AIMC Topic: Treatment Outcome

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Operative and long-term oncological outcomes in patients undergoing robotic versus laparoscopic surgery for rectal cancer.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This study aimed to compare short- and long-term outcomes after robotic versus laparoscopic approach in patients undergoing curative surgery for rectal cancer.

Open versus robot-assisted partial nephrectomy: A longitudinal comparison of 880 patients over 10 years.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Most comparisons between robot-assisted partial nephrectomy (RAPN) and open partial nephrectomy (OPN) indicate the superiority of RAPN, but the learning curve is often not considered.

Artificial intelligence in COVID-19 drug repurposing.

The Lancet. Digital health
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timel...

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.

BMC medical genomics
BACKGROUND: Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn't allow sufficient training of ML classifiers that could be used f...

Urological and sexual function after robotic and laparoscopic surgery for rectal cancer: A systematic review, meta-analysis and meta-regression.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This systematic review sought to compare the urogenital functions after laparoscopic (LAP) and robotic (ROB) surgery for rectal cancer.

Usefulness of Semisupervised Machine-Learning-Based Phenogrouping to Improve Risk Assessment for Patients Undergoing Transcatheter Aortic Valve Implantation.

The American journal of cardiology
Semisupervised machine-learning methods are able to learn from fewer labeled patient data. We illustrate the potential use of a semisupervised automated machine-learning (AutoML) pipeline for phenotyping patients who underwent transcatheter aortic va...

Machine learning predicts stem cell transplant response in severe scleroderma.

Annals of the rheumatic diseases
OBJECTIVE: The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT...