Publications by authors named "Joseph Sirrianni"

Introduction: Pediatric sleep problems can be detected across racial/ethnic subpopulations in primary care settings. However, the electronic health record (EHR) data documentation that describes patients' sleep problems may be inherently biased due to both historical biases and informed presence. This study assessed racial/ethnic differences in natural language processing (NLP) training data (e.

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Objective: We present a proof-of-concept digital scribe system as an Emergency Department (ED) consultation call-based clinical conversation summarization pipeline to support clinical documentation, and report its performance.

Materials And Methods: We use four pre-trained large language models to establish the digital scribe system: T5-small, T5-base, PEGASUS-PubMed, and BART-Large-CNN via zero-shot and fine-tuning approaches. Our dataset includes 100 referral conversations among ED clinicians and medical records.

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The transition from pediatric to adult health care is a vulnerable time period for autistic adolescents and young adults (AYA) and for some autistic AYA may include a period of receiving care in both the pediatric and adult health systems. We sought to assess the proportion of autistic AYA who continued to use pediatric health services after their first adult primary care appointment and to identify factors associated with continued pediatric contact. We analyzed electronic medical record (EMR) data from a cohort of autistic AYA seen in a primary-care-based program for autistic people.

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Objective: We present a proof-of-concept digital scribe system as an ED clinical conversation summarization pipeline and report its performance.

Materials And Methods: We use four pre-trained large language models to establish the digital scribe system: T5-small, T5-base, PEGASUS-PubMed, and BART-Large-CNN via zero-shot and fine-tuning approaches. Our dataset includes 100 referral conversations among ED clinicians and medical records.

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Background: The transition from pediatric to adult care is a challenge for autistic adolescents and young adults. Data on patient features associated with timely transfer between pediatric and adult health care are limited. Our objective was to describe the patient features associated with timely transfer to adult health care (defined as View Article and Find Full Text PDF

Background: Generative pretrained transformer (GPT) models are one of the latest large pretrained natural language processing models that enables model training with limited datasets and reduces dependency on large datasets, which are scarce and costly to establish and maintain. There is a rising interest to explore the use of GPT models in health care.

Objective: We investigate the performance of GPT-2 and GPT-Neo models for medical text prediction using 374,787 free-text dental notes.

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Generative pretrained transformer models have been popular recently due to their enhanced capabilities and performance. In contrast to many existing artificial intelligence models, generative pretrained transformer models can perform with very limited training data. Generative pretrained transformer 3 (GPT-3) is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts.

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