Hospital-Based Medicine

Analysing repeated hospital readmissions using data mining techniques.

Few studies have examined how to identify future readmission of patients with a large number of repeat emergency department (ED) visits. We explore 30-day readmission risk prediction using Microsoft's AZURE machine learning software and compare five classification methods: Logistic Regression, Boosted Decision Trees (BDTs), Support Vector Machine (SVM), Bayes Point Machine (BPM), and Two-Class Neu...

Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models.

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_G...

Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes.

Postsurgical complications (PSCs) are known as a deviation from the normal postsurgical course and categorized by severity and treatment requirements....

Social robots to support children's well-being under medical treatment: A systematic state-of-the-art review.

Hospitalization is a stressful experience for children. Socially assistive robots (SARs), designed to interact with humans, might be a means to mitiga...

Using an artificial neural network to predict traumatic brain injury.

In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients wh...

Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume.

IMPORTANCE: Forecasting the volume of hospital discharges has important implications for resource allocation and represents an opportunity to improve ...

Low vitamin D at ICU admission is associated with cancer, infections, acute respiratory insufficiency, and liver failure.

OBJECTIVES: Vitamin D deficiency may be associated with comorbidities and poor prognosis. However, this association in patients in the intensive care ...

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm.

Prediction of sediment volume and sediment load is always one of the important issues for decision-makers of watershed basins. The present study inves...

Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances.

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the p...

SCREEN-DR: Collaborative platform for diabetic retinopathy.

BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes mellitus and can lead to irreversible...

Utilizing soft constraints to enhance medical relation extraction from the history of present illness in electronic medical records.

Relation extraction between medical concepts from electronic medical records has pervasive applications as well as significance. However, previous res...

Using Artificial Intelligence (Watson for Oncology) for Treatment Recommendations Amongst Chinese Patients with Lung Cancer: Feasibility Study.

BACKGROUND: Artificial intelligence (AI) is developing quickly in the medical field and can benefit both medical staff and patients. The clinical deci...

Identify and monitor clinical variation using machine intelligence: a pilot in colorectal surgery.

Standardized clinical pathways are useful tool to reduce variation in clinical management and may improve quality of care. However the evidence suppor...

The Combined Use of Transcranial Direct Current Stimulation and Robotic Therapy for the Upper Limb.

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of...

A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis.

INTRODUCTION: Sepsis is associated to a high mortality rate, and its severity must be evaluated quickly. The severity of illness scores used are inten...

Predicting the risk of acute care readmissions among rehabilitation inpatients: A machine learning approach.

INTRODUCTION: Readmission from inpatient rehabilitation facilities to acute care hospitals is a serious problem. This study aims to develop a predicti...

Comparison of MetaMap and cTAKES for entity extraction in clinical notes.

BACKGROUND: Clinical notes such as discharge summaries have a semi- or unstructured format. These documents contain information about diseases, treatm...

Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model.

BACKGROUND: Value-based and patient-specific care represent 2 critical areas of focus that have yet to be fully reconciled by today's bundled care mod...

Societal Issues Concerning the Application of Artificial Intelligence in Medicine.

BACKGROUND: Medicine is becoming an increasingly data-centred discipline and, beyond classical statistical approaches, artificial intelligence (AI) an...

Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients ar...