Hospital-Based Medicine

Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer.

BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations.

Using a Multi-Task Recurrent Neural Network With Attention Mechanisms to Predict Hospital Mortality of Patients.

Estimating hospital mortality of patients is important in assisting clinicians to make decisions and hospital providers to allocate resources. This paper proposed a multi-task recurrent neural network with attention mechanisms to predict patients' hospital mortality, using reconstruction of patients' physiological time series as an auxiliary task. Experiments were conducted on a large public elect...

Development and Prospective Validation of a Machine Learning-Based Risk of Readmission Model in a Large Military Hospital.

BACKGROUND: Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmi...

Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient ...

Implications of asymmetric neural activity patterns in the basal ganglia outflow in the integrative neural network model for cervical dystonia.

Cervical dystonia (CD) is characterized by abnormal twisting and turning of the head with associated head oscillations. It is the most common form of ...

Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua.

Free-text information is still widely used in emergency department (ED) records. Machine learning techniques are useful for analyzing narratives, but ...

Accuracy of American College of Surgeons National Surgical Quality Improvement Program Universal Surgical Risk Calculator in Predicting Complications Following Robot-Assisted Radical Cystectomy at a National Comprehensive Cancer Center.

There is paucity of literature about the validation of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) sur...

Machine Learning Readmission Risk Modeling: A Pediatric Case Study.

BACKGROUND: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency an...

Machine Learning Analysis of Matricellular Proteins and Clinical Variables for Early Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.

Although delayed cerebral ischemia (DCI) is a well-known complication after subarachnoid hemorrhage (SAH), there are no reliable biomarkers to predict...

Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques.

BACKGROUND/OBJECTIVE: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients during the first week of hospitalization o...

Deep-learning-based out-of-hospital cardiac arrest prognostic system to predict clinical outcomes.

AIM: Out-of-hospital cardiac arrest (OHCA) is a major healthcare burden, and prognosis is critical in decision-making for treatment and the withdrawal...

Using machine-learning methods to support health-care professionals in making admission decisions.

BACKGROUND: Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The...

Laparoscopic pediatric pyeloplasty using the Flexdex® articulating needle driver: step-by-step video.

Ureteropelvic junction (UPJ) stenosis is the most common cause of pathological neonatal hydronephrosis. UPJ obstruction may be treated conservatively ...

Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled...

Machine Learning Approach to find the relation between Endometriosis, benign breast disease, cystitis and non-toxic goiter.

The exact mechanism of endometriosis is unknown. The recommendation system (RS) based on item similarities of machine learning has never been applied ...

Feature-weighted survival learning machine for COPD failure prediction.

Chronic obstructive pulmonary disease (COPD) yields a high rate of failures such as hospital readmission and death in the United States, Canada and wo...

Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.

PURPOSE: We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for ...

Chief complaint classification with recurrent neural networks.

Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automate...

Early experience with Watson for oncology in Korean patients with colorectal cancer.

BACKGROUND: Watson for oncology (WFO) is a cognitive computing system providing decision support. We evaluated the concordance rates between the treat...

Artificial intelligence in breast imaging.

This article reviews current limitations and future opportunities for the application of computer-aided detection (CAD) systems and artificial intelli...