Lincoln Hospital

About: Lincoln Hospital is a healthcare organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Emergency department. The organization has 1033 authors who have published 929 publications receiving 14486 citations. The organization is also known as: Lincoln Medical and Mental Health Center & Lincoln Hospital.

Spontaneous uterine rupture as an unusual cause of abdominal pain in the early second trimester of pregnancy.

Abstract: A case of placenta percreta causing spontaneous uterine rupture is presented. This is a rare condition, which may present in the antepartum period as abdominal pain, with or without signs of hemorrhagic shock. This entity can lead to significant morbidity and mortality if not aggressively managed. A discussion follows on the pathophysiology, incidence, risk factors, presentation and management of this condition.

Ecology of dermatophyte infections in South Bronx, New York, 1969 to 1981

Abstract: This survey describes the ecology of superficial dermatophyte infections in South Bronx, New York from 1969 to 1981. The predominant species were Trichophyton rubrum (Castellani) Sabouraud, 1911 (57.5%), followed by Trichophyton tonsurans Malmsten, 1845 (18.5%), Trichophyton mentagrophytes (Robin) Blanchard, 1986 (11.5%), Microsporum canis Bodin var. canis Matsumoto, Padhye, and Ajello, 1902 (5%), Epidermophylon floccosum (Harz) Langeron and Milochevitch, 1930 (3.9%), and M. audouinii Gruby , 1843 (2.8%).

A retrospective analysis of the utility of an artificial neural network to predict ED volume.

Abstract: Objective The objectives of this study are to design an artificial neural network (ANN) and to test it retrospectively to determine if it may be used to predict emergency department (ED) volume. Methods We conducted a retrospective review of patient registry data from February 4, 2007, to December 31, 2009, from an inner city, tertiary care hospital. We harvested data regarding weather, days of week, air quality, and special events to train the ANN. The ANN belongs to a class of neural networks called multilayer perceptrons. We designed an ANN composed of 37 input neurons, 22 hidden neurons, and 1 output neuron designed to predict the daily number of ED visits. The training method is a supervised backpropagation algorithm that uses mean squared error to minimize the average squared error between the ANN's output and the number of ED visits over all the example pairs. Results A linear regression between the predicted and actual ED visits demonstrated an R 2 of 0.957 with a slope of 0.997. Ninety-five percent of the time, the ANN was within 20 visits. Conclusion The results of this study show that a properly designed ANN is an effective tool that may be used to predict ED volume. The scatterplot demonstrates that the ANN is least predictive at the extreme ends of the spectrum suggesting that the ANN may be missing important variables. A properly calibrated ANN may have the potential to allow ED administrators to staff their units more appropriately in an effort to reduce patient wait times, decrease ED physician burnout rates, and increase the ability of caregivers to provide quality patient care. A prospective is needed to validate the utility of the ANN.