AIMC Topic: Quality Improvement

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Artificial intelligence outperforms human students in conducting neurosurgical audits.

Clinical neurology and neurosurgery
OBJECTIVES: Neurosurgical audits are an important part of improving the safety, efficiency and quality of care but require considerable resources, time, and funding. To that end, the advent of the Artificial Intelligence-based algorithms offered a no...

[Methods of artificial intelligence and their application in imaging diagnostics].

Magyar onkologia
Artificial intelligence is a dynamically evolving methodology and, due to its large number of methods, its appearance becomes more important not only in industry but also in all disciplines. Diagnostic instrument manufacturers have realized relativel...

Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

Artificial intelligence in medicine
After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical p...

Pelvic lymph node dissection at robot-assisted radical prostatectomy: Assessing utilization and nodal metastases within a statewide quality improvement consortium.

Urologic oncology
PURPOSE: Several guidelines recommend pelvic lymph node dissection (PLND) at robot-assisted radical prostatectomy (RARP) only when lymph node involvement (LN+) is >2%. Individual surgeon use of PLND is not well-known. We sought to examine variability...

De novo Nanopore read quality improvement using deep learning.

BMC bioinformatics
BACKGROUND: Long read sequencing technologies such as Oxford Nanopore can greatly decrease the complexity of de novo genome assembly and large structural variation identification. Currently Nanopore reads have high error rates, and the errors often c...

Identification of postoperative complications using electronic health record data and machine learning.

American journal of surgery
BACKGROUND: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for ...

Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces.

International journal of medical informatics
OBJECTIVES: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED).