The objective of BioCreative8 Track 3 is to extract phenotypic key medical findings embedded within EHR texts and subsequently normalize these findings to their Human Phenotype Ontology (HPO) terms. However, the presence of diverse surface forms in phenotypic findings makes it challenging to accurately normalize them to the correct HPO terms. To address this challenge, we explored various models for named entity recognition and implemented data augmentation techniques such as synonym marginalization to enhance the normalization step. Our pipeline resulted in an exact extraction and normalization F1 score 2.6\% higher than the mean score of all submissions received in response to the challenge. Furthermore, in terms of the normalization F1 score, our approach surpassed the average performance by 1.9\%. These findings contribute to the advancement of automated medical data extraction and normalization techniques, showcasing potential pathways for future research and application in the biomedical domain.

This article describes the integration of a knowledge-based system with a large COBOL-DB2-based offender management system. The knowledge-based application, developed for the purpose of offender sentence calculation, is shown to provide several benefits, including a shortened development cycle, simplified maintenance, and improved accuracy over a previous COBOL-based application.

Parole board date order received three different parole dates. On the changes, probation judgments, and new laws earliest of these parole dates, he would be eligible and sentencing guidelines enacted each year for release from prison to serve the remainder by the state legislature also affect sentence calculations. of his sentence in the community. Finally, Because offenders are often sentenced because of overcrowding in the prison, Doe under multiple laws, these changes can received a safety valve date, which is a fraction create a complex equation for judges and of his time to serve until parole.