AIMC Topic: Female

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Clinical observation of the efficacy of low-molecular-weight heparin calcium in prophylaxis of the deep venous thrombosis following the gynecological tumor surgery.

Pakistan journal of pharmaceutical sciences
Present study is conducted to investigate the efficacy and safety of application of low-molecular-weight heparin calcium in the prophylaxis of deep venous thrombosis (DVT) following the laparoscopic surgery for gynecological tumors, so as to provide ...

Detecting central hypovolemia in simulated hypovolemic shock by automated feature extraction with principal component analysis.

Physiological reports
Assessment of the volume status by blood pressure (BP) monitoring is difficult, since baroreflex control of BP makes it insensitive to blood loss up to about one liter. We hypothesized that a machine learning model recognizes the progression of centr...

Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders.

Neurosurgical focus
OBJECTIVEIf not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postope...

Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning.

Neurosurgical focus
OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD trea...

A machine learning approach to predict early outcomes after pituitary adenoma surgery.

Neurosurgical focus
OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when usin...

Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging.

Neurosurgical focus
OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression ar...

Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury.

Neurosurgical focus
OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim...

Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study.

Neurosurgical focus
OBJECTIVEGross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate ca...

Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Hospital-acquired pressure injuries are a serious problem among critical care patients. Some can be prevented by using measures such as specialty beds, which are not feasible for every patient because of costs. However, decisions about wh...