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Limitations of machine learning in psychiatry: Participation in the PAC 2018 depression challenge
Fabian Eitel; Sebastian Stober; Lea Waller; Lena Dorfschmidt; Henrik Walter; Kerstin Ritter
2019-06-25
psychiatry and clinical psychology
10.1101/19000562
medRxiv
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The authors have withdrawn this manuscript because the results were posted in error. The authors do not wish this work to be cited as reference for the project. Please contact the corresponding author if you have any questions.
Matching journals
●Non-profit
◐University press
○Commercial
The top 7 journals account for 50% of the predicted probability mass.
1
Frontiers in Psychiatry
○
87 papers in training set
Top 0.1%
12.9%
Similar papers in this journal
- Applications of Large Language Models in Psychiatry: A Systematic Review 93%
- Patients with affective disorders profit most from telemedical treatment: Evidence from a naturalistic patient cohort during the COVID-19 pandemic 93%
- Computational Psychiatry Research Map (CPSYMAP): a New Database for Visualizing Research Papers 92%
2
Translational Psychiatry
○
260 papers in training set
Top 0.5%
9.7%
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- Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data 93%
- Identifying Psychosis Episodes in Psychiatric Admission Notes via Rule-based Methods, Machine Learning, and Pre-Trained Language Models 93%
- Contactless Depression Screening via Facial Video-derived Heart Rate Variability 92%
3
Psychiatry Research
○
41 papers in training set
Top 0.1%
7.9%
Similar papers in this journal
- Integrating Expert Knowledge into Large Language Models Improves Performance for Psychiatric Reasoning and Diagnosis 94%
- Symptom Monitoring based on Digital Data Collection During Inpatient Treatment of Schizophrenia Spectrum Disorders – a Feasibility Study 92%
- The Relationship between Cannabis Use and Cognition in People with Bipolar Disorder: A Systematic Scoping Review 91%
4
npj Digital Medicine
○
118 papers in training set
Top 0.7%
7.9%
Similar papers in this journal
5
Acta Neuropsychiatrica
◐
14 papers in training set
Top 0.1%
5.5%
Similar papers in this journal
50% of probability mass above
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.