Automatic and Explainable Labeling of Medical Event Logs With Autoencoding

Oliveira, Hugo De; Augusto, Vincent; Jouaneton, B.; Lamarsalle, Ludovic; Prodel, Martin; Xie, Xiaolan

doi:10.1109/jbhi.2020.3021790

Cited by 8 publications

(10 citation statements)

References 23 publications

(30 reference statements)

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“…Finally, Anjewierden and Kabel 8 proposed an ontology-based method for automatic labeling. Hugo 9 proposed an innovative method to address the complexity of events in medical event logs. Based on automatic labeling, similar events are clustered in potential space to create accurate labels.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on automatic labeling of imbalanced texts of customer complaints based on text enhancement and layer-by-layer semantic matching

Tang

Mou

Liu

et al. 2021

Sci Rep

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Due to its potential impact on business efficiency, automated customer complaint labeling and classification are of great importance for management decision making and business applications. The majority of the current research on automated labeling uses large and well-balanced datasets. However, customer complaint labels are hierarchical in structure, with many labels at the lowest hierarchy level. Relying on lower-level labels leads to small and imbalanced samples, thus rendering the current automatic labeling practices inapplicable to customer complaints. This article proposes an automatic labeling model incorporating the BERT and word2vec methods. The model is validated on electric utility customer complaint data. Within the model, the BERT method serves to obtain shallow text tags. Furthermore, text enhancement is used to mitigate the problem of imbalanced samples that emerge when the number of labels is large. Finally, the word2vec model is utilized for deep text analysis. Experiments demonstrate the proposed model's efficiency in automating customer complaint labeling. Consequently, the proposed model supports enterprises in improving their service quality while simultaneously reducing labor costs.

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Section: Literature Reviewmentioning

confidence: 99%

Research on automatic labeling of imbalanced texts of customer complaints based on text enhancement and layer-by-layer semantic matching

Tang

Mou

Liu

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Finally, Anjewierden and Kabel [8] proposed an ontology-based method for automatic labeling. Hugo [9]proposed an innovative method to deal with the complexity of events in medical event log. Based on automatic labeling, similar events are clustered in potential space to create accurate label.…”

Section: Auto-labeling and Auto-indexingmentioning

confidence: 99%

Research on Automatic Labelling of Imbalanced Texts of Customer Complaints Based on Text Enhancement and Layer-By-Layer Semantic Matching

TANG

MOU

LIU

et al. 2021

Preprint

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[Purpose/meaning] Due to its potential impact on business efficiency, automated customer complaint labeling and classification are of great importance for management decisions and business-level applications. The majority of the current research on automated labeling uses large and well-balanced datasets. However, customer complaints' labels are hierarchical in structure, with many labels at the lowest hierarchy level. Relying on lower-level labels leads to small and imbalanced samples, thus rendering the current automatic labeling practices not applicable to customer complaints. [Methodology/process] This article proposes an automatic labeling model incorporating the BERT and Word2Vec methods. The model is validated on electric utility customer complaints data. Within the model, the BERT method serves to obtain shallow-level text tags. Further, text enhancement is used to mitigate the problem of uneven samples that emerges when the number of labels is large. Finally, the Word2Vec model is utilized for the deep-level text analysis. [Findings/conclusions] The experiments demonstrate the proposed model's efficiency in automating customer complaint labeling. Consequently, the proposed model supports the enterprises in improving their service quality while simultaneously reducing labor costs.

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“…From these literature review papers, we identified 5 papers related to Markov models, i.e., [ 10 , 13 , 34 , 38 , 40 ]. Through the second search, we used “Process mining” AND healthcare AND markov as the search keyword based on which we found 4 papers among which 3 were peered reviewed, i.e., [ 3 , 15 , 51 ]. Here, we summarize these 8 papers that we found.…”

Section: Introductionmentioning

confidence: 99%

“…Oliveira [ 15 ] proposes a new methodology to deal with the complexity of medical event logs. This paper is appeared by searching “Process mining” AND healthcare AND markov in Abstracts in Google Scholar.…”

Section: Introductionmentioning

confidence: 99%

“…To sum up, several case studies show the practical importance of using Markov models in the healthcare domain [ 3 , 10 , 15 , 34 , 40 ] among which [ 40 ] shows the importance of discovering process variants and the practical significance of comparing them. Currently, no tool is available to researchers to enable comparing process variants using Markov models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

dfgcompare: a library to support process variant analysis through Markov models

Jalali

Johannesson

Perjons

et al. 2021

BMC Med Inform Decis Mak

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Background Data-driven process analysis is an important area that relies on software support. Process variant analysis is a sort of analysis technique in which analysts compare executed process variants, a.k.a. process cohorts. This comparison can help to identify insights for improving processes. There are a few software supports to enable process cohort comparison based on the frequencies of process activities and performance metrics. These metrics are effective in cohort analysis, but they cannot support cohort comparison based on the probability of transitions among states, which is an important enabler for cohort analysis in healthcare. Results This paper defines an approach to compare process cohorts using Markov models. The approach is formalized, and it is implemented as an open-source python library, named dfgcompare. This library can be used by other researchers to compare process cohorts. The implementation is also used to compare caregivers’ behavior when prescribing drugs in the Stockholm Region. The result shows that the approach enables the comparison of process cohorts in practice. Conclusions We conclude that dfgcompare supports identifying differences among process cohorts.

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Automatic and Explainable Labeling of Medical Event Logs With Autoencoding

Cited by 8 publications

References 23 publications

Research on automatic labeling of imbalanced texts of customer complaints based on text enhancement and layer-by-layer semantic matching

Research on automatic labeling of imbalanced texts of customer complaints based on text enhancement and layer-by-layer semantic matching

Research on Automatic Labelling of Imbalanced Texts of Customer Complaints Based on Text Enhancement and Layer-By-Layer Semantic Matching

dfgcompare: a library to support process variant analysis through Markov models

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