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.