A5010368408- Heart Failure Treatment and Management
- Electrolyte and hormonal disorders
- Dialysis and Renal Disease Management
- Healthcare Technology and Patient Monitoring
- Artificial Intelligence in Healthcare
- Hemodynamic Monitoring and Therapy
- Nutrition and Health in Aging
- Blood Pressure and Hypertension Studies
- Liver Disease and Transplantation
- Context-Aware Activity Recognition Systems
- Non-Invasive Vital Sign Monitoring
- Congenital gastrointestinal and neural anomalies
- Abdominal vascular conditions and treatments
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Acute Ischemic Stroke Management
- Fibroblast Growth Factor Research
- Artificial Intelligence in Healthcare and Education
- Thyroid Disorders and Treatments
- Adrenal Hormones and Disorders
- Biomedical Research and Pathophysiology
- Renin-Angiotensin System Studies
- Cardiac Arrest and Resuscitation
- Intestinal Malrotation and Obstruction Disorders
- Parathyroid Disorders and Treatments
- Pediatric Hepatobiliary Diseases and Treatments
New York Institute of Technology
2024
Tamil Nadu Dr. M.G.R. Medical University
2020
Madras Medical College
2020
Yale University
2019
University of Amsterdam
2019
Amsterdam University Medical Centers
2019
Advocate Christ Medical Center
2012
Western General Hospital
1997
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based toolkit—to construct refine predictive models diagnosing diabetes mellitus forecasting hospital readmission rates. By analyzing a rich dataset featuring variety clinical demographic variables, we endeavored to identify patients at heightened risk complications leading readmissions. Our methodology incorporates...
BACKGROUND: Cardiovascular diseases remain a leading cause of global morbidity and mortality, with heart attacks strokes representing significant health challenges. The accurate, early diagnosis management these conditions are paramount in improving patient outcomes. specific disease, cardiovascular occlusions, has been chosen for the study due to impact it on public health. mortality globally, which blockages blood vessels, critical factor contributing conditions. OBJECTIVE: By focusing...
Coronary artery disease (CAD) is a prevailing global health issue and leading cause of death worldwide. Its accurate timely diagnosis crucial for effectively managing the improving patient outcomes. In this study, we conducted comparative analysis machine learning (ML)-based approaches to detect diagnose CAD. A dataset 918 instances from UCI ML repository, comprising 11 typical risk factors CAD predictors, was used investigation. The study deployed models in Google Colaboratory PyCaret,...
We present a family with three affected males in two generations congenital neurogenic chronic idiopathic intestinal pseudo-obstruction (CIIP), patent ductus arteriosus, and large platelet thrombocytopenia apparently segregating as an X linked recessive disorder. The pattern of segregation DNA markers within the is consistent linkage to previously described CIIP (CIIPX) locus at Xq28. This combination may represent new contiguous gene disorder appears have good prognosis supportive therapy.
This research presents an ensemble model developed to enhance the safety of bedridden patients in healthcare settings. The utilizes a unique dataset capturing six typical movements performed by laying bed, such as rolling over, falling off etc. aim is automate classification these movements, thereby enabling real-time monitoring without constant presence hospital personnel room, common practice current preprocessed and balanced using Synthetic Minority Over-sampling Technique, then split...
Thyroid hormone dysfunction is frequently observed in patients with chronic illnesses including heart failure, which increases the risk of adverse events. This study examined effects thyroid hormones (THs) on cardiac transverse-tubule (TT) integrity, Ca
This study introduces an ensemble model designed for real-time monitoring of bedridden patients.The was developed using a unique dataset, specifically acquired this study, that captures six typical movements. The dataset balanced the Synthetic Minority Over-sampling Technique, resulting in diverse distribution movement types. Three models were evaluated: Decision Tree Regressor, Gradient Boosting and Bagging Regressor. Regressor achieved accuracy 0.892 R2 score 1.0 on training 0.939 test...
This study introduces an ensemble model designed for real-time monitoring of bedridden patients. The was developed using a unique dataset, specifically acquired this study, that captures six typical movements. dataset balanced the Synthetic Minority Over-sampling Technique, resulting in diverse distribution movement types. Three models were evaluated: Decision Tree Regressor, Gradient Boosting and Bagging Regressor. Regressor achieved accuracy 0.892 R2 score 1.0 on training 0.939 test...
Introduction: Relative adrenal insufficiency is a feature of liver dysfunction where cortex cannot synthesize enough cortisol at times pathological stress and which may not manifest during normal periods.Aim: To assess the prevalence Adrenal Insufficiency (RAI) in stable cirrhotic ascites its relationship with further occurrence infective non-infective complications.Methodology: In this prospective study, out all patients presenting to our department, those SBP, HRS, jaundice, sepsis, ARDS,...