Practice Management

External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study.

Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI model using ICD-10 (International Classification of Diseases, Tenth Revision) codes with other clinical variables to predict in-hospital mortality among trauma patients from a nationwide database. This study aimed to externally validate the performan...

AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity.

BACKGROUND: Immunogenic cell death (ICD) inducers are often identified in phenotypic screening campaigns by the release or surface exposure of various danger-associated molecular patterns (DAMPs) from malignant cells. This study aimed to streamline the identification of ICD inducers by leveraging cellular morphological correlates of ICD, specifically the condensation of nucleoli (CON).

Intrinsic plasticity coding improved spiking actor network for reinforcement learning.

Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant succ...

An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study.

BACKGROUND: Rare diseases affect millions worldwide but sometimes face limited research focus individually due to low prevalence. Many rare diseases d...

Enhanced accuracy in first-spike coding using current-based adaptive LIF neuron.

First spike timings are crucial for decision-making in spiking neural networks (SNNs). A recently introduced first-spike (FS) coding method demonstrat...

Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization.

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are know...

The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model.

BACKGROUND: Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characteri...

Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database.

The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy,...

Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study.

BACKGROUND: The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disea...

Extracting International Classification of Diseases Codes from Clinical Documentation Using Large Language Models.

BACKGROUND:  Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However, their performance in...

Using machine learning to identify pediatric ophthalmologists.

This cross-sectional study used data from the American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) and machine learning alg...

Toward trustable use of machine learning models of variant effects in the clinic.

There has been considerable progress in building models to predict the effect of missense substitutions in protein-coding genes, fueled in large part ...

Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning.

Inspired by the brain's information processing using binary spikes, spiking neural networks (SNNs) offer significant reductions in energy consumption ...

Predictors for improvement in personality functioning during outpatient psychotherapy: A machine learning approach within a psychodynamic psychotherapy sample.

BACKGROUND: Since its introduction in the diagnostic manuals DSM-5 and ICD-11, the construct of personality functioning has gained increasing attentio...

Comparison of deep learning approaches to estimate injury severity from the International Classification of Diseases codes.

OBJECTIVE: The injury severity classification based on the Abbreviated Injury Scale (AIS) provides information that allows for standardized comparison...

Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals.

Coronary artery disease (CAD) is an irreversible and fatal disease. It necessitates timely and precise diagnosis to slow CAD progression. Electrocardi...

Sparse Coding Inspired LSTM and Self-Attention Integration for Medical Image Segmentation.

Accurate and automatic segmentation of medical images plays an essential role in clinical diagnosis and analysis. It has been established that integra...

Large Language Models can Help with Biostatistics and Coding Needed in Radiology Research.

INTRODUCTION: Original research in radiology often involves handling large datasets, data manipulation, statistical tests, and coding. Recent studies ...

Identification of cultural conversations in therapy using natural language processing models.

Researchers have historically focused on understanding therapist multicultural competency and orientation through client self-report measures and beha...

Data flow in clinical laboratories: could metadata and peridata bridge the gap to new AI-based applications?

In the last decades, clinical laboratories have significantly advanced their technological capabilities, through the use of interconnected systems and...