Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning.

Background: Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease.

Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study.

Background: Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes.

Descriptive study of differences in acute kidney injury progression patterns in General and Cardiac Intensive Care Units.

Background: Acute kidney injury is common in critically ill patients with detrimental effects on mortality, length of stay and post-discharge outcomes. The Acute Kidney Injury Network developed guidelines based on urine output and serum creatinine to classify patients into stages of acute kidney injury.

Methods: In this analysis we utilize the Acute Kidney Injury Network guidelines to evaluate the acute kidney injury stage in patients admitted to general and cardiac intensive care units over a period of 18 months.

Methodologies for workforce optimization in Hospital's Emergency Department.

Nurse workforce optimization and scheduling in hospital units is a complex data science and operation research problem. Traditional manual estimation and preparation of nurse staffing and scheduling with the help of a subject matter expert might leads to over staffing or under staffing of different type of nurses such as core, float pool, overtime and agency, which impacts the patient care delivery and cost significantly. The situation becomes worse in case of emergency department as the patient head on bed occupancy is very dynamic in nature and many times the department might be overcrowded with no beds for the patients.