Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications

Nordon, Galia; Koren, Gideon; Shalev, Varda; Horvitz, Eric; Radinsky, Kira

doi:10.1609/aaai.v33i01.33019565

Cited by 12 publications

(9 citation statements)

References 26 publications

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“…The most similar to our current work is the system proposed by (Nordon et al 2019). The authors use Electronic Medical Record (EMR) to generate candidates for drug repurposing and then use a knowledge graph constructed from MEDLINE documents to validate those candidates.…”

Section: Background and Related Workmentioning

confidence: 99%

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

Tyagin

Kulshrestha

Sybrandt

et al. 2022

AAAI

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In 2020, the White House released the “Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset,” wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19. The Allen Institute for AI and collaborators announced the availability of a rapidly growing open dataset of publications, the COVID-19 Open Research Dataset (CORD-19). As the pace of research accelerates, biomedical scientists struggle to stay current. To expedite their investigations, scientists leverage hypothesis generation systems, which can automatically inspect published papers to discover novel implicit connections. We present automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. The systems are based on the graph mining and transformer models. The systems are massively validated using retrospective information rediscovery and proactive analysis involving human-in-the-loop expert analysis. Both systems achieve high-quality predictions across domains in fast computational time and are released to the broad scientific community to accelerate biomedical research. In addition, by performing the domain expert curated study, we show that the systems are able to discover ongoing research findings such as the relationship between COVID-19 and oxytocin hormone. All code, details, and pre-trained models are available at https://github.com/IlyaTyagin/AGATHA-C-GP.

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Section: Background and Related Workmentioning

confidence: 99%

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

Tyagin

Kulshrestha

Sybrandt

et al. 2022

AAAI

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“…AI for social good is a broad research topic as described in Shi et al (2020). For public health, Nordon et al (2019) uses biomedical knowledge graph for drug discovery. Finally, Percha and Altman (2018) connect entity-pair dependency paths to extract relations between chemicals, genes and diseases.…”

Section: Knowledge Extraction For Social Goodmentioning

confidence: 99%

Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy

López¹,

Yadav²,

Picco³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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In challenging economic times, obtaining value for money by ensuring financial integrity and fairer distribution of services are among the top priorities for social and health-care systems globally. However, healthcare billing policies are complex and identifying non-compliance is often narrow-scope, manual and expensive. Maintaining 'integrity' is a challengeensuring that scarce resources get to those in need and are not lost to fraud and waste. Our approach fuses recent advances in dependency parsing with a policy ontology to convert the content of regulatory healthcare policy into human-friendly policy rules, that are amenable to machineexecution, with human oversight. We describe the ontology-guided transformation of textual patterns into a semantically-meaningful knowledge graph of rules, outline our experiments and evaluate results against policy rules obtained from professional investigators. The aim is to make a policy-compliance 'landscape' visible to healthcare programs -helping them identify Fraud, Waste or Abuse.

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“…Literature based discovery (Swanson 1986) has been applied successfully to the fields of medicine and biomedicine. Both using textual repositories (Swanson and Smalheiser 1999;Spangler et al 2014;Sybrandt, Shtutman, and Safro 2017;Lally et al 2017) and in combination with other data (Choi, Chiu, and Sontag 2016;Nordon et al 2019). The task of biomedical word embedding has also been the subject of diverse research, Choi, Chiu, and Sontag, show that biomedical embeddings based on different sources can differ in their representation of relations between concepts.…”

Section: Related Workmentioning

confidence: 99%

“…A famous example is the repurpsoing of Sildenafil, an antihypertensive drug, to treat erectile dysfunction. In recent years, several computational approaches were developed for the task, including: (1) Electronic health records (EHRs) based approaches (Nordon et al 2019); (2) Identifying genes associated with a disease; (3) Predicting binding site between a ligand (e.g., a drug) and a target protein using an optimization process of the chemical structure of the drug to best fit the protein structure; (4) Signature matching of a drug and a disease; In this work we focus on signature matching for the task of drug repurposing.…”

Section: Introductionmentioning

confidence: 99%

Chemical and Textual Embeddings for Drug Repurposing

Nordon

Gottlieb

Radinsky

2020

AAAI

Self Cite

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Drug approval is a long and expensive process, that can take 10-15 years and more than 2 billion dollars. Therefore alternative techniques, such as drug repositioning, to identify new uses for approved drugs, has been gaining increasing attention. We examine the employment of different drug embeddings to predict successful drug repositioning. We study the employment of drug molecular structure and show that using larger chemical construct, such as large functional chemical groups, is much more effective than small sub-structures. We then study embeddings that are based on textual medical publications and compare them with the chemical-structure-based embeddings. We eventually present a novel embedding technique to combine the merit of the textual and chemical-based approaches. We provide empirical results on a repositioning benchmark set. Additionally, we present an application of such embedding as part of an ongoing repositioning research conducted with a major health care supplier, and identify a novel drug and indication. The pair has been verified on a corpus of 1.5 million patient EHR data.

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Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications

Cited by 12 publications

References 26 publications

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy

Chemical and Textual Embeddings for Drug Repurposing

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