Does generative artificial intelligence pose a risk to performance validity test security?

Objective: We examined the performance validity test (PVT) security risk presented by artificial intelligence (AI) chatbots asking questions about neuropsychological evaluation and PVTs on two popular generative AI sites.

Method: In 2023 and 2024, multiple questions were posed to ChatGPT-3 and Bard (now Gemini). One set started generally and refined follow-up questions based on AI responses.

Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets.

Electronic medical records (EMRs) are stored in relational databases. It can be challenging to access the required information if the user is unfamiliar with the database schema or general database fundamentals. Hence, researchers have explored text-to-SQL generation methods that provide healthcare professionals direct access to EMR data without needing a database expert.

A marker-based neural network system for extracting social determinants of health.

Objective: The impact of social determinants of health (SDoH) on patients' healthcare quality and the disparity is well known. Many SDoH items are not coded in structured forms in electronic health records. These items are often captured in free-text clinical notes, but there are limited methods for automatically extracting them.

Consideration of Acute Porphyria in an Emergency Department Patient: A Case Report and Discussion of Common Pitfalls.

Introduction: Porphyria refers to a group of disorders associated with defects in heme synthesis. They can be associated with severely debilitating features, including abdominal pain, psychiatric symptoms, neurological defects, and cardiovascular irregularities. Although these diseases are rare, patients with attacks often do present to the emergency department (ED) where consideration of porphyria is generally not included in the differential.

Perceived Barriers to the De-implementation of Routine Preoperative History & Physicals Preceding Low-Risk Ambulatory Procedures: A Qualitative Study of Surgeon Perspectives.

Background: For patients undergoing surgery at an Ambulatory Surgical Center, recent changes to Centers for Medicare and Medicaid Services policy allow for the omission of a 30-day preoperative History and Physical (H&P). Preoperative H&Ps for low-risk surgery may contribute to health care waste and lead to unnecessary preoperative testing and treatment cascades.

Methods: In this qualitative study, we conducted 30 semi-structured interviews with surgeons who frequently perform low-risk surgeries.

Assigning ICD-O-3 Codes to Pathology Reports using Neural Multi-Task Training with Hierarchical Regularization.

Tracking population-level cancer information is essential for researchers, clinicians, policymakers, and the public. Unfortunately, much of the information is stored as unstructured data in pathology reports. Thus, too process the information, we require either automated extraction techniques or manual curation.

The risk of racial bias while tracking influenza-related content on social media using machine learning.

Objective: Machine learning is used to understand and track influenza-related content on social media. Because these systems are used at scale, they have the potential to adversely impact the people they are built to help. In this study, we explore the biases of different machine learning methods for the specific task of detecting influenza-related content.

Cross-registry neural domain adaptation to extract mutational test results from pathology reports.

Objective: We study the performance of machine learning (ML) methods, including neural networks (NNs), to extract mutational test results from pathology reports collected by cancer registries. Given the lack of hand-labeled datasets for mutational test result extraction, we focus on the particular use-case of extracting Epidermal Growth Factor Receptor mutation results in non-small cell lung cancers. We explore the generalization of NNs across different registries where our goals are twofold: (1) to assess how well models trained on a registry's data port to test data from a different registry and (2) to assess whether and to what extent such models can be improved using state-of-the-art neural domain adaptation techniques under different assumptions about what is available (labeled vs unlabeled data) at the target registry site.

Neural transfer learning for assigning diagnosis codes to EMRs.

Objective: Electronic medical records (EMRs) are manually annotated by healthcare professionals and specialized medical coders with a standardized set of alphanumeric diagnosis and procedure codes, specifically from the International Classification of Diseases (ICD). Annotating EMRs with ICD codes is important for medical billing and downstream epidemiological studies. However, manually annotating EMRs is both time-consuming and error prone.

Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces.

Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group), and labels that never appear in the training dataset (zero-shot group). Multi-label few- and zero-shot label prediction is mostly unexplored on datasets with large label spaces, especially for text classification. In this paper, we perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels.

Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task.

Objective: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.

Materials And Methods: We organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions.

EMR Coding with Semi-Parametric Multi-Head Matching Networks.

Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and mis-interpretation of a patient's well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility.

