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Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care.

JMIR mHealth and uHealth
BACKGROUND: Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consisten...

SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification.

International journal of molecular sciences
The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used...

Artificial intelligence (AI) applications in improvement of IMRT and VMAT radiotherapy treatment planning processes: A systematic review.

Radiography (London, England : 1995)
INTRODUCTION: Radiotherapy is a common option in the treatment of many types of cancer. Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT) are the latest radiotherapy techniques. However, clinicians face problems...

Interpretable machine learning model predicting immune checkpoint inhibitor-induced hypothyroidism: A retrospective cohort study.

Cancer science
Hypothyroidism is a known adverse event associated with the use of immune checkpoint inhibitors (ICIs) in cancer treatment. This study aimed to develop an interpretable machine learning (ML) model for individualized prediction of hypothyroidism in pa...

A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.

International journal of clinical oncology
BACKGROUND: Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of ...

The Use of Artificial Intelligence Technologies in Cancer Care.

Clinical oncology (Royal College of Radiologists (Great Britain))
Artificial intelligence (AI) is already an essential tool in the handling of large data sets in epidemiology and basic research. Significant contributions to radiological diagnosis are emerging alongside increasing use of digital pathology. The futur...

Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND/PURPOSE: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. ...

[Development and validation of a tool for the systematic identification of social vulnerabilities in cancer patients: the DEFCO tool].

Bulletin du cancer
INTRODUCTION: Literature suggests that patients from deprived backgrounds are less likely to adhere to their treatments, continue to expose themselves to risk factors and, as a result, have poorer health outcomes. It is therefore crucial to identify ...

Development of message passing-based graph convolutional networks for classifying cancer pathology reports.

BMC medical informatics and decision making
BACKGROUND: Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language...

The emerging role of AI in enhancing intratumoral immunotherapy care.

Oncotarget
The emergence of immunotherapy (IO), and more recently intratumoral IO presents a novel approach to cancer treatment which can enhance immune responses while allowing combination therapy and reducing systemic adverse events. These techniques are inte...