Marcus Eng Hock Ong

Epidemiology · 2014–2020

A researcher in the Science corpus: 5 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Epidemiology — 34 citations into it
• Cardiology and Cardiovascular Medicine — 29 citations into it
• Artificial Intelligence — 27 citations into it
• Statistics and Probability — 16 citations into it
• Health Information Management — 8 citations into it
• Information Systems — 5 citations into it

Whom Marcus Eng Hock Ong trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Peter C. Austin
2. Robert Tibshirani
3. Niko Speybroeck
4. P. Murtaugh
5. J. Tu

Papers from Marcus Eng Hock Ong's perspective

Ordered by how strongly the researchers Marcus Eng Hock Ong trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Shrinkage and Selection via the Lasso
2. Random Forests
3. Classification and regression trees
4. Bootstrap Methods for Developing Predictive Models
5. METHODS OF VARIABLE SELECTION IN REGRESSION MODELING
6. Variable selection using random forests
7. Presentation of multivariate data for clinical use: The Framingham Study risk score functions
8. Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients*
9. ViEWS--Towards a national early warning score for detecting adult inpatient deterioration.
10. Analysis of a Random Forests Model

Their papers

• AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020) — 123 citations
• Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study (2019) — 21 citations
• FAM-FACE-SG: a score for risk stratification of frequent hospital admitters (2017) — 15 citations
• A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain (2016) — 33 citations
• Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection (2014) — 72 citations