Electronic health record systems: A current and future-oriented view

Piliouras, Teresa; Yu, Pui Lam; Tian, Xin; Zuo, Bowen; Yu, Stephen; Paulino, Jennifer; Mei, Cindy; Clerger, Evodie; Davis, Devlina; Sultana, Nadia

doi:10.1109/lisat.2013.6578225

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…The electronic medical records (EMRs) are a vast repository of data maintained by healthcare professionals over a long period containing vital information about a patient in structured and unstructured formats. They include patients' medical conditions, treatments, progress notes, physical condition, prognostic and diagnostic procedures, past medications, immunization, lab reports, discharge indications, and persistence of any other medical problems [1]. Artificial intelligence (AI), particularly natural language processing (NLP), helps discover various associations among these parameters, providing clinicians with opportunities to improve treatment outcomes and administer systematic medical delivery [2].…”

Section: Introductionmentioning

confidence: 99%

Bi-directional long short term memory using recurrent neural network for biological entity recognition

Siddalingappa

Sekar

2022

IJ-AI

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<p>Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent neural network. Unlike existing approaches, the proposed method uses bidirectional traversing with GloVe vector modelling performed at character and word levels. Bio-NER is performed in three stages; firstly, the relevant medical entities in electronic medical records from PubMed were extracted using the skip-gram model. Secondly, a vector representation for each word is created through the 1-hot method. Thirdly, the weights of the recurrent neural network (RNN) layers are adjusted using backward propagation. Finally, the long-short-term memory cells store the previously encountered medical entity to tackle context-dependency. The accuracy and F-score are calculated for each medical entity type. The MacroR, MacroP, and MacroF are equal to 0.86, 0.88, and 0.87. The overall accuracy achieved was 94%.</p>

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Section: Introductionmentioning

confidence: 99%