Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.

Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.

Objective: This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).

Development and External Validation of a Machine Learning Model to Predict Restriction from Spirometry.

Background: Though European Respiratory Society and American Thoracic Society (ERS/ATS) guidelines for pulmonary function test (PFT) interpretation recommend the use of the forced vital capacity (FVC) lower limit of normal (LLN) to exclude restriction, recent data suggest that the negative predictive value (NPV) of the FVC LLN is lower than has been accepted, particularly among non-Hispanic Black patients. We sought to develop and externally validate a machine learning (ML) model to predict restriction from spirometry and determine whether its use may improve the accuracy and equity of PFT interpretation.

Methods: We included PFTs with both static and dynamic lung volume measurements for patients between 18 and 80 years of age who were tested at pulmonary diagnostic labs within two health systems.

Large language models display human-like social desirability biases in Big Five personality surveys.

Large language models (LLMs) are becoming more widely used to simulate human participants and so understanding their biases is important. We developed an experimental framework using Big Five personality surveys and uncovered a previously undetected social desirability bias in a wide range of LLMs. By systematically varying the number of questions LLMs were exposed to, we demonstrate their ability to infer when they are being evaluated.

Disparities by Race and Urbanicity in Online Health Care Facility Reviews.

Importance: Online review platforms offer valuable insights into patient satisfaction and the quality of health care services, capturing content and trends that traditional metrics might miss. The COVID-19 pandemic has disrupted health care services, influencing patient experiences.

Objective: To examine health care facility numerical ratings and patient experience reported on an online platform by facility type and area demographic characteristics after the COVID-19 pandemic (ie, post-COVID).

People judge others more harshly after talking to bots.

People now commonly interact with Artificial Intelligence (AI) agents. How do these interactions shape how humans perceive each other? In two preregistered studies (total = 1,261), we show that people evaluate other humans more harshly after interacting with an AI (compared with an unrelated purported human). In Study 1, participants who worked on a creative task with AIs (versus purported humans) subsequently rated another purported human's work more negatively.

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy.

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data are captured during this process, but these data currently play a small role in surgical planning.

Minimally Invasive Anterolateral Cervical Cordotomy for Intractable Cancer Pain Using Microtubular Retractors: A Single Institution Case Series.

Background And Objectives: As advancements in cancer treatments have allowed patients with a high burden of disease to live longer, the number of patients who present with debilitating refractory pain has increased. Anterolateral cordotomy has long been used for the treatment of intractable unilateral cancer pain using either an imaging-guided percutaneous approach or an open surgical approach. In this report, we describe a novel minimally invasive modification to the open surgical approach.

History and Perspectives of Hyperradical, Laterally Extended Parametrectomy (LEP).

Cervical cancer has been and still is a major global health problem and a major treatment challenge for which surgical interventions have played a key role throughout the past century. In early stages (I/A2-II/B), where high-risk factors are not present, the efficacy of surgical and radiotherapy treatment has been considered equivalent with different (treatment modality specific) complications and quality of life consequences. Negative prognostic factors in early stages of the disease (pelvic lymph-node positivity) and in more advanced stages (parametrial and/or surgical margins' tumor involvement) forecast the deterioration of outlooks for good life expectancy.

Tapping into alcohol use during COVID: Drinking correlates among bartenders and servers.

The COVID pandemic placed a spotlight on alcohol use and the hardships of working within the food and beverage industry, with millions left jobless. Following previous studies that have found elevated rates of alcohol problems among bartenders and servers, here we studied the alcohol use of bartenders and servers who were employed during COVID. From February 12-June 16, 2021, in the midst of the U.

An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives.

Mental health conversational agents (a.k.a.

Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation.

Large language models (LLMs) such as Open AI's GPT-4 (which power ChatGPT) and Google's Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments.

Key language markers of depression on social media depend on race.

Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g.

A cross-cultural examination of temporal orientation through everyday language on social media.

Past research has shown that culture can form and shape our temporal orientation-the relative emphasis on the past, present, or future. However, there are mixed findings on how temporal orientations vary between North American and East Asian cultures due to the limitations of survey methodology and sampling. In this study, we applied an inductive approach and leveraged big data and natural language processing between two popular social media platforms-Twitter and Weibo-to assess the similarities and differences in temporal orientation in the United States of America and China, respectively.

Language-based EMA assessments help understand problematic alcohol consumption.

Background: Unhealthy alcohol consumption is a severe public health problem. But low to moderate alcohol consumption is associated with high subjective well-being, possibly because alcohol is commonly consumed socially together with friends, who often are important for subjective well-being. Disentangling the health and social complexities of alcohol behavior has been difficult using traditional rating scales with cross-section designs.

Are LRRK2 p.G2019S or GBA1 variants associated with long-term outcomes of deep brain stimulation for Parkinson's disease?

Background: Deep brain stimulation (DBS) is a well-established treatment option for individuals with advanced Parkinson's disease (PD). The potential influence of the LRRK2 p.G2019S or GBA1 variants on its lasting efficacy and adverse effects should be better characterized.

Interictal intracranial EEG asymmetry lateralizes temporal lobe epilepsy.

Patients with drug-resistant temporal lobe epilepsy often undergo intracranial EEG recording to capture multiple seizures in order to lateralize the seizure onset zone. This process is associated with morbidity and often ends in postoperative seizure recurrence. Abundant interictal (between-seizure) data is captured during this process, but these data currently play a small role in surgical planning.

Filling in the white space: Spatial interpolation with Gaussian processes and social media data.

Full national coverage below the state level is difficult to attain through survey-based data collection. Even the largest survey-based data collections, such as the CDC's Behavioral Risk Factor Surveillance System or the Gallup-Healthways Well-being Index (both with more than 300,000 responses p.a.

Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder.

Background: Sensors within smartphones, such as accelerometer and location, can describe longitudinal markers of behavior as represented through devices in a method called digital phenotyping. This study aimed to assess the feasibility of digital phenotyping for patients with alcohol-associated liver disease and alcohol use disorder, determine correlations between smartphone data and alcohol craving, and establish power assessment for future studies to prognosticate clinical outcomes.

Methods: A total of 24 individuals with alcohol-associated liver disease and alcohol use disorder were instructed to download the AWARE application to collect continuous sensor data and complete daily ecological momentary assessments on alcohol craving and mood for up to 30 days.

The online language of work-personal conflict.

With the blurring of boundaries in this digital age, there is increasing concern around work-personal conflict. Assessing and tracking work-personal conflict is critical as it not only affects individual workers but is also a vital measure among broader well-being and economic indices. This inductive study examines the extent to which work-personal conflict corresponds to individuals' language use on social media.

A transdiagnostic, dimensional classification of anxiety shows improved parsimony and predictive noninferiority to DSM.

The current conceptualization of anxiety in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)-which includes 11 anxiety disorders plus additional anxiety-related conditions-does not align with accumulating evidence that anxiety is transdiagnostic and dimensional in nature. Transdiagnostic dimensional anxiety models have been proposed, yet they measure anxiety at either a very broad (e.g.

Gastrointestinal parasitosis in cattle: Unveiling the landscape across diverse production systems in Bangladesh.

Background: Factors influencing parasitosis in cattle in Bangladesh remain inadequately explored, necessitating a comprehensive investigation for interventions and sustainable livestock farming.

Objectives: We conducted this study to estimate the prevalence and distribution of gastrointestinal parasites, exploring their intricate relationship with farm management practices across a spectrum of small-, medium-, and large-scale commercial farms.

Methods: We conducted this study in the Chattogram district of Bangladesh.