Extracting chemical-protein relations with ensembles of SVM and deep learning models.

Mining relations between chemicals and proteins from the biomedical literature is an increasingly important task. The CHEMPROT track at BioCreative VI aims to promote the development and evaluation of systems that can automatically detect the chemical-protein relations in running text (PubMed abstracts). This work describes our CHEMPROT track entry, which is an ensemble of three systems, including a support vector machine, a convolutional neural network, and a recurrent neural network.

Generalizing biomedical relation classification with neural adversarial domain adaptation.

Motivation: Creating large datasets for biomedical relation classification can be prohibitively expensive. While some datasets have been curated to extract protein-protein and drug-drug interactions (PPIs and DDIs) from text, we are also interested in other interactions including gene-disease and chemical-protein connections. Also, many biomedical researchers have begun to explore ternary relationships.

Isolated lower extremity vasculitis leading to progressive critical limb ischemia.

Giant cell arteritis is a large- and medium-vessel vasculitis that has been described as a systemic disease process with disseminated vessel involvement. Advances in vascular imaging techniques have demonstrated that involvement of the large vessels of the upper and lower limbs may be more prevalent than was once thought, although the clinical implications of this are unknown. Isolated lower extremity claudication without systemic or classic cranial symptoms, especially as a primary manifestation of giant cell arteritis, is rare.

A novel surgical approach to symptomatic left renal vein aneurysm.

Visceral venous aneurysms are uncommon and renal vein aneurysms are among the rarest in this subset. Renal vein aneurysms are frequently asymptomatic, but patients may present with flank pain or hematuria. Complications of untreated visceral venous aneurysms include thrombus formation and, very rarely, rupture.

Extracting Drug-Drug Interactions with Word and Character-Level Recurrent Neural Networks.

Drug-drug interactions (DDIs) are known to be responsible for nearly a third of all adverse drug reactions. Hence several current efforts focus on extracting signal from EMRs to prioritize DDIs that need further exploration. To this end, being able to extract explicit mentions of DDIs in free text narratives is an important task.

Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores.

Background: The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task.

Objective: Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification.

Case Report: An Innovative Endovascular Technique for Repair of Descending Thoracic Aortic Aneurysm following an Open Coarctation Repair.

It was once postulated that open surgical repair of coarctation of the aorta during childhood patients was cured. However, long-term follow-up has been significant for late problems such as an aneurysm. The incidence of such aneurysm after open surgical coarctation repair is 11-24%.

Laparoscopic Left Gastric Artery Aneurysm Resection.

Aneurysmal degeneration of the visceral branches of the abdominal aorta is a rare and potentially life-threatening disease entity. Visceral artery aneurysms (VAAs) are exceedingly rare and have a prevalence of 0.1 to 2%.

Transperitoneal Intervascular Approach for Repair of the Lumbar Spine.

This report describes a novel anterior surgical approach to the midlumbar spine. A transperitoneal dissection separating the tissue planes between the infrarenal vena cava and abdominal aorta allows for ample exposure in the reconstruction of midlumbar vertebral body fractures.

Analyzing the Moving Parts of a Large-Scale Multi-Label Text Classification Pipeline: Experiences in Indexing Biomedical Articles.

Medical subject headings (MeSH) is a controlled hierarchical vocabulary used by the National Library of Medicine (NLM) to index biomedical articles. In the 2014 version of MeSH terminology there are a total of 27,149 terms. Librarians at the NLM tag each biomedical article to be indexed for the PubMed literature search system with terms from MeSH.

Convolutional Neural Networks for Biomedical Text Classification: Application in Indexing Biomedical Articles.

Building high accuracy text classifiers is an important task in biomedicine given the wealth of information hidden in unstructured narratives such as research articles and clinical documents. Due to large feature spaces, traditionally, discriminative approaches such as logistic regression and support vector machines with n-gram and semantic features (e.g.

An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records.

Background: Diagnosis codes are assigned to medical records in healthcare facilities by trained coders by reviewing all physician authored documents associated with a patient's visit. This is a necessary and complex task involving coders adhering to coding guidelines and coding all assignable codes. With the popularity of electronic medical records (EMRs), computational approaches to code assignment have been proposed in the recent years.