Using large language models for extracting stressful life events to assess their impact on preventive colon cancer screening adherence.

Background: Increase in early onset colorectal cancer makes adherence to screening a significant public health concern, with various social determinants playing a crucial role in its incidence, diagnosis, treatment, and outcomes. Stressful life events, such as divorce, marriage, or sudden loss of job, have a unique position among the social determinants of health.

Methods: We applied a large language model (LLM) to social history sections of clinical notes in the health records database of the Medical University of South Carolina to extract recent stressful life events and assess their impact on colorectal cancer screening adherence.

Estimating the Prevalence of Child Abuse and Neglect Among Adolescents in Primary Care Through Diagnoses Codes and Free-Text EHR Clinical Notes.

Introduction: Adolescents' child abuse and neglect experiences are often under-documented in primary care, leading to missed opportunities for interventions. This study compares the prevalence of child abuse and neglect cases identified by diagnostic codes versus a natural language processing approach of clinical notes.

Method: We retrospectively analyzed data from 8,157 adolescents, using ICD-10 codes and a natural language processing algorithm to identify child abuse and neglect cases and applied topic modeling on clinical notes to extract prevalent topics.

Enhancement of complement-dependent cytotoxicity by linking factor-H derived short consensus repeats 19-20 to CD20 antibodies.

Antibody-mediated complement-dependent cytotoxicity (CDC) on malignant cells is regulated by several complement control proteins, including the inhibitory complement factor H (fH). fH consists of 20 short consensus repeat elements (SCRs) with specific functional domains. Previous research revealed that the fH-derived SCRs 19-20 (SCR1920) can displace full-length fH on the surface of chronic lymphocytic leukemia (CLL) cells, which sensitizes CLL cells for e.

An Extensible Evaluation Framework Applied to Clinical Text Deidentification Natural Language Processing Tools: Multisystem and Multicorpus Study.

Background: Clinical natural language processing (NLP) researchers need access to directly comparable evaluation results for applications such as text deidentification across a range of corpus types and the means to easily test new systems or corpora within the same framework. Current systems, reported metrics, and the personally identifiable information (PII) categories evaluated are not easily comparable.

Objective: This study presents an open-source and extensible end-to-end framework for comparing clinical NLP system performance across corpora even when the annotation categories do not align.

High Accuracy Open-Source Clinical Data De-Identification: The CliniDeID Solution.

Clinical data de-identification offers patient data privacy protection and eases reuse of clinical data. As an open-source solution to de-identify unstructured clinical text with high accuracy, CliniDeID applies an ensemble method combining deep and shallow machine learning with rule-based algorithms. It reached high recall and precision when recently evaluated with a selection of clinical text corpora.

Initial development of tools to identify child abuse and neglect in pediatric primary care.

Background: Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warranted. Electronic health records (EHR) contain extensive information to support healthcare decisions, yet time constraints preclude most providers from thorough EHR reviews that could indicate CAN.

Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models.

Background: To advance new therapies into clinical care, clinical trials must recruit enough participants. Yet, many trials fail to do so, leading to delays, early trial termination, and wasted resources. Under-enrolling trials make it impossible to draw conclusions about the efficacy of new therapies.

Comparing Multiple Models for Section Header Classification with Feature Evaluation.

We present on the performance evaluation of machine learning (ML) and Natural Language Processing (NLP) based Section Header classification. The section headers classification task was performed as a two-pass system. The first pass detects a section header while the second pass classifies it.

A Natural Language Processing Tool Offering Data Extraction for COVID-19 Related Information (DECOVRI).

A new natural language processing (NLP) application for COVID-19 related information extraction from clinical text notes is being developed as part of our pandemic response efforts. This NLP application called DECOVRI (Data Extraction for COVID-19 Related Information) will be released as a free and open source tool to convert unstructured notes into structured data within an OMOP CDM-based ecosystem. The DECOVRI prototype is being continuously improved and will be released early (beta) and in a full version.

Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing.

Objective: The COVID-19 (coronavirus disease 2019) pandemic response at the Medical University of South Carolina included virtual care visits for patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The telehealth system used for these visits only exports a text note to integrate with the electronic health record, but structured and coded information about COVID-19 (eg, exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing.

Materials And Methods: To capture COVID-19 information from multiple sources, a new data mart and a new natural language processing (NLP) application prototype were developed.

The prospective GermanVasc cohort study.

Previous observational studies reported a wide variation and possible room for improvement in the treatment of patients suffering from symptomatic peripheral artery disease (PAD). Yet, systematic assessment of everyday clinical practice is lacking. A General Data Protection Regulation (GDPR) compliant registry was developed and used to collect comprehensive data on clinical treatment and outcomes regarding PAD in Germany.

Comparative Study of Various Approaches for Ensemble-based De-identification of Electronic Health Record Narratives.

De-identification of electric health record narratives is a fundamental task applying natural language processing to better protect patient information privacy. We explore different types of ensemble learning methods to improve clinical text de-identification. We present two ensemble-based approaches for combining multiple predictive models.

