Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype

Malik, Khalid Mahmood; Krishnamurthy, Madan; Alobaidi, Mazen; Hussain, Maqbool; Alam, Fakhare; Malik, Ghaus M.

doi:10.1016/j.eswa.2019.113120

Cited by 41 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decision tree model is unstable and inefficient in the prediction process of SAH. Malik et al [ 43 ] proposed a knowledge graph model for concept extraction, individual and relational feature analysis, and prediction process. Ensemble learning method based on skip-gram technique was applied to handle structured and unstructured data of patient records.…”

Section: Review Of Sah Prediction Modelsmentioning

confidence: 99%

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Sengupta

Alzbutas

2022

BioMed Research International

View full text Add to dashboard Cite

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.

show abstract

Section: Review Of Sah Prediction Modelsmentioning

confidence: 99%

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Sengupta

Alzbutas

2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…An accurate clinical summarization is expected to revolutionize the field of domain-specific knowledge graphs [ 57 ] and clinical decision support systems. For example, an individual PICO-extracted element from a biomedical text could be represented as a relationship in a knowledge graph, which could then be used for various clinical applications or could be directly supplied to clinical decision support systems for physicians to link to internal data-driven instances.…”

Section: Discussionmentioning

confidence: 99%

Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation

Afzal¹,

Alam²,

Malik³

et al. 2020

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

Background Automatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization. Objective Traditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs. Methods In our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context–aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context–aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score. Results Evaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context–aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory. Conclusions By employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.

show abstract

“…Therefore, there is a notable consensus in both industry and academia to consolidate efforts to overcome the challenges of this vital sector [46]. KGs offer the healthcare sector technical means to derive meaningful insights from voluminous and heterogeneous healthcare data [47,48]. For example, Rotmensch et al [49] constructed a KG that captures diseases and symptoms related entities form 273,174 electronic medical records.…”

Section: Healthcarementioning

confidence: 99%

Domain-specific knowledge graphs: A survey

Abu-Salih

2021

Journal of Network and Computer Applications

186

View full text Add to dashboard Cite

Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype

Cited by 41 publications

References 18 publications

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Clinical Context–Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation

Domain-specific knowledge graphs: A survey

Contact Info

Product

Resources

About