Involvement of machine learning tools in healthcare decision making
SMDAC Jayatilake… - Journal of healthcare …, 2021 - Wiley Online Library
In the present day, there are many diseases which need to be identified at their early stages
to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason …
to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason …
[HTML][HTML] Machine learning and data mining methods in diabetes research
The remarkable advances in biotechnology and health sciences have led to a significant
production of data, such as high throughput genetic data and clinical information, generated …
production of data, such as high throughput genetic data and clinical information, generated …
[HTML][HTML] Comparing different supervised machine learning algorithms for disease prediction
Supervised machine learning algorithms have been a dominant method in the data mining
field. Disease prediction using health data has recently shown a potential application area …
field. Disease prediction using health data has recently shown a potential application area …
[HTML][HTML] Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
L Huang, AL Shea, H Qian, A Masurkar, H Deng… - Journal of biomedical …, 2019 - Elsevier
Electronic medical records (EMRs) support the development of machine learning algorithms
for predicting disease incidence, patient response to treatment, and other healthcare events …
for predicting disease incidence, patient response to treatment, and other healthcare events …
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 …
A machine learning-based framework to identify type 2 diabetes through electronic health records
Objective To discover diverse genotype-phenotype associations affiliated with Type 2
Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide …
Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide …
A patient network-based machine learning model for disease prediction: The case of type 2 diabetes mellitus
In recent years, the prevalence of chronic diseases such as type 2 diabetes mellitus (T2DM)
has increased, bringing a heavy burden to healthcare systems. While regular monitoring of …
has increased, bringing a heavy burden to healthcare systems. While regular monitoring of …
Mining electronic health records (EHRs) A survey
The continuously increasing cost of the US healthcare system has received significant
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
A review of approaches to identifying patient phenotype cohorts using electronic health records
Objective To summarize literature describing approaches aimed at automatically identifying
patients with a common phenotype. Materials and methods We performed a review of …
patients with a common phenotype. Materials and methods We performed a review of …
Automated machine learning for healthcare and clinical notes analysis
Machine learning (ML) has been slowly entering every aspect of our lives and its positive
impact has been astonishing. To accelerate embedding ML in more applications and …
impact has been astonishing. To accelerate embedding ML in more applications and …