Hospital-Based Medicine

Development of a Complication- and Treatment-Aware Prediction Model for Favorable Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning.

BACKGROUND: Current prognostic tools in aneurysmal subarachnoid hemorrhage (aSAH) are constrained by being primarily based on patient and disease characteristics on admission.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-inf...

Drivers of Prolonged Hospitalization Following Spine Surgery: A Game-Theory-Based Approach to Explaining Machine Learning Models.

BACKGROUND: Understanding the interactions between variables that predict prolonged hospital length of stay (LOS) following spine surgery can help unc...

Patient satisfaction with a pharmacist-led best possible medication discharge plan via tele-robot in a remote and rural community hospital.

INTRODUCTION: Medication reconciliation (MedRec) reduces the risk of preventable medication-related adverse events (ADEs). A best possible medication ...

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.

BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidiscipl...

Design of 1-year mortality forecast at hospital admission: A machine learning approach.

Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life ...

Artificial intelligence in pancreaticobiliary endoscopy.

Artificial intelligence (AI) applications in health care have exponentially increased in recent years, and a few of these are related to pancreatobili...

A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients.

Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT ris...

Optimization of Genomic Classifiers for Clinical Deployment: Evaluation of Bayesian Optimization to Select Predictive Models of Acute Infection and In-Hospital Mortality.

Acute infection, if not rapidly and accurately detected, can lead to sepsis, organ failure and even death. Current detection of acute infection as wel...

Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models.

Artificial intelligence (AI) is the development of computer systems that normally require human intelligence. In the field of acute kidney injury (AKI...

Cytomegalovirus and Severe Acute Respiratory Syndrome Coronavirus 2 Co-infection in Renal Transplants: A Retrospective Study from a Single Center.

There is a scarcity of data regarding the impact of cytomegalovirus (CMV) infection complicating the coronavirus disease-2019 (COVID-19) course. The o...

Machine-learning prediction of unplanned 30-day rehospitalization using the French hospital medico-administrative database.

Predicting unplanned rehospitalizations has traditionally employed logistic regression models. Machine learning (ML) methods have been introduced in h...

Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.

OBJECTIVE: To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery ...

Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review.

PURPOSE OF REVIEW: Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associate...

[Launching Robot-Assisted Laparoscopic Surgery for Rectal Cancer in Our Hospital-Short-Term Results].

Robot-assisted laparoscopic surgery(RALS)for rectal cancer has been covered by National Health Insurance in Japan since April 2018. We launched RALS i...

Patient Coded Severity and Payment Penalties Under the Hospital Readmissions Reduction Program: A Machine Learning Approach.

OBJECTIVE: The objective of this study was to examine variation in hospital responses to the Centers for Medicare and Medicaid's expansion of allowabl...

Feasibility of machine learning based predictive modelling of postoperative hyponatremia after pituitary surgery.

PURPOSE: Hyponatremia after pituitary surgery is a frequent finding with potential severe complications and the most common cause for readmission. Sev...

The toll of noninfected CRS patients to the COVID-19 pandemic.

Social distancing with the aim of avoiding infections and pre-serve critical care capacities during the COVID-19 pandemic has been implemented in Germ...

[Clinical analysis of robot-assisted laparoscopic transabdominal preperitoneal inguinal hernia repair].

To explore the clinical value of robot-assisted laparoscopic transabdominal preperitoneal (TAPP) inguinal hernia repair. We performed a retrospectiv...

Machine Learning With Feature Domains Elucidates Candidate Drivers of Hospital Readmission Following Spine Surgery in a Large Single-Center Patient Cohort.

BACKGROUND: Unplanned hospital readmissions constitute a significant cost burden in healthcare. Identifying factors contributing to readmission risk p...