Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: A feasibility study

Sung, Sheng Feng; Chen, Kuanchin; Wu, Darren Philbert; Hung, Ling Chien; Su, Yu Hsiang; Hu, Yu

doi:10.1016/j.ijmedinf.2018.02.005

Cited by 34 publications

(26 citation statements)

References 42 publications

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“…The trained RNN was integrated into an NLP pipeline making use of a dictionary lookup approach to identify important concepts found in the text. In [51], an EHR interface was powered by NLP techniques, exploiting MetaMap, as a decision-making support for stroke patients candidate to Intravenous Thrombolytic Therapy (IVT).…”

Section: Applications Of Medical Guismentioning

confidence: 99%

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine

Rundo

Pirrone

Vitabile

et al. 2020

Journal of Biomedical Informatics

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The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes,

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Section: Applications Of Medical Guismentioning

confidence: 99%

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine

Rundo

Pirrone

Vitabile

et al. 2020

Journal of Biomedical Informatics

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“…The free context of medical data, medical notes and reports have given a new dimension to the text mining paradigm. The information retrieval represents document under scrutiny as a collection of predefined words however natural language processing takes this a step forward Latest research presented MetaMap that aimed to reduce the error rate by identifying eligibility for Intravenous Thrombolytic Therapy (IVT) in stroke patients using natural language processing [57]. MetaMap handicapped itself in the generalization of outcomes due to a small sample size and tend to acquire long processing time which makes it a hard choice for real-time large datasets.…”

Section: A Natural Language Processing (Nlp)mentioning

confidence: 99%

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Raja¹,

Asghar²

2020

IJACSA

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Clinical advancements are one of the major outcomes of the technological phase shift of data sciences. The significance of information technology in medical sciences by utilizing the Clinical Decision Support System (CDSS) has opened the spillways of exponentially improved predictive models. Utilizing the latest norms of classification algorithms on clinical data are widely incorporated for prognostic assessments. Medical experts have to make decisions that are crucial in nature and if the research can develop a mechanism that assists them in evolving solid reasoning, infer the knowledge and clearly express their clinical decision by justifying their assertions made, it will be a win-win situation. However, this field of science is still an unknown world for clinicians despite the fact that the enormous amount of medical data cannot be exploited to its maximum without invoking the technological support. The objective of this research is to introduce the clinicians and policymakers of the medical domain with the renowned computer-based methodologies employed to construct a clinical decision support system. We expect that gaining the technical insight into the medical domain by the stakeholders will ensure commissioning the accurate and effective CDSS for improved healthcare delivery.

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“…NLP has been actively studied in analyses of unstructured text data, which accounts for a large portion of the medical records such as admission notes, nursing records and discharge summaries [10, 11]. NLP tools can be applied in a rule-based fashion to parse out the meaning of texts, although they are employing both supervised and unsupervised machine learning (ML) algorithms [12] Prior stroke research includes feasibility studies of NLP for predicting a future stroke [13], extracting risk factor information [14], and timely screening for urgent thrombolysis [15]. In addition, several reports have used NLP to predict the progression of cancer or to classify breast pathology by analyzing free text radiology reports [16, 17].…”

Section: Introductionmentioning

confidence: 99%

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke

et al. 2019

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Background and purpose This project assessed performance of natural language processing (NLP) and machine learning (ML) algorithms for classification of brain MRI radiology reports into acute ischemic stroke (AIS) and non-AIS phenotypes. Materials and methods All brain MRI reports from a single academic institution over a two year period were randomly divided into 2 groups for ML: training (70%) and testing (30%). Using “quanteda” NLP package, all text data were parsed into tokens to create the data frequency matrix. Ten-fold cross-validation was applied for bias correction of the training set. Labeling for AIS was performed manually, identifying clinical notes. We applied binary logistic regression, naïve Bayesian classification, single decision tree, and support vector machine for the binary classifiers, and we assessed performance of the algorithms by F1-measure. We also assessed how n-grams or term frequency-inverse document frequency weighting affected the performance of the algorithms. Results Of all 3,204 brain MRI documents, 432 (14.3%) were labeled as AIS. AIS documents were longer in character length than those of non-AIS (median [interquartile range]; 551 [377–681] vs. 309 [164–396]). Of all ML algorithms, single decision tree had the highest F1-measure (93.2) and accuracy (98.0%). Adding bigrams to the ML model improved F1-mesaure of naïve Bayesian classification, but not in others, and term frequency-inverse document frequency weighting to data frequency matrix did not show any additional performance improvements. Conclusions Supervised ML based NLP algorithms are useful for automatic classification of brain MRI reports for identification of AIS patients. Single decision tree was the best classifier to identify brain MRI reports with AIS.

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Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: A feasibility study

Cited by 34 publications

References 42 publications

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine

Beyond the Horizon: A Meticulous Analysis of Clinical Decision-Making Practices

Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke

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