Hospitalists

The impact of extraneous features on the performance of recurrent neural network models in clinical tasks.

Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables or features are useful in predicting clinical outcomes can be challenging. Advanced algorithms, such as deep neural networks, were designed to process high-dimensional inputs containing variables in their measured for...

Implementation of machine learning algorithms to create diabetic patient re-admission profiles.

BACKGROUND: Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today's computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business.

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.

IMPORTANCE: Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate ...

Meteorological factors for subarachnoid hemorrhage in the greater Düsseldorf area revisited: a machine learning approach to predict the probability of admission of patients with subarachnoid hemorrhage.

BACKGROUND: Reported data regarding the relation between the incidence of spontaneous subarachnoid hemorrhage (SAH) and weather conditions are conflic...

Finding relevant free-text radiology reports at scale with IBM Watson Content Analytics: a feasibility study in the UK NHS.

BACKGROUND: Significant amounts of health data are stored as free-text within clinical reports, letters, discharge summaries and notes. Busy clinician...

Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease.

While machine learning approaches can enhance prediction ability, little is known about their ability to predict 30-day readmission after hospitalizat...

Predicting rehospitalization within 2 years of initial patient admission for a major depressive episode: a multimodal machine learning approach.

Machine learning methods show promise to translate univariate biomarker findings into clinically useful multivariate decision support systems. At curr...

Analysis of risk factor domains in psychosis patient health records.

BACKGROUND: Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problemati...

Leveraging Semantics in WordNet to Facilitate the Computer-Assisted Coding of ICD-11.

The International Classification of Diseases (ICD) not only serves as the bedrock for health statistics but also provides a holistic overview of every...

Association between Blood Pressure after Haemodynamic Resuscitation in the Prehospital Setting and 28-Day Mortality in Septic Shock.

OBJECTIVE: Septic shock results in a decreased blood pressure (BP) leading to organ failure. The haemodynamic resuscitation aims at restoring the BP t...

Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic str...

Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection.

Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where epileptic seizure can lead to life-threatening emergencies. By...

Prediction model for the water jet falling point in fire extinguishing based on a GA-BP neural network.

Past research on the process of extinguishing a fire typically used a traditional linear water jet falling point model and the results ignored externa...

Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries.

*: Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language p...

Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach.

BACKGROUND: Severe traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of a...

Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health ...

Readmission prediction using deep learning on electronic health records.

Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. I...

Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data.

OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this s...

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters.

BACKGROUND: Hemodialysis mainly relies on the "artificial kidney," which plays a very important role in temporarily or permanently substituting for th...

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data.

BACKGROUND: The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfe...