scholarly journals Implant Term Extraction from Swedish Medical Records – Phase 1: Lessons Learned

2021 ◽  
Author(s):  
Oskar Jerdhaf ◽  
Marina Santini ◽  
Peter Lundberg ◽  
Anette Karlsson ◽  
Arne Jönsson
Keyword(s):  
At Risk ◽  
Medical Records ◽  
Learning Algorithm ◽  
Phase 1 ◽  
Lessons Learned ◽  
Automatic Identification ◽  
Deep Learning Algorithm ◽  
Term Extraction ◽  
Swedish Medical ◽  
Magnetic Resonance Imaging Mri

We present the case of automatic identification of “implant terms”. Implant terms are specialized terms that are important for domain experts (e.g. radiologists), but they are difficult to retrieve automatically because their presence is sparse. The need of an automatic identification of implant terms spurs from safety reasons because patients who have an implant may be at risk if they undergo Magnetic Resonance Imaging (MRI). At present, the workflow to verify whether a patient could be at risk of MRI side-effects is manual and laborious. We claim that this workflow can be sped up, streamlined and become safer by automatically sieving through patients’ medical records to ascertain if they have or have had an implant. To this aim we use BERT, a state-of-the-art deep learning algorithm based on pre-trained word embeddings and we create a model that outputs term clusters. We then assess the linguistic quality or term relatedness of individual term clusters using a simple intra-cluster metric that we call cleanliness. Results are promising.

scholarly journals Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7116
Author(s):  
Jia-Lien Hsu ◽  
Teng-Jie Hsu ◽  
Chung-Ho Hsieh ◽  
Anandakumar Singaravelan
Keyword(s):  
Medical Records ◽  
Learning Algorithm ◽  
Text Processing ◽  
Medical Center ◽  
Clinical Information ◽  
Self Report ◽  
Diagnostic Code ◽  
Evaluation Tool ◽  
Deep Learning Algorithm ◽  
International Statistical Classification

The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and reporting diseases, injuries, disorders, and health conditions. Most previously-proposed disease predicting systems need clinical information collected by the medical staff from the patients in hospitals. In this paper, we propose a deep learning algorithm to classify disease types and identify diagnostic codes by using only the subjective component of progress notes in medical records. In this study, we have a dataset, consisting of about one hundred and sixty-eight thousand medical records, from a medical center, collected during 2003 and 2017. First, we apply standard text processing procedures to parse the sentences and word embedding techniques for vector representations. Next, we build a convolution neural network model on the medical records to predict the ICD-9 code by using a subjective component of the progress note. The prediction performance is evaluated by ten-fold cross-validation and yields an accuracy of 0.409, recall of 0.409 and precision of 0.436. If we only consider the “chapter match” of ICD-9 code, our model achieves an accuracy of 0.580, recall of 0.580, and precision of 0.582. Since our diagnostic code prediction model is solely based on subjective components (mainly, patients’ self-report descriptions), the proposed approach could serve as a remote and self-diagnosis assistance tool, prior to seeking medical advice or going to the hospital. In addition, our work may be used as a primary evaluation tool for discomfort in the rural area where medical resources are restricted.

Exploration of Intelligent Development of Medical Heritage

2021 ◽  
Vol 105 ◽  
pp. 272-281
Author(s):  
Jing Hua Li ◽  
Ying Hui Wang ◽  
Zong You Li ◽  
Qi Yu ◽  
Ye Tian ◽  
...  
Keyword(s):  
Big Data ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Medical Records ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Information Barriers ◽  
Deep Learning Algorithm ◽  
Application Systems ◽  
Analysis Platform

