Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes

2017 ◽  
pp. 396-406 ◽  
Author(s):  
Olivia R. Zhang ◽  
Yaoyun Zhang ◽  
Jun Xu ◽  
Kirk Roberts ◽  
Xiang Y. Zhang ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Domain Knowledge ◽  
Clinical Notes

scholarly journals Interactive medical word sense disambiguation through informed learning

2018 ◽  
Vol 25 (7) ◽  
pp. 800-808 ◽  
Author(s):  
Yue Wang ◽  
Kai Zheng ◽  
Hua Xu ◽  
Qiaozhu Mei
Keyword(s):  
Active Learning ◽  
Learning Curve ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Interactive Learning ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
High Quality ◽  
Clinical Notes ◽  
Sense Disambiguation

Abstract Objective Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert’s domain knowledge in building high-quality medical WSD models with minimal human effort. Methods We developed an interactive learning algorithm with expert labeling instances and features. An expert can provide supervision in 3 ways: labeling instances, specifying indicative words of a sense, and highlighting supporting evidence in a labeled instance. The algorithm learns from these labels and iteratively selects the most informative instances to ask for future labels. Our evaluation used 3 WSD corpora: 198 ambiguous terms from Medical Subject Headings (MSH) as MEDLINE indexing terms, 74 ambiguous abbreviations in clinical notes from the University of Minnesota (UMN), and 24 ambiguous abbreviations in clinical notes from Vanderbilt University Hospital (VUH). For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy on the test set against the number of labeled instances was generated. The area under the learning curve was used as the primary evaluation metric. Results Our interactive learning algorithm significantly outperformed active learning, the previous fastest learning algorithm for medical WSD. Compared to active learning, it achieved 90% accuracy for the MSH corpus with 42% less labeling effort, 35% less labeling effort for the UMN corpus, and 16% less labeling effort for the VUH corpus. Conclusions High-quality WSD models can be efficiently trained with minimal supervision by inviting experts to label informative instances and provide domain knowledge through labeling/highlighting contextual features.

scholarly journals Medical code prediction via capsule networks and ICD knowledge

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Weidong Bao ◽  
Hongfei Lin ◽  
Yijia Zhang ◽  
Jian Wang ◽  
Shaowu Zhang
Keyword(s):  
Domain Knowledge ◽  
Routing Algorithm ◽  
Clinical Manifestations ◽  
International Classification Of Diseases ◽  
Dynamic Routing ◽  
Prediction Task ◽  
Clinical Notes ◽  
Classification Of Diseases ◽  
Label Information ◽  
Mimic Iii

Abstract Background Clinical notes record the health status, clinical manifestations and other detailed information of each patient. The International Classification of Diseases (ICD) codes are important labels for electronic health records. Automatic medical codes assignment to clinical notes through the deep learning model can not only improve work efficiency and accelerate the development of medical informatization but also facilitate the resolution of many issues related to medical insurance. Recently, neural network-based methods have been proposed for the automatic medical code assignment. However, in the medical field, clinical notes are usually long documents and contain many complex sentences, most of the current methods cannot effective in learning the representation of potential features from document text. Methods In this paper, we propose a hybrid capsule network model. Specifically, we use bi-directional LSTM (Bi-LSTM) with forwarding and backward directions to merge the information from both sides of the sequence. The label embedding framework embeds the text and labels together to leverage the label information. We then use a dynamic routing algorithm in the capsule network to extract valuable features for medical code prediction task. Results We applied our model to the task of automatic medical codes assignment to clinical notes and conducted a series of experiments based on MIMIC-III data. The experimental results show that our method achieves a micro F1-score of 67.5% on MIMIC-III dataset, which outperforms the other state-of-the-art methods. Conclusions The proposed model employed the dynamic routing algorithm and label embedding framework can effectively capture the important features across sentences. Both Capsule networks and domain knowledge are helpful for medical code prediction task.

Some Clinical Notes on Forced Stuttering

1972 ◽  
Vol 37 (2) ◽  
pp. 177-186 ◽  
Author(s):  
Oliver Bloodstein ◽  
Roberta Levy Shogan
Keyword(s):  
Loss Of Control ◽  
Clinical Notes ◽  
High Degree

Stutterers sometimes report that by exerting articulatory pressure they can force themselves to have “real” blocks. A procedure was devised for instructing subjects to force stuttering under various conditions and for recording their introspections. Most subjects were able to force at least a few blocks which they regarded as real. Most of the words on which the attempts were said to succeed were feared or difficult words, and at times subjects assisted the process by “telling” themselves that they would not be able to say the word. Fewer subjects were able to force blocks on isolated sounds than on words, and almost none claimed to succeed on mere articulatory contacts. Subjects repeatedly characterized “real” stuttering as involving feelings of physical tension and loss of control over speech. The nature of the forced block is discussed with reference to a concept of stuttering as a struggle reaction which has acquired a high degree of automaticity.

Automated Assessment of Domain Knowledge With Online Knowledge Mapping

2006 ◽  
Author(s):  
Gregory K. W. K. Chung ◽  
Eva L. Baker ◽  
David G. Brill ◽  
Ravi Sinha ◽  
Farzad Saadat ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Knowledge Mapping ◽  
Automated Assessment

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

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