scholarly journals Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

2019 ◽  
Author(s):  
Suk Lee ◽  
Eunbin Ju ◽  
Suk Woo Choi ◽  
Hyungju Lee ◽  
Jang Bo Shim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cancer Patient ◽  
Patient Outcomes

Abstract 2068: Radiotherapy is associated with a deletion signature that contributes to poor cancer patient outcomes

2021 ◽  
Author(s):  
Emre Kocakavuk ◽  
Kevin J. Anderson ◽  
Kevin C. Johnson ◽  
Frederick S. Varn ◽  
Samirkumar B. Amin ◽  
...  
Keyword(s):  
Cancer Patient ◽  
Patient Outcomes

Clinical Integration of Digital Solutions in Health Care: An Overview of the Current Landscape of Digital Technologies in Cancer Care

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Shivank Garg ◽  
Noelle L. Williams ◽  
Andrew Ip ◽  
Adam P. Dicker
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Health Care ◽  
Data Collection ◽  
Patient Outcomes ◽  
Cancer Care ◽  
Digital Health ◽  
Health Technologies ◽  
Health Care Value

Digital health constitutes a merger of both software and hardware technology with health care delivery and management, and encompasses a number of domains, from wearable devices to artificial intelligence, each associated with widely disparate interaction and data collection models. In this review, we focus on the landscape of the current integration of digital health technology in cancer care by subdividing digital health technologies into the following sections: connected devices, digital patient information collection, telehealth, and digital assistants. In these sections, we give an overview of the potential clinical impact of such technologies as they pertain to key domains, including patient education, patient outcomes, quality of life, and health care value. We performed a search of PubMed ( www.ncbi.nlm.nih.gov/pubmed ) and www.ClinicalTrials.gov for numerous terms related to digital health technologies, including digital health, connected devices, smart devices, wearables, activity trackers, connected sensors, remote monitoring, electronic surveys, electronic patient-reported outcomes, telehealth, telemedicine, artificial intelligence, chatbot, and digital assistants. The terms health care and cancer were appended to the previously mentioned terms to filter results for cancer-specific applications. From these results, studies were included that exemplified use of the various domains of digital health technologies in oncologic care. Digital health encompasses the integration of a vast array of technologies with health care, each associated with varied methods of data collection and information flow. Integration of these technologies into clinical practice has seen applications throughout the spectrum of care, including cancer screening, on-treatment patient management, acute post-treatment follow-up, and survivorship. Implementation of these systems may serve to reduce costs and workflow inefficiencies, as well as to improve overall health care value, patient outcomes, and quality of life.

Public reporting outcomes of multidisciplinary cancer care: Conceptual issues.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 280-280
Author(s):  
James Austin Talcott
Keyword(s):  
Cancer Patient ◽  
Patient Outcomes ◽  
Cancer Care ◽  
Treatment Plan ◽  
Multidisciplinary Care ◽  
Collective Responsibility ◽  
Public Reporting ◽  
Analytic Description ◽  
Eligibility Criteria

280 Background: Patient outcomes are the most valid measures of quality of care. Public reporting makes them available to patients, referring physicians and payers. Our institution has committed to reporting all cancer patient outcomes. In carrying out that objective, we encountered novel analytical issues arising from differences between the usually reported treatments, surgical procedures, and multidisciplinary cancer care. We present important conceptual issues we identified. Methods: Analytic description of methodological issues encountered in publishing cancer patient outcomes. Results: Issues include: (1) Assigning responsibility. Patient outcomes of multidisciplinary cancer care can be influenced by multiple oncology specialties and institutions. While providers directly control only the treatment they provide, providers share collective responsibility for executing the treatment plan, coordinating care and vouching for the quality of collaborating providers. Therefore, reporting outcomes of one element of multidisciplinary care is incomplete and inadequate. (2) Claiming responsibility. Providers with institutional affiliations but no involvement in quality improvement or reporting processes should be excluded from public reporting. Minimum requirements to qualify for public institutional affiliation should be enforced and “free riders” identified. (3) Defining and presenting valid comparison populations. Treatment trials’ eligibility criteria and recruitment practices exclude poorer prognosis patients, producing biased comparisons to complete “registry” populations that distort patient expectations. Publishing outcomes of well-characterized subpopulations improves valid results and provides more individualized information. Conclusions: Reporting patient outcomes after multidisciplinary cancer care raises novel conceptual issues. We discuss our response to three important issues.

scholarly journals Impact of a Fast-track Esophagectomy Protocol on Esophageal Cancer Patient Outcomes and Hospital Charges

2015 ◽  
Vol 261 (6) ◽  
pp. 1114-1123 ◽  
Author(s):  
Jitesh B. Shewale ◽  
Arlene M. Correa ◽  
Carla M. Baker ◽  
Nicole Villafane-Ferriol ◽  
Wayne L. Hofstetter ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Cancer Patient ◽  
Patient Outcomes ◽  
Fast Track ◽  
Esophageal Cancer Patient ◽  
Hospital Charges

scholarly journals Artificial Intelligence–Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development (Preprint)

2020 ◽  
Author(s):  
Enrico Santus ◽  
Nicola Marino ◽  
Davide Cirillo ◽  
Emmanuele Chersoni ◽  
Arnau Montagud ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Health Care ◽  
Research And Development ◽  
Precision Medicine ◽  
Patient Outcomes ◽  
Medical Literature ◽  
Drug Repurposing ◽  
Public Health Systems ◽  
Improve Patient

UNSTRUCTURED Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

scholarly journals Is Artificial Intelligence Better Than Human Clinicians in Predicting Patient Outcomes? (Preprint)

2020 ◽  
Author(s):  
Joon Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Patient Outcome ◽  
Deep Learning ◽  
Patient Outcomes ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Challenging Problem ◽  
Imaging Diagnostics ◽  
Better Than

UNSTRUCTURED In contrast with medical imaging diagnostics powered by artificial intelligence (AI), in which deep learning has led to breakthroughs in recent years, patient outcome prediction poses an inherently challenging problem because it focuses on events that have not yet occurred. Interestingly, the performance of machine learning–based patient outcome prediction models has rarely been compared with that of human clinicians in the literature. Human intuition and insight may be sources of underused predictive information that AI will not be able to identify in electronic data. Both human and AI predictions should be investigated together with the aim of achieving a human-AI symbiosis that synergistically and complementarily combines AI with the predictive abilities of clinicians.

Sign in / Sign up

Export Citation Format

Share Document