Suicide is the 10th leading cause of death in the USA and globally. Despite decades of research, the ability to predict who will die by suicide is still no better than 50%. Traditional screening instruments have helped identify risk factors for suicide, but they have not provided accurate predictive power for reducing death rates. Over the past decade, natural language processing (NLP), a form of machine learning (ML), has been used to identify suicide risk by analyzing language data. Recent work has demonstrated the successful integration of a suicide risk screening interview to collect language data for NLP analysis from patients in two emergency departments (ED) of a large healthcare system. Results indicated that ML/NLP models performed well identifying patients that came to the ED for suicide risk. However, little is known about the clinician's perspective of how a qualitative brief interview suicide risk screening tool to collect language data for NLP integrates into an ED workflow. This report highlights the feedback and observations of patient experiences obtained from clinicians using brief suicide screening interviews. The investigator used an open-ended, narrative interview approach to inquire about the qualitative interview process. Three overarching themes were identified: behavioral health workflow, clinical implications of interview probes, and integration of an application into provider patient experience. Results suggest a brief, qualitative interview method was feasible, person-centered, and useful as a suicide risk detection approach. 

/pdf/user-feedback-on-the-use-of-a-natural-language-processing-34vmok7w.pdf

User Feedback on the Use of a Natural Language Processing Application to Screen for Suicide Risk in the Emergency Department

Abstract Purpose of the Review Acid suppression with proton pump inhibitors (PPIs) represents the standard of care in the treatment of acid-related diseases. However, despite their effectiveness, PPIs display some intrinsic limitations, which underlie the unmet clinical needs that have been identified over the past decades. The aims of this review are to summarize the current status and future development of the new class of antisecretory drugs (potassium-competitive acid blockers, P-CABs) that have recently been introduced into medical practice. Recent Findings Over the past decades, clinical needs unmet by the current acid suppressants have been recognized, especially in the management of patients with GERD, Helicobacter pylori infection and NSAID-related peptic ulcer. The failure to address these needs is mainly due to their inability to achieve a consistent acid suppression in all patients and, particularly, to control nighttime acidity. It was then realized that an extended duration of acid suppression would exert additional benefits. The available data with P-CABs show that they are able to address these unmet clinical needs. Summary Four different P-CABs (vonoprazan, tegoprazan, fexuprazan and keverprazan) are currently available. However, only two of them are approved outside Asia. Vonoprazan is available in North, Central and South America while tegoprazan is marketed only in Latin American countries. Two other compounds (namely linazapran glurate and zestaprazan) are presently under clinical development. While clinical trials on GERD have been performed with all P-CABs, only vonoprazan and tegoprazan have been investigated as components of Helicobacter pylori eradication regimens. The available data show that—in the above two clinical indications—P-CABs provide similar or better efficacy in comparison with PPIs. Their safety in the short-term overlaps that of PPIs, but data from long-term treatment are needed. 

Potassium-competitive Acid Blockers: Current Clinical Use and Future Developments

Background Current depression, anxiety, and suicide screening techniques rely on retrospective patient reported symptoms to standardized scales. A qualitative approach to screening combined with the innovation of natural language processing (NLP) and machine learning (ML) methods have shown promise to enhance person-centeredness while detecting depression, anxiety, and suicide risk from in-the-moment patient language derived from an open-ended brief interview. Objective To evaluate the performance of NLP/ML models to identify depression, anxiety, and suicide risk from a single 5–10-min semi-structured interview with a large, national sample. Method Two thousand four hundred sixteen interviews were conducted with 1,433 participants over a teleconference platform, with 861 (35.6%), 863 (35.7%), and 838 (34.7%) sessions screening positive for depression, anxiety, and suicide risk, respectively. Participants completed an interview over a teleconference platform to collect language about the participants’ feelings and emotional state. Logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB) models were trained for each condition using term frequency-inverse document frequency features from the participants’ language. Models were primarily evaluated with the area under the receiver operating characteristic curve (AUC). Results The best discriminative ability was found when identifying depression with an SVM model (AUC = 0.77; 95% CI = 0.75–0.79), followed by anxiety with an LR model (AUC = 0.74; 95% CI = 0.72–0.76), and an SVM for suicide risk (AUC = 0.70; 95% CI = 0.68–0.72). Model performance was generally best with more severe depression, anxiety, or suicide risk. Performance improved when individuals with lifetime but no suicide risk in the past 3 months were considered controls. Conclusion It is feasible to use a virtual platform to simultaneously screen for depression, anxiety, and suicide risk using a 5-to-10-min interview. The NLP/ML models performed with good discrimination in the identification of depression, anxiety, and suicide risk. Although the utility of suicide risk classification in clinical settings is still undetermined and suicide risk classification had the lowest performance, the result taken together with the qualitative responses from the interview can better inform clinical decision-making by providing additional drivers associated with suicide risk.

