Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need.

Objective: To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.

Methods: We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion parameters. We adopted soft prompts (ie, trainable vectors) with frozen LLM, where the LLM parameters were not updated (ie, frozen) and only the vectors of soft prompts were updated, known as prompt tuning.

A study of generative large language model for medical research and healthcare.

There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text.

A large language model for electronic health records.

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain).

Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.

Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings.

Real-time seizure detection is a resource intensive process as it requires continuous monitoring of patients on stereoelectroencephalography. This study improves real-time seizure detection in drug resistant epilepsy (DRE) patients by developing patient-specific deep learning models that utilize a novel self-supervised dynamic thresholding approach. Deep neural networks were constructed on over 2000 h of high-resolution, multichannel SEEG and video recordings from 14 DRE patients.

Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach.

Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED).

Differential Subsampling with Cartesian Ordering for Ultrafast High-Resolution MRA in the Assessment of Intracranial Aneurysms.

Background And Purpose: We aimed to evaluate the feasibility of an ultrafast whole head contrast-enhanced MRA (CE-MRA) in morphometric assessment of intracranial aneurysms in comparison to routinely used time-of-flight (TOF)-MRA.

Methods: In this prospective single institutional study, patients with known untreated intracranial aneurysm underwent MRA. Routine multislab TOF-MRA was obtained with a 3D voxel sizes of .

A technical comparison of thrombectomy vacuum aspiration systems.

Introduction: Improved functional outcomes after mechanical thrombectomy for emergent large vessel occlusion depend on expedient reperfusion after clinical presentation. Device technology has improved substantially over the years, and several commercial options exist for both large-bore aspiration catheters and suction pump systems.

Objective: To compare various vacuum pumps and examine the aspiration forces they generate as well as the force of catheter tip detachment from an artificial thrombus.

An attention based deep learning model of clinical events in the intensive care unit.

This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocardial infarction (MI), and vancomycin antibiotic administration over two week patient ICU courses in the MIMIC-III dataset. These models achieved next-day predictive AUC of 0.876 for sepsis, 0.

Combination of Active Transfer Learning and Natural Language Processing to Improve Liver Volumetry Using Surrogate Metrics with Deep Learning.

Purpose: To determine if weakly supervised learning with surrogate metrics and active transfer learning can hasten clinical deployment of deep learning models.

Materials And Methods: By leveraging Liver Tumor Segmentation (LiTS) challenge 2017 public data ( = 131 studies), natural language processing of reports, and an active learning method, a model was trained to segment livers on 239 retrospectively collected portal venous phase abdominal CT studies obtained between January 1, 2014, and December 31, 2016. Absolute volume differences between predicted and originally reported liver volumes were used to guide active learning and assess accuracy.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.

Background: There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task.

Navigation-Linked Heads-Up Display in Intracranial Surgery: Early Experience.

Background: The use of intraoperative navigation during microscope cases can be limited when attention needs to be divided between the operative field and the navigation screens. Heads-up display (HUD), also referred to as augmented reality, permits visualization of navigation information during surgery workflow.

Objective: To detail our initial experience with HUD.

Extensivity and additivity of the Kolmogorov-Sinai entropy for simple fluids.

According to the van der Waals picture, attractive and repulsive forces play distinct roles in the structure of simple fluids. Here, we examine their roles in dynamics; specifically, in the degree of deterministic chaos using the Kolmogorov-Sinai (KS) entropy rate and the spectra of Lyapunov exponents. With computer simulations of three-dimensional Lennard-Jones and Weeks-Chandler-Andersen fluids, we find repulsive forces dictate these dynamical properties, with attractive forces reducing the KS entropy at a given thermodynamic state.

Neurosurgical Skills Assessment: Measuring Technical Proficiency in Neurosurgery Residents Through Intraoperative Video Evaluations.

Objectives: Although technical skills are fundamental in neurosurgery, there is little agreement on how to describe, measure, or compare skills among surgeons. The primary goal of this study was to develop a quantitative grading scale for technical surgical performance that distinguishes operator skill when graded by domain experts (residents, attendings, and nonsurgeons). Scores provided by raters should be highly reliable with respect to scores from other observers.

Operator experience determines performance in a simulated computer-based brain tumor resection task.

Purpose: Develop measures to differentiate between experienced and inexperienced neurosurgeons in a virtual reality brain surgery simulator environment.

Methods: Medical students (n = 71) and neurosurgery residents (n = 12) completed four simulated Glioblastoma multiforme resections. Simulated surgeries took place over four days with intermittent spacing in between (average time between surgeries of 4.

Relationship between dynamical entropy and energy dissipation far from thermodynamic equilibrium.

Connections between microscopic dynamical observables and macroscopic nonequilibrium (NE) properties have been pursued in statistical physics since Boltzmann, Gibbs, and Maxwell. The simulations we describe here establish a relationship between the Kolmogorov-Sinai entropy and the energy dissipated as heat from a NE system to its environment. First, we show that the Kolmogorov-Sinai or dynamical entropy can be separated into system and bath components and that the entropy of the system characterizes the dynamics of energy dissipation.

Data quality in tissue analysis using desorption electrospray ionization.

There has been a recent surge in applications of mass spectrometry (MS) to tissue analysis, particularly lipid-based tissue imaging using ambient ionization techniques. This recent growth highlights the need to examine the effects of sample handling, storage conditions, and experimental protocols on the quality of the data obtained. Variables such as time before freezing after organ removal, storage time at -80 °C, time stored at room temperature, heating, and freeze/thaw cycles were investigated for their effect on the data quality obtained in desorption electrospray ionization (DESI)-MS using mouse brain.

Direct detection of fatty acid ethyl esters using low temperature plasma (LTP) ambient ionization mass spectrometry for rapid bacterial differentiation.

Low temperature plasma mass spectrometry (LTP-MS) was employed to detect fatty acid ethyl esters (FAEE) from bacterial samples directly. Positive ion mode FAEE mass spectrometric profiles of sixteen different bacterial samples were obtained without extraction or other sample preparation. In the range m/z 200-300, LTP mass spectra show highly reproducible and characteristic patterns.

New ionization methods and miniature mass spectrometers for biomedicine: DESI imaging for cancer diagnostics and paper spray ionization for therapeutic drug monitoring.

The state-of-the-art in two new ambient ionization methods for mass spectrometry, desorption electrospray ionization (DESI) and paper spray (PS), is described and their utility is illustrated with new studies on tissue imaging and biofluid analysis. DESI is an ambient ionization method that can be performed on untreated histological sections of biological tissue in the open lab environment to image lipids, fatty acids, hormones and other compounds. Paper spray is performed in the open lab too; it involves electrospraying dry blood spots or biofluid deposits from a porous medium.

Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry.

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections.

Origin of chiral selectivity in gas-phase serine tetramers.

Serine "magic-number" clusters have attracted substantial experimental and theoretical interest since their discovery. Serine undergoes marked chiral enrichment upon sublimation, which has been associated with the homochiral selectivity of the octamer. This process has been implicated in one possible mechanism leading to the origin of biological homochirality.