Combined SNA and LDA methods to understand adverse medical events

Zhu, Lin; Reychav, Iris; McHaney, Roger; Broda, Aric; Tal, Yossi; Manor, Orly

doi:10.3233/jrs-180052

Cited by 7 publications

(4 citation statements)

References 66 publications

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“…The current research suggests that it is feasible to recognize and intelligently diagnose the preprocessed data of the underlying devices of the medical Internet of Things with CNN model. In comparison with traditional machine learning methods such as SVM [ 22 ] and LDA [ 23 ], the depth learning method for intelligent diagnosis does not require manual selection of data feature values. The depth model can automatically extract features and perform high-level abstraction.…”

Section: Resultsmentioning

confidence: 99%

Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

Guo

Yang

et al. 2021

Journal of Healthcare Engineering

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Introduction. Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward. Methods. Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set. Results. Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%. Conclusion. The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

Guo

Yang

et al. 2021

Journal of Healthcare Engineering

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“…Selvi et al [36] used the LDA model to generate a topic model based on the classification of medical datasets. Zhu et al [37] used the social network analysis (SNA) to describe the main keyword of adverse medical events and the LDA to investigate topics of different hazard levels. They combined SNA and LDA to detect common topic keywords.…”

Section: Lda Modelmentioning

confidence: 99%

KTI-RNN: Recognition of Heart Failure from Clinical Notes

et al. 2023

Tsinghua Sci. Technol.

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Although deep learning methods have recently attracted considerable attention in the medical field, analyzing large-scale electronic health record data is still a difficult task. In particular, the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment decisions. This study uses data from the Medical Information Mart for Intensive Care database. Compared with structured data, unstructured data contain abundant patient information. However, this type of data has unsatisfactory characteristics, e.g., many colloquial vocabularies and sparse content. To solve these problems, we propose the KTI-RNN model for unstructured data recognition. The proposed model overcomes sparse content and obtains good classification results. The term frequency-inverse word frequency (TF-IWF) model is used to extract the keyword set. The latent dirichlet allocation (LDA) model is adopted to extract the topic word set. These models enable the expansion of the medical record text content. Finally, we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network (BiRNN) model and the output layer. We call it gated-attention-BiRNN (GA-BiRNN) and use it to identify heart failure from extensive medical texts. Results show that the F 1 score of the proposed KTI-RNN model is 85.57%, and the accuracy rate of the proposed KTI-RNN model is 85.59%.

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“…14 One particular unsupervised topic modelling method, latent Dirichlet allocation (LDA), 15 has proven particularly popular and successful. LDA has been used for topic mining in studies of health data across an array of data sources, including discussions from condition-specific online support groups [16][17][18][19][20] and more general online discussion platforms, [21][22][23][24][25][26][27][28][29] data about adverse medical events, 30 interview transcripts of patients, 31 32 media articles 33 and survey data. 34 35 Other studies have used LDA to analyse topics in patient-reported concerns as well, in situations where no existing topic information is available.…”

Section: What Does This Paper Add?mentioning

confidence: 99%

Categorising patient concerns using natural language processing techniques

Fairie

Zhang

D’Souza

et al. 2021

BMJ Health Care Inform

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ObjectivesPatient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.MethodsPatient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.ResultsThe LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).DiscussionLDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.ConclusionOur findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.

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Combined SNA and LDA methods to understand adverse medical events

Cited by 7 publications

References 66 publications

Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

KTI-RNN: Recognition of Heart Failure from Clinical Notes

Categorising patient concerns using natural language processing techniques

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