Cross-trial prediction of treatment outcome in depression: a machine learning approach.

By Adam M. Chekroud, Ryan Zotti, Zarrar Shehzad, Ralitza Gueorguieva, Marcia K. Johnson, Madhukar H. Trivedi, Tyrone D. Cannon, John H. Krystal, Philip R. Corlett. Lancet psychiatry, 2016

586 citations recorded by the source corpus.

Subfields: Applied Psychology, Pharmacology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/6194c942b91634e8b7c340f158ba759b0789ca7d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

1. Fei Wang
2. Avi Goldfarb
3. Robert J. DeRubeis
4. Anita M. Preininger

In Pharmacology

1. Anita M. Preininger
2. Amir Hussain
3. Mufti Mahmud
4. Fei Wang
5. Robert J. DeRubeis

In Social Psychology

1. Amir Hussain
2. Kate H Bentley

Citations in the corpus

• The promise of machine learning in predicting treatment outcomes in psychiatry (2021, background): “…logistic regression with elastic net regularization to identify the most relevant predictors in the EDEN study (N=1027) – much like Chekroud et al 40 – to determine a smaller subset of variables that could still predict outcomes but require less effort for future data collection and improve…”
• Artificial Intelligence and the Implementation Challenge (2019, background): “…changes related to forecasted emergency room volumes [21] Producing a more accurate prediction of the likely outcome of a particular intervention [20] Decision support N/A d Use of robotics for operational tasks in dementia care, such as meal delivery [24] Automatically altering insulin…”
• Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, methodology): “ML methods were also able to predict treatment responses to commonly prescribed antidepressants like citalopram (65% accuracy) [64], or identify features like education that were related to placebo versus medication responses [66].”
• Automated assessment of psychiatric disorders using speech: A systematic review (2019, background): “25 More immediately, such technologies could be used for screening in schools, universities, armed forces, and primary-care settings.”
• Establishment of Best Practices for Evidence for Prediction A Review (2019, unknown): “be performed with samples smaller than several hundred observations, (4) multiple measures of prediction accuracy should be examined and reported, (5) the coefficient of determination should be computed using the sums of squares formulation and not the correlation coefficient, and (6) kfold cross-va”
• Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “Another example of an incorrect prediction by ML that could be fixed by interpretability is the case of a deep learning model that was developed to predict which patients would benefit from an antidepressant medication called escitalopram [35].”
• Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis (2018, unknown): “95 Munich 22 (33) 11 (22) 22 (34) 17 (31)”
• AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “[89] adopted feedforward feature selection and gradient boosting in cross-trial prediction of treatment outcomes in depression.”