Literature-Augmented Clinical Outcome Prediction

Naik, Aakanksha; Parasa, Sravanthi; Feldman, Sergey; Wang, Lucy Lu; Hope, Tom

doi:10.48550/arxiv.2111.08374

Cited by 2 publications

(3 citation statements)

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“…We present key concepts of task-guided scientific knowledge retrieval, including work on prototypes that highlight the promise of the direction and bring into focus concrete steps forward for novel representations, tools, and services. We review systems that help researchers discover novel perspectives and inspirations [16,17,19,42], help guide the attention of researchers toward opportunity areas rife with uncertainties and unknowns [26,46], and models that leverage retrieval and synthesis of scientific knowledge as part of machine learning and prediction [12,37]. We conclude with a discussion of opportunities ahead with computational approaches that have the potential to revolutionize science.…”

Section: Widening Gapmentioning

confidence: 99%

“…(2) In our work on clinical predictions [37], the goal is to enhance prediction of medical outcomes of patients hospitalized in the intensive care unit (ICU), such as in-hospital mortality or prolonged length of stay. Our system, named BEEP (biomedical evidence enhanced prediction), learns to perform predictions by retrieving medical papers that are relevant to each specific ICU patient, and to synthesize this outer knowledge in combination with internal EMR knowledge to form a final prediction.…”

Section: Ingest Scientific Corpora Resourcesmentioning

confidence: 99%

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A Computational Inflection for Scientific Discovery

Hope¹,

Downey²,

Etzioni³

et al. 2022

Preprint

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We stand at the foot of a significant inflection in the trajectory of scientific discovery. As society continues on its fast-paced digital transformation, so does humankind's collective scientific knowledge and discourse. We now read and write papers in digitized form, and a great deal of the formal and informal processes of science are captured digitally-including papers, preprints and books, code and datasets, conference presentations, and interactions in social networks and collaboration and communication platforms. The transition has led to the creation and growth of a tremendous amount of information-much of which is available for public access-opening exciting opportunities for computational models and systems that analyze and harness it. In parallel, exponential growth in data processing power has fueled remarkable advances in artificial intelligence, including self-supervised neural models capable of learning powerful representations from large-scale unstructured text without costly human supervision. Dramatic changes in scientific communication channels-such as the advent of the first scientific journal in the 17 th century-have historically catalyzed revolutions in scientific thought and behavior. The confluence of societal and computational trends suggests that computer science is poised to ignite a revolution in the scientific process itself.

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Section: Widening Gapmentioning

confidence: 99%

Section: Ingest Scientific Corpora Resourcesmentioning

confidence: 99%

A Computational Inflection for Scientific Discovery

Hope¹,

Downey²,

Etzioni³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although these applications are diverse, their common theme is the attempt to make sense of an overabundance of scientific information. Recently, work has further improved these scientific language models by introducing external knowledge from humanconstructed databases into existing models [Lu et al 2021a;Lai et al 2023], applying distillation for data augmentation , and augmenting models via retrieval [Naik et al 2021;Zamani et al 2022].…”

Section: Single-modal Foundation Modelsmentioning

confidence: 99%

Artificial intelligence in recommender systems

Zhang

Jin

2020

Complex Intell. Syst.

206

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Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

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Literature-Augmented Clinical Outcome Prediction

Cited by 2 publications

References 0 publications

A Computational Inflection for Scientific Discovery

A Computational Inflection for Scientific Discovery

Artificial intelligence in recommender systems

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