Coreference resolution of medical concepts in discharge summaries by exploiting contextual information

Dai, Hong Jie; Chen, Chun Yu; Wu, Chi Yang; Lai, Po Ting; Tsai, Richard Tzong-Han; Hsu, Wen Lian

doi:10.1136/amiajnl-2012-000808

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Detailed medical information and relationship between text instances were extracted as a co-reference resolution from a patient record summary in [28]. Three NLP systems employed to resolve phrased which refer to the same entity, i.e.…”

Section: A Rule-based Methodsmentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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Electronic health records are used to extract patient's information instantly and remotely, which can help to keep track of patients' due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using 'HIPAA Safe Harbor' technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

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Section: A Rule-based Methodsmentioning

confidence: 99%

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Latif

Xiao

et al. 2020

IEEE Access

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“…The pipeline integrates an entity recognition system developed in our previous works [22, 23] and systems developed for recognizing protected health information (PHI) and classifying symptom severity of a patient in the CEGS N-GRID 2016 shared task [24]. Figure 2 illustrates the overall text mining pipeline.…”

Section: Methodsmentioning

confidence: 99%

“…Various medical entities were extracted using rules and machine learning models established in our previous works [22, 23, 25, 26]. The entities recognized by our pipeline include age, gender, lab values such as glycated hemoglobin, glucose, cholesterol, blood pressure, body mass index, and other heart disease-related risk factors.…”

Section: Methodsmentioning

confidence: 99%

Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients

Dai

Uddin

et al. 2017

Journal of Biomedical Informatics

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Evidence has revealed interesting associations of clinical and social parameters with violent behaviors of patients with psychiatric disorders. Men are more violent preceding and during hospitalization, whereas women are more violent than men throughout the 3 days following a hospital admission. It has also been proven that mental disorders may be a consistent risk factor for the occurrence of violence. In order to better understand violent behaviors of patients with psychiatric disorders, it is important to investigate both the clinical symptoms and psychosocial factors that accompany violence in these patients. In this study, we utilized a dataset released by the Partners Healthcare and Neuropsychiatric Genome-scale and RDoC Individualized Domains project of Harvard Medical School to develop a unique text mining pipeline that processes unstructured clinical data in order to recognize clinical and social parameters such as age, gender, history of alcohol use, violent behaviors, etc., and explored the associations between these parameters and violent behaviors of patients with psychiatric disorders. The aim of our work was to demonstrate the feasibility of mining factors that are strongly associated with violent behaviors among psychiatric patients from unstructured psychiatric evaluation records using clinical text mining. Experiment results showed that stimulants, followed by a family history of violent behavior, suicidal behaviors, and financial stress were strongly associated with violent behaviors. Key aspects explicated in this paper include employing our text mining pipeline to extract clinical and social factors linked with violent behaviors, generating association rules to uncover possible associations between these factors and violent behaviors, and lastly the ranking of top rules associated with violent behaviors using statistical analysis and interpretation.

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“…It can improve classification accuracy over non-collective methods when instances are interrelated [ 15 - 17 ]. MLN performs well in many collective classification tasks such as entity linking [ 18 - 20 ], coreference resolution [ 21 , 22 ] and biomedical event extraction [ 23 ]. In node-by-node SRL, related instances are nodes having linguistic dependencies.…”

Section: Methodsmentioning

confidence: 99%

A resource-saving collective approach to biomedical semantic role labeling

Tsai¹,

Lai

2014

BMC Bioinformatics

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BackgroundBiomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem. However, in BioSRL such an approach has not been attempted because it would require more training data to recognize the more specialized and diverse terms found in biomedical literature, increasing training time and computational complexity.ResultsWe first constructed a collective BioSRL system based on MLN. This system, called collective BIOSMILE (CBIOSMILE), is trained on the BioProp corpus. To reduce the resources used in BioSRL training, we employ a tree-pruning filter to remove unlikely nodes from the parse tree and four argument candidate identifiers to retain candidate nodes in the tree. Nodes not recognized by any candidate identifier are discarded. The pruned annotated parse trees are used to train a resource-saving MLN-based system, which is referred to as resource-saving collective BIOSMILE (RCBIOSMILE). Our experimental results show that our proposed CBIOSMILE system outperforms BIOSMILE, which is the top BioSRL system. Furthermore, our proposed RCBIOSMILE maintains the same level of accuracy as CBIOSMILE using 92% less memory and 57% less training time.ConclusionsThis greatly improved efficiency makes RCBIOSMILE potentially suitable for training on much larger BioSRL corpora over more biomedical domains. Compared to real-world biomedical corpora, BioProp is relatively small, containing only 445 MEDLINE abstracts and 30 event triggers. It is not large enough for practical applications, such as pathway construction. We consider it of primary importance to pursue SRL training on large corpora in the future.

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Coreference resolution of medical concepts in discharge summaries by exploiting contextual information

Cited by 9 publications

References 13 publications

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review

Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients

A resource-saving collective approach to biomedical semantic role labeling

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