The Elaborated Intrusion Theory of Desire: A 10-year retrospective and implications for addiction treatments

Ten years after the publication of Elaborated Intrusion (EI) Theory, there is now substantial research into its key predictions. The distinction between intrusive thoughts, which are driven by automatic processes, and their elaboration, involving controlled processing, is well established. Desires for both addictive substances and other desired targets are typically marked by imagery, especially when they are intense. Attention training strategies such as body scanning reduce intrusive thoughts, while concurrent tasks that introduce competing sensory information interfere with elaboration, especially if they compete for the same limited-capacity working memory resources. EI Theory has spawned new assessment instruments that are performing strongly and offer the ability to more clearly delineate craving from correlated processes. It has also inspired new approaches to treatment. In particular, training people to use vivid sensory imagery for functional goals holds promise as an intervention for substance misuse, since it is likely to both sustain motivation and moderate craving

EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria.

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria

Automated Detection of Substance-Use Status and Related Information from Clinical Text

This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system for the extraction of the substance-use status and related attributes (type, frequency, amount, quit-time, and period). The first stage uses a keyword search technique to detect sentences related to substance use and to exclude unrelated records. In the second stage, an extension of the NegEx negation detection algorithm is developed and employed for detecting the negated records. The third stage involves identifying the temporal status of the substance use by applying windowing and chunking methodologies. Finally, in the fourth stage, regular expressions, syntactic patterns, and keyword search techniques are used in order to extract the substance-use attributes. The proposed system achieves an F1-score of up to 0.99 for identifying substance-use-related records, 0.98 for detecting the negation status, and 0.94 for identifying temporal status. Moreover, F1-scores of up to 0.98, 0.98, 1.00, 0.92, and 0.98 are achieved for the extraction of the amount, frequency, type, quit-time, and period attributes, respectively. Natural Language Processing (NLP) and rule-based techniques are employed efficiently for extracting substance-use status and attributes, with the proposed system being able to detect substance-use status and attributes over both sentence-level and document-level data. Results show that the proposed system outperforms the compared state-of-the-art substance-use identification system on an unseen dataset, demonstrating its generalisability

The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health

Objective: The n2c2/UW SDOH Challenge explores the extraction of social determinant of health (SDOH) information from clinical notes. The objectives include the advancement of natural language processing (NLP) information extraction techniques for SDOH and clinical information more broadly. This paper presents the shared task, data, participating teams, performance results, and considerations for future work. Materials and Methods: The task used the Social History Annotated Corpus (SHAC), which consists of clinical text with detailed event-based annotations for SDOH events such as alcohol, drug, tobacco, employment, and living situation. Each SDOH event is characterized through attributes related to status, extent, and temporality. The task includes three subtasks related to information extraction (Subtask A), generalizability (Subtask B), and learning transfer (Subtask C). In addressing this task, participants utilized a range of techniques, including rules, knowledge bases, n-grams, word embeddings, and pretrained language models (LM). Results: A total of 15 teams participated, and the top teams utilized pretrained deep learning LM. The top team across all subtasks used a sequence-to-sequence approach achieving 0.901 F1 for Subtask A, 0.774 F1 Subtask B, and 0.889 F1 for Subtask C. Conclusions: Similar to many NLP tasks and domains, pretrained LM yielded the best performance, including generalizability and learning transfer. An error analysis indicates extraction performance varies by SDOH, with lower performance achieved for conditions, like substance use and homelessness, that increase health risks (risk factors) and higher performance achieved for conditions, like substance abstinence and living with family, that reduce health risks (protective factors)

The Effects of Electronic Treatment Reminder Cues on Relapse Prevention

Substance use is highly prevalent in the United States, and although treatments designed to reduce substance use have shown promise, relapse rates between 40% and 70% following treatment have been reported in recent studies. Given the high rate and chronicity of relapse following substance abuse treatment, conducting research aimed to develop techniques to lower the risk of relapse following treatment is imperative. A promising option to reduce relapse is to use treatment reminder cues, or cues that are salient features of the treatment environment that can be used to extend the effects of treatment into non-treatment settings. This study investigated the effects of treatment reminder cues on rates of relapse in 50 male and female individuals entering intensive outpatient treatment for substance abuse. It utilized a one-month randomized and controlled design using state-of-the-art electronic handheld computer technology. Participants in the experimental condition were prompted to read and respond to four treatment reminder cues per day in addition to one daily diary survey assessing for a variety of proximal variables related to relapse. All participants were asked to complete assessment questionnaires of relevant variables that may affect relapse at baseline and 1-month follow-up. Chi-square tests were used to determine if adding treatment reminder cues to standard treatment resulted in less relapse relative to standard treatment alone, and whether onset occurred significantly later for those receiving treatment reminder cues. Binary logistic regression analyses investigated the extent to which compliance with treatment reminder cues was associated with relapse. Results indicated that twice as many individuals in the control group relapsed compared to the experimental group, which approached statistical significance. In addition, those in the experimental group relapsed substantially later than did those in the control group. Results indicated no effect of increased compliance on decreased relapse. Overall, this study holds the promise of providing a simple, inexpensive, and effective strategy for attenuating rates of relapse or delaying the onset of return to use by extending the context of treatment beyond the immediate therapeutic setting. Clinical and research implications, and future directions for substance abuse research are discussed

Consolidated List of Requirements

This document is a consolidated catalogue of requirements for the Electronic Health Care Record (EHCR) and Electronic Health Care Record Architecture (EHCRA), gleaned largely from work done in the EU Framework III and IV programmes and CEN, but also including input from other sources including world-wide standardisation initiatives. The document brings together the relevant work done into a classified inventory of requirements to inform the on-going standardisation process as well as act as a guide to future implementation of EHCRA-based systems. It is meant as a contribution both to understanding of the standard and to the work that is being considered to improve the standard. Major features include the classification into issues affecting the Health Care Record, the EHCR, EHCR processing, EHCR interchange and the sharing of health care information and EHCR systems. The principal information sources are described briefly. It is offered as documentation that is complementary to the four documents of the ENV 13606 Parts I-IV produced by CEN Pts 26,27,28,29. The requirements identified and classified in this deliverable are referenced in other deliverables

Unseen Affective Faces Influence Person Perception Judgments in Schizophrenia.

To demonstrate the influence of unconscious affective processing on consciously processed information among people with and without schizophrenia, we used a continuous flash suppression (CFS) paradigm to examine whether early and rapid processing of affective information influences first impressions of structurally neutral faces. People with and without schizophrenia rated visible neutral faces as more or less trustworthy, warm, and competent when paired with unseen smiling or scowling faces compared to when paired with unseen neutral faces. Yet, people with schizophrenia also exhibited a deficit in explicit affect perception. These findings indicate that early processing of affective information is intact in schizophrenia but the integration of this information with semantic contexts is problematic. Furthermore, people with schizophrenia who were more influenced by smiling faces presented outside awareness reported experiencing more anticipatory pleasure, suggesting that the ability to rapidly process affective information is important for anticipation of future pleasurable events