Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis

(2015) Junichiro Satomi et al.   JOURNAL OF NEUROSURGERY

Probabilistic machine learning and artificial intelligence

(2015) Zoubin Ghahramani   NATURE

Toward Probabilistic Diagnosis and Understanding of Depression Based on Functional MRI Data Analysis with Logistic Group LASSO

(2015) Yu Shimizu et al.   PLoS One

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting

(2015) Lakshmana Ayaru et al.   PLoS One

Applying Classification Trees to Hospital Administrative Data to Identify Patients with Lower Gastrointestinal Bleeding

(2015) Juned Siddique et al.   PLoS One

Machine learning, medical diagnosis, and biomedical engineering research - commentary

(2014) Kenneth R Foster et al.   Biomedical Engineering Online

Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments

(2014) Truyen Tran et al.   BMC Psychiatry

The Evolution of Boosting Algorithms

(2014) H. Binder et al.   METHODS OF INFORMATION IN MEDICINE

Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

(2014) Tsair-Fwu Lee et al.   PLoS One

Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier

(2014) Tatsuya Yoshida et al.   PLoS One

The Parable of Google Flu: Traps in Big Data Analysis

(2014) D. Lazer et al.   SCIENCE

Overfitting in prediction models – Is it a problem only in high dimensions?

(2013) Jyothi Subramanian et al.   Contemporary Clinical Trials

A Survey ofL1Regression

(2013) Diego Vidaurre et al.   INTERNATIONAL STATISTICAL REVIEW

Gradient boosting machines, a tutorial

(2013) Alexey Natekin et al.   Frontiers in Neurorobotics

Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?

(2012) W. G. Touw et al.   BRIEFINGS IN BIOINFORMATICS

The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets

(2012) O. González-Recio et al.   JOURNAL OF DAIRY SCIENCE

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

(2012) Anne-Laure Boulesteix et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Decision trees: a recent overview

(2011) S. B. Kotsiantis   ARTIFICIAL INTELLIGENCE REVIEW

Regression shrinkage and selection via the lasso: a retrospective

(2011) Robert Tibshirani   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. “Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment”

(2011) Andreas Jungherr et al.   SOCIAL SCIENCE COMPUTER REVIEW

Pitfalls of supervised feature selection

(2009) Pawel Smialowski et al.   BIOINFORMATICS

A Division Algebraic Framework for Multidimensional Support Vector Regression

(2009) A. Shilton et al.   IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS

What are decision trees?

(2008) Carl Kingsford et al.   NATURE BIOTECHNOLOGY