-computer interaction: a study on information synthesis from EHR clinical notes.

Background: Time is a measurable and critical resource that affects the quality of services provided in clinical practice. There is limited insight into the effects of time restrictions on clinicians' cognitive processes with the electronic health record (EHR) in providing ambulatory care.

Objective: To understand the impact of time constraints on clinicians' synthesis of text-based EHR clinical notes.

A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the nun study.

Generative semantic verbal fluency (SVF) tests show early and disproportionate decline relative to other abilities in individuals developing Alzheimer's disease. Optimal performance on SVF tests depends on the efficiency of using clustered organization of semantically related items and the ability to switch between clusters. Traditional approaches to clustering and switching have relied on manual determination of clusters.

A qualitative analysis of EHR clinical document synthesis by clinicians.

Clinicians utilize electronic health record (EHR) systems during time-constrained patient encounters where large amounts of clinical text must be synthesized at the point of care. Qualitative methods may be an effective approach for uncovering cognitive processes associated with the synthesis of clinical documents within EHR systems. We utilized a think-aloud protocol and content analysis with the goal of understanding cognitive processes and barriers involved as medical interns synthesized patient clinical documents in an EHR system to accomplish routine clinical tasks.

Using SemRep to label semantic relations extracted from clinical text.

In this paper we examined the relationship between semantic relatedness among medical concepts found in clinical reports and biomedical literature. Our objective is to determine whether relations between medical concepts identified from Medline abstracts may be used to inform us as to the nature of the association between medical concepts that appear to be closely related based on their distribution in clinical reports. We used a corpus of 800k inpatient clinical notes as a source of data for determining the strength of association between medical concepts and SemRep database as a source of labeled relations extracted from Medline abstracts.

Automated semantic indices related to cognitive function and rate of cognitive decline.

The objective of our study is to introduce a fully automated, computational linguistic technique to quantify semantic relations between words generated on a standard semantic verbal fluency test and to determine its cognitive and clinical correlates. Cognitive differences between patients with Alzheimer's disease and mild cognitive impairment are evident in their performance on the semantic verbal fluency test. In addition to the semantic verbal fluency test score, several other performance characteristics sensitive to disease status and predictive of future cognitive decline have been defined in terms of words generated from semantically related categories (clustering) and shifting between categories (switching).