/pdf/virtually-screening-adults-for-depression-anxiety-and-1hajhskq.pdf

Virtually screening adults for depression, anxiety, and suicide risk using machine learning and language from an open-ended interview

Suicide attempts are an emerging health problem around the world. Increased levels of IL-6 have been associated with suicidal behavior. Therefore, the aims of this study were to evaluate the serum levels of IL-6 in individuals with suicide attempts and a comparison group and to associate the IL-6 levels with the lethality of the suicide attempt. Additionally, we associated the rs2228145 polymorphism of the IL6R gene with suicide attempts or with the IL-6 serum levels. Suicide attempts and their lethality were evaluated using the Columbia Suicide Severity Rating Scale. The serum concentrations of IL-6 were measured by the ELISA technique in individuals with suicide attempts and then compared to a control group. The rs2228145 polymorphism of the IL6R gene was analyzed by real-time polymerase chain reaction. We found elevated serum levels of IL-6 in the suicide attempt group when compared to the control group (F = 10.37, p = 0.002). However, we found no differences of the IL-6 levels between high and low lethality. The IL6R gene polymorphism rs2479409 was not associated with suicide attempts. Our data suggest that IL-6 serum is increased in individuals with suicide attempts.

/pdf/high-serum-levels-of-il-6-are-associated-with-suicide-1qeskte2.pdf

High Serum Levels of IL-6 Are Associated with Suicide Attempt but Not with High Lethality Suicide Attempts: A Preliminary Case–Control Study

Post-traumatic stress disorder (PTSD) is a highly prevalent psychological disorder characterized by intense feelings of fear or helplessness after experiencing a traumatic event. PTSD is highly comorbid with mood disorders and patients are at increased risk for suicide. The present study aimed to identify neural connectivity alterations associated with suicidal ideation (SI) in PTSD patients by using resting-state functional magnetic resonance imaging. Voxel-to-voxel intrinsic connectivity was compared between PTSD patients with no (N-SI; N = 26) and high (H-SI; N = 7) SI. Region-to-voxel functional connectivity analysis was performed to identify the regions that contributed to intrinsic connectivity changes. H-SI patients had increased connectivity to various brain regions representing the central executive network, salience network, and default mode network in the frontal, temporal, and occipital lobes as well as subcortical structures involved in executive and limbic functioning, and motor systems. These results suggest SI is associated with large network-level alterations in PTSD patients and is not the result of neuronal abnormalities in any one specific area. 

Functional Connectivity Alterations in PTSD Patients with Suicidal Ideation

A validation study of PHQ-9 suicide item with the Columbia suicide severity rating scale in outpatients with mood disorders at National Network of depression centers.

Review of the clinical development of fexuprazan for gastroesophageal reflux–related disease

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Alisha Merchant

Author Tools

Chat about Author

Papers

A validation study of PHQ-9 suicide item with the Columbia suicide severity rating scale in outpatients with mood disorders at National Network of depression centers.

Review of the clinical development of fexuprazan for gastroesophageal reflux–related disease