An unstructured model is presented to describe growth of mycelial pellets in submerged cultures. This model integrates growth kinetics at the scale of the hyphae with the physical mechanisms of mass-transfer processes at the scale of the pellets and the fermentor. The main elements of the model are biomass, substrate, and oxygen balances for the liquid phase and the pellets. The possible occurrence of oxygen limitation in the pellets is introduced in analogy with catalyst theories by means of an effectiveness factor. To simulate the growth of pellets the model is transferred into a computer program. The model is tested by means of fermentation experiments in a bubble column. Results of the growth experiments compare favorably with the outcome of computer simulations.
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http://dx.doi.org/10.1002/bit.260240115 | DOI Listing |
Front Artif Intell
January 2025
Language Intelligence and Information Retrieval (LIIR) Lab, Department of Computer Science, KU Leuven, Leuven, Belgium.
The digitization of healthcare records has revolutionized medical research and patient care, with electronic health records (EHRs) containing a wealth of structured and unstructured data. Extracting valuable information from unstructured clinical text presents a significant challenge, necessitating automated tools for efficient data mining. Natural language processing (NLP) methods have been pivotal in this endeavor, aiming to extract crucial clinical concepts embedded within free-form text.
View Article and Find Full Text PDFBMC Surg
January 2025
Department of Statistics, Debremarkos University, Debremarkos, Ethiopia.
Introduction: Post-surgical recovery time is influenced by various factors, including patient demographics, surgical details, pre-existing conditions, post-operative care, and socioeconomic status. Understanding these dynamics is crucial for improving patient outcomes. This study aims to identify significant predictors of post-surgical recovery time in a resource-limited Ethiopian hospital setting and to evaluate the variability attributable to individual patient differences and surgical team variations.
View Article and Find Full Text PDFBMC Med Res Methodol
January 2025
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030-R, Boston, MA, 02120, USA.
Background: A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an "out of the box" implementation of open-source large language models (LLMs) without any fine-tuning can accurately extract social determinants of health (SDoH) data from free-text clinical notes.
Methods: We conducted a cross-sectional study using EHR data from the Mass General Brigham (MGB) system, analyzing free-text notes for SDoH information.
Identifying immunosuppressed patients using structured data can be challenging. Large language models effectively extract structured concepts from unstructured clinical text. Here we show that GPT-4o outperforms traditional approaches in identifying immunosuppressive conditions and medication use by processing hospital admission notes.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States.
Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.
Objective: This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).
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