AIMC Topic: Medical Oncology

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Cancer Informatics in 2019: Deep Learning Takes Center Stage.

Yearbook of medical informatics
OBJECTIVE: To summarize significant research contributions on cancer informatics published in 2019.

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care.

Yearbook of medical informatics
OBJECTIVES: Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We a...

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology.

Clinical pharmacology and therapeutics
The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive a...

Artificial intelligence and radiomics in pediatric molecular imaging.

Methods (San Diego, Calif.)
In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving fie...

Radiomics in neuro-oncology: Basics, workflow, and applications.

Methods (San Diego, Calif.)
Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and...

Early experience with Watson for Oncology: a clinical decision-support system for prostate cancer treatment recommendations.

World journal of urology
PURPOSE: Urological oncologists have difficulty providing optimal personalized care due to rapid alterations in scientific research results, medical advancements, and treatment guidelines. IBM's Watson for Oncology (WFO) is an artificial intelligence...

Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data.

BMC health services research
BACKGROUND: The Oncology Care Model (OCM) was developed as a payment model to encourage participating practices to provide better-quality care for cancer patients at a lower cost. The risk-adjustment model used in OCM is a Gamma generalized linear mo...

Innovations in research and clinical care using patient-generated health data.

CA: a cancer journal for clinicians
Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and t...

Oncology Research: Clinical Trial Management Systems, Electronic Medical Record, and Artificial Intelligence.

Seminars in oncology nursing
OBJECTIVE: To discuss the implications of electronic systems and regulations regarding the use of electronic systems implemented during the conduct of a clinical trial and identify the impact of such platforms on oncology nurses' responsible for prov...