A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System.

Background: Family history information is important to assess the risk of inherited medical conditions. Natural language processing has the potential to extract this information from unstructured free-text notes to improve patient care and decision making. We describe the end-to-end information extraction system the Medical University of South Carolina team developed when participating in the 2019 National Natural Language Processing Clinical Challenge (n2c2)/Open Health Natural Language Processing (OHNLP) shared task.

A Comparative Analysis of Speed and Accuracy for Three Off-the-Shelf De-Identification Tools.

A growing quantity of health data is being stored in Electronic Health Records (EHR). The free-text section of these clinical notes contains important patient and treatment information for research but also contains Personally Identifiable Information (PII), which cannot be freely shared within the research community without compromising patient confidentiality and privacy rights. Significant work has been invested in investigating automated approaches to text de-identification, the process of removing or redacting PII.

An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.

Objective: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits.

Materials And Methods: After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms.

Automatic trial eligibility surveillance based on unstructured clinical data.

Introduction: Insufficient patient enrollment in clinical trials remains a serious and costly problem and is often considered the most critical issue to solve for the clinical trials community. In this project, we assessed the feasibility of automatically detecting a patient's eligibility for a sample of breast cancer clinical trials by mapping coded clinical trial eligibility criteria to the corresponding clinical information automatically extracted from text in the EHR.

Methods: Three open breast cancer clinical trials were selected by oncologists.

Patient-Pivoted Automated Trial Eligibility Pipeline: The First of Three Phases in a Modular Architecture.

Automated extraction of patient trial eligibility for clinical research studies can increase enrollment at a decreased time and money cost. We have developed a modular trial eligibility pipeline including patient-batched processing and an internal webservice backed by a uimaFIT pipeline as part of a multi-phase approach to include note-batched processing, the ability to query trials matching patients or patients matching trials, and an external alignment engine to connect patients to trials.

Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers.

Clinical text de-identification enables collaborative research while protecting patient privacy and confidentiality; however, concerns persist about the reduction in the utility of the de-identified text for information extraction and machine learning tasks. In the context of a deep learning experiment to detect altered mental status in emergency department provider notes, we tested several classifiers on clinical notes in their original form and on their automatically de-identified counterpart. We tested both traditional bag-of-words based machine learning models as well as word-embedding based deep learning models.

Ensemble-based Methods to Improve De-identification of Electronic Health Record Narratives.

Text de-identification is an application of clinical natural language processing that offers significant efficiency and scalability advantages. Hence, various learning algorithms have been applied to this task to yield better performance. Instead of choosing the best individual learning algorithm, we aim to improve de-identification by constructing ensembles that lead to more accurate classification.

Rapid flow-based peptide synthesis.

A flow-based solid-phase peptide synthesis methodology that enables the incorporation of an amino acid residue every 1.8 min under automatic control or every 3 min under manual control is described. This is accomplished by passing a stream of reagent through a heat exchanger into a low volume, low backpressure reaction vessel, and through a UV detector.

PET/CT imaging of integrin αvβ3 expression in human carotid atherosclerosis.

Objectives: The goal of this study was to evaluate the feasibility of [(18)F]Galacto-RGD positron emission tomography (PET)/computed tomography (CT) imaging of αvβ3 expression in human carotid plaques.

Background: The integrin αvβ3 is expressed by macrophages and angiogenic endothelial cells in atherosclerotic lesions and thus is a marker of plaque inflammation and, potentially, of plaque vulnerability. [(18)F]Galacto-RGD is a PET tracer binding specifically to αvβ3.

Ex vivo characterization of carotid plaques by intravascular ultrasonography and virtual histology: concordance with real plaque pathomorphology.

Background: The purpose of this study was to evaluate the accuracy of carotid plaque characterisation by virtual histology using intravascular ultrasonography (VH-IVUS) by comparing the results with real morphology.

Methods: Following elective carotid endarterectomy (CEA), atherosclerotic plaques from 36 patients (19 asymptomatic, 17 symptomatic) underwent ex-vivo VH-IVUS examination. Afterwards, tissue specimens were fixed with formalin and embedded in paraffin.

End-to-end continuous manufacturing of pharmaceuticals: integrated synthesis, purification, and final dosage formation.

A series of tubes: The continuous manufacture of a finished drug product starting from chemical intermediates is reported. The continuous pilot-scale plant used a novel route that incorporated many advantages of continuous-flow processes to produce active pharmaceutical ingredients and the drug product in one integrated system.

MRI plaque imaging detects carotid plaques with a high risk for future cerebrovascular events in asymptomatic patients.

Purpose: The aim of this study was to investigate prospectively whether MRI plaque imaging can identify patients with asymptomatic carotid artery stenosis who have an increased risk for future cerebral events. MRI plaque imaging allows categorization of carotid stenosis into different lesion types (I-VIII). Within these lesion types, lesion types IV-V and VI are regarded as rupture-prone plaques, whereas the other lesion types represent stable ones.