Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Meaningless comparisons lead to false optimism in medical machine learning
Orianna DeMasi, Konrad P. Körding, Benjamin Recht
PLoS ONE (2017) Vol. 12, Iss. 9, pp. e0184604-e0184604
Open Access | Times Cited: 67

Showing 1-25 of 67 citing articles:

Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
Nick McGreivy, Ammar Hakim
Nature Machine Intelligence (2024) Vol. 6, Iss. 10, pp. 1256-1269
Closed Access | Times Cited: 24
Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors
Paola Pedrelli, Szymon Fedor, Asma Ghandeharioun, et al.
Frontiers in Psychiatry (2020) Vol. 11
Open Access | Times Cited: 117
Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors
Katharina Schultebraucks, Meng Qian, Duna Abu‐Amara, et al.
Molecular Psychiatry (2020) Vol. 26, Iss. 9, pp. 5011-5022
Open Access | Times Cited: 90
Machine learning applications in radiation oncology
Matthew Field, Nicholas Hardcastle, Michael Jameson, et al.
Physics and Imaging in Radiation Oncology (2021) Vol. 19, pp. 13-24
Open Access | Times Cited: 63
REFORMS: Consensus-based Recommendations for Machine-learning-based Science
Sayash Kapoor, Emily M. Cantrell, Kenny Peng, et al.
Science Advances (2024) Vol. 10, Iss. 18
Open Access | Times Cited: 15
A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression
Rahel Pearson, Derek Pisner, Björn Meyer, et al.
Psychological Medicine (2018) Vol. 49, Iss. 14, pp. 2330-2341
Open Access | Times Cited: 69
Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications
David Daniel Ebert, Mathias Harrer, Jennifer Apolinário-Hagen, et al.
Advances in experimental medicine and biology (2019), pp. 583-627
Closed Access | Times Cited: 58
Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach
Silvan Hornstein, Valerie L. Forman‐Hoffman, Albert Nazander, et al.
Digital Health (2021) Vol. 7
Open Access | Times Cited: 43
Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment
Santiago Papini, Sonya B. Norman, Laura Campbell‐Sills, et al.
JAMA Network Open (2023) Vol. 6, Iss. 6, pp. e2321273-e2321273
Open Access | Times Cited: 17
Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions
Kirsten Zantvoort, Nils Hentati Isacsson, Burkhardt Funk, et al.
Digital Health (2024) Vol. 10
Open Access | Times Cited: 6
Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization
Santiago Papini, Derek Pisner, Jason Shumake, et al.
Journal of Anxiety Disorders (2018) Vol. 60, pp. 35-42
Open Access | Times Cited: 59
Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study
Orianna DeMasi, Sidney Feygin, Aluma Dembo, et al.
JMIR mhealth and uhealth (2017) Vol. 5, Iss. 10, pp. e137-e137
Open Access | Times Cited: 51
Do Models of Mental Health Based on Social Media Data Generalize?
Keith Harrigian, Carlos Aguirre, Mark Dredze
(2020)
Open Access | Times Cited: 40
Finding the Best Match — a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions
Kirsten Zantvoort, Jonas Scharfenberger, Leif Boß, et al.
Journal of Healthcare Informatics Research (2023) Vol. 7, Iss. 4, pp. 447-479
Open Access | Times Cited: 15
Emotion Recognition Using Smart Watch Sensor Data: Mixed-Design Study
Juan C. Quiroz, Elena Geangu, Min Hooi Yong
JMIR Mental Health (2018) Vol. 5, Iss. 3, pp. e10153-e10153
Open Access | Times Cited: 43
A step towards quantifying when an algorithm can and cannot predict an individual's wellbeing
Orianna DeMasi, Benjamin Recht
(2017), pp. 763-771
Closed Access | Times Cited: 26
A web-based automated machine learning platform to analyze liquid biopsy data
Hanfei Shen, Tony Liu, Jesse Cui, et al.
Lab on a Chip (2020) Vol. 20, Iss. 12, pp. 2166-2174
Closed Access | Times Cited: 21
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
Glorianna Jagfeld, Fiona Lobban, Paul Rayson, et al.
(2021)
Open Access | Times Cited: 20
Smartphones within Psychological Science
David A. Ellis
(2020)
Closed Access | Times Cited: 19
Methodological choices and clinical usefulness for machine learning predictions of outcome in Internet-based cognitive behavioural therapy
Nils Hentati Isacsson, Fehmi Ben Abdesslem, Erik Forsell, et al.
Communications Medicine (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 2
Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions
Kirsten Zantvoort, Barbara Nacke, Dennis Görlich, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 2
The relationship between cognitive deficits and impaired short-term functional outcome in clinical high-risk for psychosis participants: A machine learning and modelling approach
Kate Haining, Gina Brunner, Ruchika Gajwani, et al.
Schizophrenia Research (2021) Vol. 231, pp. 24-31
Open Access | Times Cited: 13
Resolving the Credibility Crisis: Recommendations for Improving Predictive Algorithms for Clinical Utility
Stephen J. Ruberg, Sandeep Menon, Charmaine Demanuele
Harvard data science review (2023) Vol. 5, Iss. 4
Open Access | Times Cited: 5
Back to the future - The integration of big data with machine learning is re-establishing the importance of predictive correlations in ovarian cancer diagnostics and therapeutics
John F. McDonald
Gynecologic Oncology (2018) Vol. 149, Iss. 2, pp. 230-231
Closed Access | Times Cited: 15
Can We Assess Mental Health Through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation
Adam Tsakalidis, Maria Liakata, Theo Damoulas, et al.
Lecture notes in computer science (2019), pp. 407-423
Closed Access | Times Cited: 15
Page 1 - Next Page

Scroll to top