Hospitalists

Development of a Risk Prediction Model for Linezolid-Induced Thrombocytopenia Based on the Machine Learning Algorithm.

The research aimed to develop a validated model for predicting the risk of linezolid-induced thrombocytopenia (LIT). An XGBoost model and SelectFromModel method were used to screen the important factors. Based on the selected features, five models-Logistic Regression, XGBoost, Random Forest, Naive Bayes, and Support Vector Machine-were established. Finally, the model results were interpreted using...

Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study.

BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient conditions. This study aimed to develop and externally validate machine learning models for predicting malnutrition within 24 h of intensive care unit (...

Clinical decision support using pseudo-notes from multiple streams of EHR data.

Electronic health records (EHR) contain data from disparate sources, spanning various biological and temporal scales. In this work, we introduce the M...

Machine learning based prediction modeling of micro-EDM of Ti-29Nb-13Ta-4.6Zr (TNTZ).

Although Ti-6Al-4V stands out as one of the best in biomedical, automotive, and aerospace applications due to its low density and higher corrosion res...

Machine learning to improve predictive performance of prehospital early warning scores.

Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding crit...

[Early warning scores: a rapid umbrella review].

BACKGROUND: Early warning scores (EWS) are used for monitoring and evaluating vital signs in hospitalized patients. With EWS, escalating measures for ...

Child and Family Outcomes After PICU Admission: Creation of an Open Access Literature Database Using a Global Team of PICU Specialists Integrated With Machine Learning.

OBJECTIVES: As research examining child health outcomes after PICU admission grows, so does the need for the identification and synthesis of a large b...

Comprehensive predictive modeling in subarachnoid hemorrhage: integrating radiomics and clinical variables.

Subarachnoid hemorrhage (SAH) is a severe condition with high morbidity and long-term neurological consequences. Radiomics, by extracting quantitative...

Use of childhood adversity and mental health admission patterns to predict suicide in young people.

Dougall et al found that mental health admissions are a strong predictor of suicide risk in young people. The findings can improve machine learning mo...

Predicting involuntary admission following inpatient psychiatric treatment using machine learning trained on electronic health record data - CORRIGENDUM.

Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT.

BACKGROUND AND PURPOSE: Robustness against input data perturbations is essential for deploying deep learning models in clinical practice. Adversarial ...

MPSE identifies newborns for whole genome sequencing within 48 h of NICU admission.

Identifying critically ill newborns who will benefit from whole genome sequencing (WGS) is difficult and time-consuming due to complex eligibility cri...

Machine Learning-Assisted Spray Pyrolysis for the Synthesis of Single-Atom Fe-N-C Porous Hollow Microspheres for Zinc-Air Batteries.

Non-noble metal single-atom catalysts with high catalytic activity have garnered considerable attention from researchers in recent years. Yet, their s...

Clinical phenotypes and risk of early hemodynamic deterioration in intermediate-high-risk patients with acute pulmonary embolism.

INTRODUCTION: Intermediate-high-risk pulmonary embolism (PE) patients face elevated risks of sudden clinical deterioration in early hours after sympto...

A Mortality Risk Prediction Model for Septic Shock in Patients Aged ≥50: Role of Norepinephrine Index and Procalcitonin.

BACKGROUND: Septic shock is a high-mortality syndrome, particularly in patients aged 50 and older. Predicting mortality in this population is challeng...

Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability.

The prevalence and spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global public health problem. This study aims to identify the ris...

Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury.

Severe acute kidney injury (sAKI) is a prevalent and serious complication among patients with sepsis-induced myocardial injury (SIMI). Prompt and earl...

Ethical Considerations for Generative Artificial Intelligence in Plastic Surgery.

The integration of artificial intelligence (AI) into surgical care is rapidly transforming healthcare by enhancing efficiency, clinical decision-makin...

A Novel and Modern Calculator to Predict Vaginal Birth after Cesarean Delivery.

Counseling patients who are considering a trial of labor after cesarean (TOLAC) is a challenging task given the risks and benefits of either approach....

Large language model discharge summary preparation using real-world electronic medical record data shows promise.

The efficacy of large language models (LLMs) in discharge summary preparation using real clinical documentation remains novel. Our study aimed to test...