AIMC Topic: Hospitals

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Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Clinical decision support assisted by prediction models usually faces the challenges of limited clinical data and a lack of labels when the model is developed with data from a single medical institution. Accordingly, researc...

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

The Lancet. Digital health
BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reaso...

Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study.

Journal of medical Internet research
BACKGROUND: Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lea...

Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study.

Journal of medical Internet research
BACKGROUND: Patients' choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital cho...

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals.

Nature communications
Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide trea...

Robot-assisted S2 screw fixation for posterior pelvic ring injury.

Injury
BACKGROUND: Percutaneous sacroiliac screw is one of the main methods to treat unstable posterior pelvic ring injury. However, complexity of pelvic anatomical structure increases the difficulty and risk with freehand operation. Besides, S2 screw fixat...

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, ...

Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital.

Asian Pacific journal of cancer prevention : APJCP
BACKGROUND: Acute Lymphoblastic Leukemia (ALL) is the most common blood disease in children and is responsible for the most deaths amongst children. Due to major improvements in the treatment protocols in the 50-years period, the survivability of thi...

Assessment of User Needs for Telemedicine Robots in a Developing Nation Hospital Setting.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
This study aimed to investigate the needs of medical users of telemedicine robots to encourage international cooperation and development. As the use of telemedicine expands, it is necessary to develop new systems, including robots, which consider t...

Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design.

Artificial intelligence in medicine
OBJECTIVE: Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address thi...