Articles and Recent News
Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images
Burn is one of the serious public health problems. Usually, burn diagnoses are based on expert medical and clinical experience and it is necessary to have a medical or clinical expert to conduct an examination in restorative clinics or at emergency rooms in hospitals. But sometimes a patient may have a burn where there is no specialized facility available, and in such a case a computerized automatic burn assessment tool may aid diagnosis.
Defining the Relationship Between Compressive Stress and Tissue Trauma During Laparoscopic Surgery Using Human Large Intestine
Excessive magnitudes of compressive stress exerted on gastrointestinal tissues can lead to pathological scar tissue or adhesion formation, bleeding, inflammation or even death from bowel perforation and sepsis. It is currently unknown however, at exactly what magnitude of compressive stress that these pathologies occur.
Prediction of Recovery from Severe Hemorrhagic Shock Using Logistic Regression
Objective: This study implements logistic regression models (LRMs) and feature selection for creating a predictive model for recovery form hemorrhagic shock (HS) with resuscitation using blood in multiple experimental rat animal protocols
Non-contact Early Warning of Shaking Palsy
Objective: Parkinsonian gait is a defining feature of Shaking Palsy (SP) and it has one of the worse impact on human healthy life than other SP symptoms. The objective of this work is to propose a Parkinsonian gait detection system based on an S-band perception technique to classify abnormal gait and normal walking.
Reduced Rank Least Squares for Real-Time Short Term Estimation of Mean Arterial Blood Pressure in Septic Patients Receiving Norepinephrine
Norepinephrine (NE), an endogenous catecholamine, is a mainstay treatment for septic shock, which is a life-threatening manifestation of severe infection. NE counteracts the loss in blood pressure associated with septic shock.
The Conceptual Design of a Novel Workstation for Seizure Prediction using Machine Learning with Potential eHealth Applications
Recent attempts to predict refractory epileptic seizures using machine learning algorithms to process electroencephalograms (EEGs) have shown great promise. However, research in this area requires a specialized workstation. Commercial solutions are unsustainably expensive, can be unavailable in most countries, and are not designed specifically for seizure prediction research.