ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2018
Learning Credible Models.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- The generative revolution: AI foundation models in geospatial health-applications, challenges and future research.International journal of health geographics · 2025Article
- Algorithm, expert, or both? Evaluating the role of feature selection methods on user preferences and reliance.PloS one · 2025Article
- Learning Concept Credible Models for Mitigating Shortcuts.Advances in neural information processing systems · 2022Article
- Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.American journal of surgery · 2021Article
- A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy outcomes prediction in lung cancer patients.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Article
- Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging.Frontiers in cardiovascular medicine · 2021Review
- Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century.The British journal of radiology · 2020Review
- Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records.JCO clinical cancer informatics · 2020Article
- Sleep quality prediction in caregivers using physiological signals.Computers in biology and medicine · 2019Article
- Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.Nature machine intelligence · 2019Article
- Machine learning for patient risk stratification for acute respiratory distress syndrome.PloS one · 2019Article
- Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise As Much As Possible.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In many settings, it is important that a model be capable of providing reasons for its predictions (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.