Markov logic networks for adverse drug event extraction from text

Natarajan, Sriraam; Bangera, Vishal; Khot, Tushar; Picado, Benjamim; Wazalwar, Anurag; Costa, Vítor Santos; Page, David; Caldwell, Michael S.

doi:10.1007/s10115-016-0980-6

Cited by 11 publications

(6 citation statements)

References 32 publications

(26 reference statements)

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“…While other state-of-the-art information extraction/identification approaches have been proposed in the machine learning and NLP literature, the scarcity of annotated social media data is still a major obstacle in assessing the true value of social media for the various health-related tasks. Novel, generic extraction algorithms for various health-related tasks have been proposed in the recent past [170], but their performance in real-life social media data have not been evaluated. This obstacle has been discussed in recent reviews [100] and there has been a greater urgency for creating and releasing annotated datasets and targeted unlabeled data sets in distinct languages [102,159,171].…”

Section: Resultsmentioning

confidence: 99%

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Gonzalez-Hernandez¹,

Sarker²,

O’Connor³

et al. 2017

Yearb Med Inform

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SummaryBackground: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems.

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Section: Resultsmentioning

confidence: 99%

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Gonzalez-Hernandez¹,

Sarker²,

O’Connor³

et al. 2017

Yearb Med Inform

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“…Natarajan et al [48] proposed MLNs for adverse drug event extraction from text. Thus, in this work, authors addressed the question of whether they can quantitatively estimate relationships between drugs and conditions from the medical literature.…”

Section: Application Of Srlmentioning

confidence: 99%

Statistical Relational Learning: A State-of-the-Art Review

Kastrati¹,

Biba²

2019

Journal of Engineering Technology and Applied Sciences

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The objective of this paper is to review the state-of-the-art of statistical relational learning (SRL) models developed to deal with machine learning and data mining in relational domains in presence of missing, partially observed, and/or noisy data. It starts by giving a general overview of conventional graphical models, first-order logic and inductive logic programming approaches as needed for background. The historical development of each SRL key model is critically reviewed. The study also focuses on the practical application of SRL techniques to a broad variety of areas and their limitations.

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“…Karlsson et al [11] and Page et al [4] perform ADE information extraction from EHR data. More recently, Kang et al [12] took a knowledge-based approach for extracting ADEs from bio-medical text, while Natarajan et al [13] use Markov logic networks for the same problem.…”

Section: Related Workmentioning

confidence: 99%

Drug‐Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities

et al. 2018

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We develop a pipeline to mine complex drug interactions by combining different similarities and interaction types (molecular, structural, phenotypic, genomic etc). Our goal is to learn an optimal kernel from these heterogeneous similarities in a supervised manner. We formulate an extensible framework that can easily integrate new interaction types into a rich model. The core of our pipeline features a novel kernel-learning approach that tunes the weights of the heterogeneous similarities, and fuses them into a Similarity-based Kernel for Identifying Drug-Drug interactions and Discovery, or SKID3. Experimental evaluation on the DrugBank database shows that SKID3 effectively combines similarities generated from chemical reaction pathways (which generally improve precision) and molecular and structural fingerprints (which generally improve recall) into a single kernel that gets the best of both worlds, and consequently demonstrates the best performance.

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Markov logic networks for adverse drug event extraction from text

Cited by 11 publications

References 32 publications

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Capturing the Patient’s Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Statistical Relational Learning: A State-of-the-Art Review

Drug‐Drug Interaction Discovery: Kernel Learning from Heterogeneous Similarities

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