eD octor: machine learning and the future of medicine
Abstract Machine learning (ML) is a burgeoning field of medicine with huge resources being
applied to fuse computer science and statistics to medical problems. Proponents of ML extol …
applied to fuse computer science and statistics to medical problems. Proponents of ML extol …
Opportunities and challenges in develo** risk prediction models with electronic health records data: a systematic review
Objective: Electronic health records (EHRs) are an increasingly common data source for
clinical risk prediction, presenting both unique analytic opportunities and challenges. We …
clinical risk prediction, presenting both unique analytic opportunities and challenges. We …
Systematic review on ai-blockchain based e-healthcare records management systems
Electronic health records (EHRs) are digitally saved health records that provide information
about a person's health. EHRs are generally shared among healthcare stakeholders, and …
about a person's health. EHRs are generally shared among healthcare stakeholders, and …
Development and assessment of a machine learning model to help predict survival among patients with oral squamous cell carcinoma
Importance Predicting survival of oral squamous cell carcinoma through the use of
prediction modeling has been underused, and the development of prediction models would …
prediction modeling has been underused, and the development of prediction models would …
The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning
J Sim, YA Kim, JH Kim, JM Lee, MS Kim, YM Shim… - Scientific reports, 2020 - nature.com
The primary goal of this study was to evaluate the major roles of health-related quality of life
(HRQOL) in a 5-year lung cancer survival prediction model using machine learning …
(HRQOL) in a 5-year lung cancer survival prediction model using machine learning …
Development and validation of a deep learning algorithm for mortality prediction in selecting patients with dementia for earlier palliative care interventions
Importance Early palliative care interventions drive high-value care but currently are
underused. Health care professionals face challenges in identifying patients who may …
underused. Health care professionals face challenges in identifying patients who may …
Comparison of machine learning algorithms for the prediction of five‐year survival in oral squamous cell carcinoma
H Alkhadar, M Macluskey, S White… - Journal of Oral …, 2021 - Wiley Online Library
Abstract Background/Aim Machine learning analyses of cancer outcomes for oral cancer
remain sparse compared to other types of cancer like breast or lung. The purpose of the …
remain sparse compared to other types of cancer like breast or lung. The purpose of the …
[HTML][HTML] Machine learning methods to extract documentation of breast cancer symptoms from electronic health records
Context Clinicians document cancer patients' symptoms in free-text format within electronic
health record visit notes. Although symptoms are critically important to quality of life and …
health record visit notes. Although symptoms are critically important to quality of life and …
Use of electronic health data for disease prediction: A comprehensive literature review
Disease prediction has the potential to benefit stakeholders such as the government and
health insurance companies. It can identify patients at risk of disease or health conditions …
health insurance companies. It can identify patients at risk of disease or health conditions …
Lung cancer survival prediction via machine learning regression, classification, and statistical techniques
JA Bartholomai, HB Frieboes - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
A regression model is developed to predict survival time in months for lung cancer patients.
It was previously shown that predictive models perform accurately for short survival times of …
It was previously shown that predictive models perform accurately for short survival times of …