Biomedical subjects

S Walczak

Publications and source records attributed to S Walczak.

14 recordsLinked to original sources

PubMed2001

An artificial neural network approach to diagnosing epilepsy using lateralized bursts of theta EEGs.

Determining the cause of seizures is a significant medical problem, as misdiagnosis can result in increased morbidity and even mortality of patients. The reported research evaluates the efficacy of using an artificial neural network (ANN) for determining epileptic seizure occurrences for patients with lateralized bursts of theta (LBT) EEGs. Training and test cases are acquired from examining records of 1,500 consecutive adult seizure patients. The small resulting pool of 92 patients with LBT EEGs requires using a jack-knife procedure for developing the ANN categorization models. The ANNs are evaluated for accuracy, specificity, and sensitivity on classification of each patient into the correct two-group categorization: epileptic seizure or non-epileptic seizure. The original ANN model using eight variables produces a categorization accuracy of 62%. Following a modified factor analysis, an ANN model utilizing just four of the original variables achieves a categorization accuracy of 68%.

Diagnosis, Computer-Assisted↗

PubMed2000

Redesigning the medical office for improved efficiency: an object-oriented event-driven messaging system.

Medical practitioners are under ever increasing pressure to maximize patient care, while minimizing costs. One productivity area that has not previously undergone thorough investigation is the efficient utilization of time for intra-office communication. Medical office personnel typically need to communicate patient information and resource requests, as well as personal messages. An intra-office communication system is designed that reduces time-waste typically incurred in medical office environments. Redesigning medical offices with intra-office communication systems provides time savings of several man hours per day. The subsequent increase in time efficiency enables higher quality of patient care and larger patient loads to be managed by existing medical staff.

Dental Offices↗

PubMed2000

Predicting pediatric length of stay and acuity of care in the first ten minutes with artificial neural networks.

OBJECTIVE: To evaluate the efficacy of artificial neural networks in categorizing pediatric trauma patients into four distinct acuity of care groups and in determining the length of stay (LOS) within specific areas of the hospital. DESIGN: Using historical information from >8,000 pediatric trauma patient records, train and evaluate artificial neural networks to predict the injury severity and LOS for each patient in pediatric intensive care units (PICUs), step-down units, and floor units. Each artificial neural network is evaluated for categorization accuracy and mean absolute error difference on the predicted LOS. SUBJECTS: A total of 10,353 patient records from the National Pediatric Trauma registry, representing all pediatric trauma patients treated at affiliated hospitals from April 1994 through December 1996. Records with incomplete information were eliminated from the study, leaving 8,081 usable patient records. MEASUREMENTS: A total of 14 variables are selected from the 81 values present in the National Pediatric Trauma Registry as independent variables for the artificial neural networks. Each neural network produces nine output values: five for categorizing the patient's injury severity, three for the LOS in the PICU, step-down unit, and floor units, and one for the patient's total LOS. RESULTS: A fuzzy ARTMAP neural network accurately categorizes 88% of mortality patients and 58.3% of critical PICU patients. A backpropagation neural network succeeded in predicting the total LOS to within 1 day for 51.4% and the ICU LOS to within 1 day for 70.4% of all evaluated patients. CONCLUSION: Information available in the first 10 mins of a patient's presentation at the emergency room can be used by an artificial neural network to predict injury severity and LOS. Artificial neural networks enable more effective resource planning and patient management.

Journal Article↗

PubMed1998

Factors influencing stay in the postanesthesia care unit: a prospective analysis.

STUDY OBJECTIVE: To identify indicators of prolonged length of stay (LOS) in the postanesthesia care unit (PACU) and to test the following hypotheses: (1) that patient age, pain medication administration at the time of PACU admission, length of surgery, and cardiovascular, pulmonary, and pain responses postoperatively predict prolonged PACU LOS and (2) that cardiovascular and pulmonary symptoms preoperatively predict cardiovascular and pulmonary symptoms postoperatively. DESIGN: Prospective, observational analysis. SETTING: PACU of a university teaching hospital. PATIENTS: 1,067 patients scheduled for surgery with general anesthesia between February and September 1996, 18 years of age or older. MEASUREMENT AND MAIN RESULTS: 11.2% of the variation in prolonged PACU LOS can be predicted by age, pain medication at the time of PACU admission, and postoperative cardiovascular, pulmonary, and pain symptoms. A significant number of patients who did not report a prior history experienced postoperative cardiovascular and pulmonary symptoms. CONCLUSION: Patient history and postoperative symptoms predict only a small percentage of prolonged PACU stays. Organizational factors may be a more important predictor of prolonged PACU stay. Additionally, assessment of cardiovascular and pulmonary history needs refinement to improve prediction of patient responses postoperatively.

Adolescent↗

PubMed1998

Posttraumatic stress disorder in veterans with spinal cord injury: trauma-related risk factors.

Trauma-related risk factors for posttraumatic stress disorder (PTSD) were examined in a sample of 125 veterans with spinal cord injury. Category of injury was found to be the most consistent predictor of PTSD diagnosis and symptom severity with paraplegia predicting more PTSD symptoms than quadriplegia. The occurrence of a head injury at the time of the trauma was found to predict PTSD symptom severity measures, but not PTSD diagnosis. Trauma recency consistently predicted Impact of Event score (IES) and was found to be related to current PTSD severity and lifetime PTSD diagnosis in multiple but not simple regression models. Trauma severity was found to be significantly related to self-reported PTSD symptoms and lifetime PTSD diagnosis in simple but not in multiple regression analyses. Type of trauma, alcohol or other drug (AOD) use during the trauma and loss of consciousness (LOC) during the trauma were not consistently associated with PTSD symptom severity or diagnosis.

Adult↗

PubMed1998

A comparison of posttraumatic stress disorder in veterans with and without spinal cord injury.

The authors assessed effects of paraplegic and quadriplegic spinal cord injuries (SCIs) on posttraumatic stress disorder (PTSD) by comparing severity and prevalence of PTSD in these groups to a sample of controls who experienced traumatic injuries other than SCI. The authors found that veterans with quadriplegia reported significantly less severe current PTSD symptoms than controls who were not significantly different from veterans with paraplegia. These results suggest that sustaining a quadriplegic SCI decreases risk of current PTSD, whereas sustaining a paraplegic SCI is associated with greater risk of PTSD, although the risk is no greater than that incurred from experiencing the trauma itself.

Adult↗

PubMed1996