Hospitalists

Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction

Optimizing human-AI interaction requires users to reflect on their own performance critically. Our paper examines whether people using AI to complete tasks can accurately monitor how well they perform. In Study 1, participants (N = 246) used AI to solve 20 logical problems from the Law School Admission Test. While their task performance improved by three points compared to a norm population, par...

Automation of Trainable Datasets Generation for Medical-Specific Language Model: Using MIMIC-IV Discharge Notes.

This study introduces a novel approach for generating machine-generated instruction datasets for fine-tuning medical-specialized language models using MIMIC-IV discharge records. The study created a large-scale text dataset comprising instructions, cropped discharge notes as inputs, and outputs in JSONL format. The dataset was generated through three main stages, generating instruction and output ...

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging E...

SemOntoMap: A Hybrid Approach for Semantic Annotation of Clinical Texts.

This study addresses the challenge of leveraging free-text descriptions in Electronic Health Records (EHR) for clinical research and healthcare improv...

Optimizing ICU Care: Machine Learning and PCA for Early Prediction of Renal Replacement Therapy Requirement.

Forecasting the need for Renal Replacement Therapy (RRT) in intensive care units (ICUs) at an early stage can enhance patient outcomes and optimize re...

What Kind of Transformer Models to Use for the ICD-10 Codes Classification Task.

Coding according to the International Classification of Diseases (ICD)-10 and its clinical modifications (CM) is inherently complex and expensive. Nat...

The Application Artificial Intelligence-Assisted Robot System in Nursing Follow-up of Discharged Patients.

To construct a robot intelligent discharge follow-up platform and explore its application effects in clinical discharge follow-up scenarios Applying i...

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals,...

LightIED: Explainable AI with Light CNN for Interictal Epileptiform Discharge Detection.

Interictal epileptic discharge (IED) detection from electroencephalography (EEG) is an important but difficult step in the epilepsy diagnosis. To redu...

Evaluating LLMs for Diagnosis Summarization.

During a patient's hospitalization, extensive information is documented in clinical notes. The efficient summarization of this information is vital fo...

Interictal Epileptiform Discharge Detection Using Time-Frequency Analysis and Transfer Learning.

Interictal epileptiform discharges (IEDs) are electrophysiological events that intermittently occur in between seizures in Epilepsy patients. Automate...

Prediction of gait recovery using machine learning algorithms in patients with spinal cord injury.

With advances in artificial intelligence, machine learning (ML) has been widely applied to predict functional outcomes in clinical medicine. However, ...

Artificial intelligence: revolutionizing cardiology with large language models.

Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspecti...

Early Disease Prediction Using a Text-Numerical Hybrid Model Using Large-Scale Clinical Real-World Data.

To assist physicians in predicting diseases, most natural language processing (NLP) models have focused on progress notes in electronic medical record...

Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients.

BACKGROUND: Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This un...

[Modern opportunities of using computer programs and mobile devices in the frame of personality identification].

A technology of mobile devices on the basis of Android and iOS sharing, in which previously trained neural networks on the mobile device with the use ...

Optimal design of triangular side orifice using multi-objective optimization NSGA-II.

Triangular orifices are widely used in industrial and engineering applications, including fluid metering, flow control, and measurement. Predicting di...

[Deep Learning-Based Identification of Common Complication Features of Surgical Incisions].

OBJECTIVE: In recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-o...

Long-term SARS-CoV-2 neutralizing antibody level prediction using multimodal deep learning: A prospective cohort study on longitudinal data in Wuhan, China.

The ongoing epidemic of SARS-CoV-2 is taking a substantial financial and health toll on people worldwide. Assessing the level and duration of SARS-CoV...

A Deep Learning Framework for Image-Based Screening of Kawasaki Disease.

Kawasaki disease (KD) is a leading cause of acquired heart disease in children and is characterized by the presence of a combination of five clinical ...