Learning to Predict with Supporting Evidence: Applications to Clinical\n Risk Prediction Article Swipe
Aniruddh Raghu
,
John V. Guttag
,
Katherine Young
,
Eugene Pomerantsev
,
Adrian V. Dalca
,
Collin M. Stultz
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.02768
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.02768
The impact of machine learning models on healthcare will depend on the degree\nof trust that healthcare professionals place in the predictions made by these\nmodels. In this paper, we present a method to provide people with clinical\nexpertise with domain-relevant evidence about why a prediction should be\ntrusted. We first design a probabilistic model that relates meaningful latent\nconcepts to prediction targets and observed data. Inference of latent variables\nin this model corresponds to both making a prediction and providing supporting\nevidence for that prediction. We present a two-step process to efficiently\napproximate inference: (i) estimating model parameters using variational\nlearning, and (ii) approximating maximum a posteriori estimation of latent\nvariables in the model using a neural network, trained with an objective\nderived from the probabilistic model. We demonstrate the method on the task of\npredicting mortality risk for patients with cardiovascular disease.\nSpecifically, using electrocardiogram and tabular data as input, we show that\nour approach provides appropriate domain-relevant supporting evidence for\naccurate predictions.\n
Related Topics
Concepts
Inference
Machine learning
Computer science
Artificial intelligence
Latent variable
Probabilistic logic
Artificial neural network
A priori and a posteriori
Task (project management)
Process (computing)
Domain (mathematical analysis)
Data mining
Engineering
Mathematics
Philosophy
Epistemology
Operating system
Mathematical analysis
Systems engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.02768
- https://arxiv.org/pdf/2103.02768
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4295265090
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4295265090Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2103.02768Digital Object Identifier
- Title
-
Learning to Predict with Supporting Evidence: Applications to Clinical\n Risk PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-03Full publication date if available
- Authors
-
Aniruddh Raghu, John V. Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. StultzList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.02768Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2103.02768Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2103.02768Direct OA link when available
- Concepts
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Inference, Machine learning, Computer science, Artificial intelligence, Latent variable, Probabilistic logic, Artificial neural network, A priori and a posteriori, Task (project management), Process (computing), Domain (mathematical analysis), Data mining, Engineering, Mathematics, Philosophy, Epistemology, Operating system, Mathematical analysis, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.prediction. | 78 |
| abstract_inverted_index.predictions | 20 |
| abstract_inverted_index.be\ntrusted. | 44 |
| abstract_inverted_index.approximating | 95 |
| abstract_inverted_index.for\naccurate | 148 |
| abstract_inverted_index.probabilistic | 49, 115 |
| abstract_inverted_index.professionals | 16 |
| abstract_inverted_index.variables\nin | 64 |
| abstract_inverted_index.cardiovascular | 130 |
| abstract_inverted_index.of\npredicting | 124 |
| abstract_inverted_index.predictions.\n | 149 |
| abstract_inverted_index.these\nmodels. | 23 |
| abstract_inverted_index.domain-relevant | 37, 145 |
| abstract_inverted_index.latent\nconcepts | 54 |
| abstract_inverted_index.electrocardiogram | 133 |
| abstract_inverted_index.latent\nvariables | 101 |
| abstract_inverted_index.objective\nderived | 112 |
| abstract_inverted_index.clinical\nexpertise | 35 |
| abstract_inverted_index.supporting\nevidence | 75 |
| abstract_inverted_index.variational\nlearning, | 92 |
| abstract_inverted_index.disease.\nSpecifically, | 131 |
| abstract_inverted_index.efficiently\napproximate | 85 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.59866586 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |