Hospital-Based Medicine

Drug discovery and drug marketing with the critical roles of modern administration.

Drug research and development is a long-term and complicated process with the involvement of multidisciplinary, multi-sector cooperation and regulations of the Food and Drug Administration (FDA). It is of high risk, high cost, high benefit and time-consuming. Therefore, the drug administration and management is extremely necessary and useful. We discussed the whole process including laboratory stu...

Machine learning modeling for predicting hospital readmission following lumbar laminectomy.

In BriefAuthors of this study analyzed hospital readmissions following laminectomy and developed predictive models to identify readmitted patients with an accuracy >95% when using all variables and >79% when using only predischarge variables. A model capable of predicting 40% of readmitted patients was created using only the variables known predischarge. This investigation is important in its prov...

FABLE: A Semi-Supervised Prescription Information Extraction System.

Prescription information is an important component of electronic health records (EHRs). This information contains detailed medication instructions tha...

Disease Trajectories and End-of-Life Care for Dementias: Latent Topic Modeling and Trend Analysis Using Clinical Notes.

Despite the increasing prevalence, growing costs, and high mortality of dementia in older adults in the U.S., little is known about the course of thes...

Do reductions in agricultural field drainage during the growing season impact bacterial densities and loads in small tile-fed watersheds?

Predicting bacterial levels in watersheds in response to agricultural beneficial management practices (BMPs) requires understanding the germane proces...

Robot-assisted intravertebral augmentation corrects local kyphosis more effectively than a conventional fluoroscopy-guided technique.

OBJECTIVEIntravertebral augmentation (IVA) is a reliable minimally invasive technique for treating Magerl type A vertebral body fractures. However, po...

Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.

BACKGROUND: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing il...

A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation.

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients followi...

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.

OBJECTIVES: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospi...

Hyponatremia Presenting with Recurrent Mania.

Primary psychogenic polydipsia (PPD) is a chronic, relapsing condition in which there is a disturbance in thirst control primarily due to an underlyin...

The feasibility of fingerstick blood collection for point-of-care HIV-1 viral load monitoring in rural Zambia.

Viral load monitoring for HIV treatment is recommended but not feasible in many settings. A point-of-care test using capillary blood would increase ac...

The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.

OBJECTIVE: To further develop and refine an Emergency Department (ED) in-patient admission prediction model using machine learning techniques.

A combined modelling of fuzzy logic and Time-Driven Activity-based Costing (TDABC) for hospital services costing under uncertainty.

Hospital traditional cost accounting systems have inherent limitations that restrict their usefulness for measuring the exact cost of healthcare servi...

A nurse-driven method for developing artificial intelligence in "smart" homes for aging-in-place.

OBJECTIVES: To offer practical guidance to nurse investigators interested in multidisciplinary research that includes assisting in the development of ...

Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals.

BACKGROUND: The importance of identifying and evaluating adverse drug reactions (ADRs) has been widely recognized. Many studies have developed algorit...

Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.

BACKGROUND: Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learnin...

Natural language generation for electronic health records.

One broad goal of biomedical informatics is to generate fully-synthetic, faithfully representative electronic health records (EHRs) to facilitate data...

Vitamin D in the ICU: More sun for critically ill adult patients?

Critical illness in patients is characterized by systemic inflammation and oxidative stress. Vitamin D has a myriad of biological functions relevant t...

Optimal intensive care outcome prediction over time using machine learning.

BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognosticati...

A conceptual framework for clinicians working with artificial intelligence and health-assistive Smart Homes.

The Smart Home designed to extend older adults independence is emerging as a clinical solution to the growing ageing population. Nurses will and shoul...