Comprehend Medical: A Named Entity Recognition and Relationship Extraction Web Service

Bhatia, Parminder; Celikkaya, Busra; Khalilia, Mohammed; Senthivel, Selvan

doi:10.1109/icmla.2019.00297

Cited by 44 publications

(28 citation statements)

References 41 publications

(51 reference statements)

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“…The proposed model provides sequential hourly predictions for sepsis using data only available at or prior to the prediction time, and as such can be deployed prospectively. We also consider a baseline model by constructing a feature vector that includes frequency of occurrence of key terms related to diagnoses and drugs extracted using a commercially available clinical NLP tool (ACM) 24 . After excluding terms with abnormally high or low frequency 18,25 , a term-frequency matrix was constructed using 2187 unique medical terms.…”

Section: Methodsmentioning

confidence: 99%

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis

Amrollahi

Shashikumar

Razmi

et al. 2021

Preprint

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Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading causes of morbidity and mortality in hospitals. Early detection of sepsis and timely antibiotics administration is known to save lives. In this work, we design a sepsis prediction algorithm based on data from electronic health records (EHR) using a deep learning approach. While most existing EHR-based sepsis prediction models utilize structured data including vitals, labs, and clinical information, we show that incorporation of features based on clinical texts, using a pre-trained neural language representation model, allows for incorporation of unstructured data without an explicit need for ontology-based named-entity recognition and classification. The proposed model is trained on a large critical care database of over 40,000 patients, including 2805 septic patients, and is compared against competing baseline models. In comparison to a baseline model based on structured data alone, incorporation of clinical texts improved AUC from 0.81 to 0.84. Our findings indicate that incorporation of clinical text features via a pre-trained language representation model can improve early prediction of sepsis and reduce false alarms.

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Section: Methodsmentioning

confidence: 99%

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis

Amrollahi

Shashikumar

Razmi

et al. 2021

Preprint

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“…This tool was developed by National Library of Medicine (NLP) in order to map biomedical text to concepts in the Unified Medical Language System (UMLS). To implement the mapping, MM uses a hybrid approach which combines a knowledgeintensive approach, natural language processing (NLP) and computational linguistic techniques [6,7]. Amazon Comprehend Medical has been released by Amazon Web Service (AWS) in 2018 to automatically extract clinical concepts from clinical notes.…”

Section: Methodsmentioning

confidence: 99%

Entity Extraction for Clinical Notes, a Comparison Between MetaMap and Amazon Comprehend Medical

Shah-Mohammadi

Cui

Finkelstein

2021

Studies in Health Technology and Informatics

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Extracting meaningful information from clinical notes is challenging due to their semi- or unstructured format. Clinical notes such as discharge summaries contain information about diseases, their risk factors, and treatment approaches associated to them. As such, it is critical for healthcare quality as well as for clinical research to extract those information and make them accessible to other computerized applications that rely on coded data. In this context, the goal of this paper is to compare the automatic medical entity extraction capacity of two available entity extraction tools: MetaMap (MM) and Amazon Comprehend Medical (ACM). Recall, precision and F-score have been used to evaluate the performance of the tools. The results show that ACM achieves higher average recall, average precision, and average F-score in comparison with MM.

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“…Descriptions can often be verbose and can contain a large amount of noise. To improve the robustness and reduce noise, we have incorporated medical entity based hard attention [4] using Amazon Web Services Comprehend Medical (CM) [5] which is a natural language processing service to perform entity and relation extraction.…”

Section: Entity Boosted Two-tower Neural Networkmentioning

confidence: 99%

Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network

Yuan

Bhatia

Celikkaya

et al. 2021

Communications in Computer and Information Science

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Recent advancements in medical entity linking have been applied in the area of scientific literature and social media data. However, with the adoption of telemedicine and conversational agents such as Alexa in healthcare settings, medical name inference has become an important task. Medication name inference is the task of mapping user friendly medication names from a free-form text to a concept in a normalized medication list. This is challenging due to the differences in the use of medical terminology from health care professionals and user conversations coming from the lay public. We begin with mapping descriptive medication phrases (DMP) to standard medication names (SMN). Given the prescriptions of each patient, we want to provide them with the flexibility of referring to the medication in their preferred ways. We approach this as a ranking problem which maps SMN to DMP by ordering the list of medications in the patient's prescription list obtained from pharmacies. Furthermore, we leveraged the output of intermediate layers and performed medication clustering. We present the Medication Inference Model (MIM) achieving state-of-the-art results. By incorporating medical entities based attention, we have obtained further improvement for ranking models.

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Comprehend Medical: A Named Entity Recognition and Relationship Extraction Web Service

Cited by 44 publications

References 41 publications

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis

Entity Extraction for Clinical Notes, a Comparison Between MetaMap and Amazon Comprehend Medical

Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network

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