AI Medical Compendium Topic

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Hospitals

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Artificial intelligence guided predicting the length of hospital-stay in a neurosurgical hospital based on the text data of electronic medical records.

Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko
BACKGROUND: Rational use of internal resources of hospitals including bed fund turnover is important objective in high-tech medicine. Machine learning technologies can improve neurosurgical care and contribute to patient-oriented approach.

Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

Critical care medicine
OBJECTIVES: Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a to...

How Socio-Technical Factors Can Undermine Expectations of Human-Robot Cooperation in Hospitals.

Studies in health technology and informatics
This research analysed human-robot cooperation and interaction in the basement of a Danish hospital, where kitchen staff and porters conducted their daily routines in an environment shared with mobile service robots. The robots were installed to ease...

The Cost of Robot-assisted Total Hip Arthroplasty: Comparing Safety and Hospital Charges to Conventional Total Hip Arthroplasty.

The Journal of the American Academy of Orthopaedic Surgeons
INTRODUCTION: Utilization of robotic assistance is increasing for total hip arthroplasty (THA). However, few studies have directly examined the efficacy of this technique at reducing complications. This research aims to compare the rates of periopera...

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patien...

Categorising patient concerns using natural language processing techniques.

BMJ health & care informatics
OBJECTIVES: Patient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the result...

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

International journal of epidemiology
BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection ...

Design of 1-year mortality forecast at hospital admission: A machine learning approach.

Health informatics journal
Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-y...

[Launching Robot-Assisted Laparoscopic Surgery for Rectal Cancer in Our Hospital-Short-Term Results].

Gan to kagaku ryoho. Cancer & chemotherapy
Robot-assisted laparoscopic surgery(RALS)for rectal cancer has been covered by National Health Insurance in Japan since April 2018. We launched RALS in our hospital in October 2019 and now report the short-term results(up to January 2020). Altogether...