Empirical study using network of semantically related associations in bridging the knowledge gap

Abedi, Vida; Yeasin, Mohammed; Zand, Ramin

doi:10.1186/s12967-014-0324-9

Cited by 11 publications

(5 citation statements)

References 22 publications

(26 reference statements)

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“…In the future, additional computational approaches should be tested on idea assessment. For example, multi-gram dictionary, if available, may be used to account for phrases [1]. Semantic network analysis has been used to evaluate creative ideas [24].…”

Section: Discussionmentioning

confidence: 99%

Towards Computational Assessment of Idea Novelty

Wang¹,

Dong²,

Ma³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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In crowdsourcing ideation websites, companies can easily collect large amount of ideas. Screening through such volume of ideas is very costly and challenging, necessitating automatic approaches. It would be particularly useful to automatically evaluate idea novelty since companies commonly seek novel ideas. Three computational approaches were tested, based on Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and term frequency-inverse document frequency (TF-IDF), respectively. These three approaches were used on three set of ideas and the computed idea novelty was compared with human expert evaluation. TF-IDF based measure correlated better with expert evaluation than the other two measures. However, our results show that these approaches do not match human judgement well enough to replace it.

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Section: Discussionmentioning

confidence: 99%

Towards Computational Assessment of Idea Novelty

Wang¹,

Dong²,

Ma³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Latent semantic indexing (LSI) was used as a data-driven dimensionality reduction strategy. More specifically, each of the four variations of NLP outputs was fed into the LSI 20 , 21 pipeline, where the dimensionality of the features was reduced to 50%, 20%, 10%, and 5%, respectively, thus generating five versions for each of the four outputs note collections. The various post-processing and the LSI pipeline created 20 versions of the ED provider (four entity selection × five LSI levels) for modeling [ Figure 1(d) ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Prediction of Stroke in Emergency Departments

Abedi,

Misra,

Chaudhary

et al. 2024

Ther Adv Neurol Disord

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Background: Stroke misdiagnosis, associated with poor outcomes, is estimated to occur in 9% of all stroke patients. Objectives: We hypothesized that machine learning (ML) could assist in the diagnosis of ischemic stroke in emergency departments (EDs). Design: The study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines. We performed model development and prospective temporal validation, using data from pre- and post-COVID periods; we also performed a case study on a small cohort of previously misdiagnosed stroke patients. Methods: We used structured and unstructured electronic health records (EHRs) of 56,452 patient encounters from 13 hospitals in Pennsylvania, from September 2003 to January 2021. ML pipelines, including natural language processing, were created using pre-event clinical data and provider notes in the EDs. Results: Using pre-event information, our model’s area under the receiver operating characteristics curve (AUROC) ranged from 0.88 to 0.92 with a similar range accuracy (0.87–0.90). Using provider notes, we identified five models that reached a balanced performance in terms of AUROC, sensitivity, and specificity. Model AUROC ranged from 0.93 to 0.99. Model sensitivity and specificity reached 0.90 and 0.99, respectively. Four of the top five performing models were based on the post-COVID provider notes; however, no performance difference between models tested on pre- and post-COVID was observed. Conclusion: This study leveraged pre-event and at-encounter level EHR for stroke prediction. The results indicate that available clinical information can be used for building EHR-based stroke prediction models and ED stroke alert systems.

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“…20 Another example includes the use of natural language processing for identification of hidden or novel associations that might be important in the detection of potential drug adverse effects based on scientific publications. 42…”

Section: Drug Discovery and Repurposingmentioning

confidence: 99%

Artificial Intelligence Transforms the Future of Health Care

Noorbakhsh-Sabet

Zand

Zhang

et al. 2019

The American Journal of Medicine

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344

178

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Life sciences researchers using Artificial Intelligence are under pressure to innovate faster than ever. Large, multilevel, and integrated datasets offer the promise of unlocking novel insights and accelerating breakthroughs. Although more data are available than ever, only a fraction is being curated, integrated, understood, and analyzed. Artificial Intelligence focuses on how computers learn from data and mimic human thought processes. Artificial Intelligence increases learning capacity and provides decision support system at scales that are transforming the future of healthcare. This article is a review of machine learning applications in healthcare with a focus on clinical, translational, and public health applications with an overview of the important role of privacy, data-sharing, and genetic information.

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Empirical study using network of semantically related associations in bridging the knowledge gap

Cited by 11 publications

References 22 publications

Towards Computational Assessment of Idea Novelty

Towards Computational Assessment of Idea Novelty

Machine Learning-Based Prediction of Stroke in Emergency Departments

Artificial Intelligence Transforms the Future of Health Care

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