With the rapid development of science and technology, more and more new methods and technologies have been added to the traditional Chinese Medicine Inheritance model, which makes the process of inheritance of famous doctors have more means, and the results of inheritance are more objective, rigorous and intelligent. In the process of inheriting the informationization of famous doctors, there are some bottlenecks, such as data acquisition difficulties, data processing difficulties, algorithm application difficulties, analysis and summary difficulties. Integration of artificial intelligence with big data, deep learning algorithm and knowledge atlas technology has brought technological innovation to the informationization of famous doctors' inheritance. Under this wave, the team of the Intelligent Research and Development Center of Traditional Chinese Medicine, Institute of Traditional Chinese Medicine Information, Chinese Academy of Traditional Chinese Medical Sciences, has developed a series of professional application systems in the field of traditional Chinese medicine around the planning of famous doctors' inheritance and excavation, and has developed ancient Chinese medicine, such as Today's Medical Records Cloud Platform, Medical Records Big Data Analysis Platform, Cloud Medical Records APP, Famous Medical Heritage Workstation. To a certain extent, it can solve the problems of inefficient collection of medical records, lack of objective data support and information barriers in the summary of famous doctors' experience under the limitation of traditional model, so as to promote the inheritance of famous doctors' experience and enhance the teaching ability and efficiency of teachers and apprentices.

scholarly journals Behavior Features for Automatic Detection of Depression from Facebook Users

2020 ◽  
Author(s):  
Siranuch Hemtanon ◽  
Saifon Aekwarangkoon ◽  
Nichnan Kittphattanabawon
Keyword(s):  
At Risk ◽  
Depressive Disorder ◽  
Learning Algorithm ◽  
Common Mental Disorders ◽  
Late Night ◽  
Deep Learning Algorithm ◽  
Standard Tool ◽  
Active Data ◽  
Detection Of Depression ◽  
F Measure

Major depressive disorder is one of common mental disorders globally. It is best to be early detected and cured. This work introduces a method to detect depressive disorder at risk via a behavior made on Facebook platform. A set of features related to Facebook main functions including amount of posting, sharing, commenting and replying is designed to represent users’ activities in a numerical value form. The collected data with periodic and consecutive aspects are gathered without interpreting content. Thus, the data are easier to be collected with less privacy issue. To distinct between positive and negative depression-at risk, PHQ-9 questionnaire, a standard tool commonly used to screen depression patient in Thailand, was used. These features hence are used in supervised learning classification algorithm for detecting a risk of being depressive disorder. From the experiment of 160 Thai Facebook users, the statistical result indicated that depression-positive users tend to do consecutive actions and rarely reply to other comments. Moreover, they often have activities in late night. The classification experiment shows that the designed features based on users’ activities from Facebook with deep learning algorithm yields about 87% in terms of F-measure. After analyzing the data, we thus split data regarding users’ gender and removed obviously low active data, and the F-measure from classification went up to 91.4 which improves for 4 points.

scholarly journals Identifying Periampullary Regions in MRI Images Using Deep Learning

2020 ◽  
Author(s):  
Yong Tang ◽  
Xinpei Chen ◽  
Weijia Wang ◽  
Jiali Wu ◽  
Yingjun Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Method ◽  
Training Set ◽  
Deep Learning Algorithm ◽  
Invasive Method ◽  
T1 And T2 ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set ◽  
Development And Validation

Abstract Background: Development and validation of a deep learning method to automatically segment the peri-ampullary (PA) region in magnetic resonance imaging (MRI) images. Methods: A group of patients with or without periampullary carcinoma (PAC) was included. The PA regions were manually annotated in MRI images by experts. Patients were randomly divided into one training set and one validation set. A deep learning method to automatically segment the PA region in MRI images was developed using the training set. The segmentation performance of the method was evaluated in the validation set. Results: The deep learning algorithm achieved optimal accuracies in the segmentation of the PA regions in both T1 and T2 MRI images. The value of the intersection over union (IoU) was 0.67 and 0.68 for T1 and T2 images, respectively. Conclusions: Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images. This automated non-invasive method helps clinicians to identify and locate the PA region using preoperative MRI scanning.

scholarly journals Identifying Periampullary Regions in MRI Images Using Deep Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Yong Tang ◽  
Yingjun Zheng ◽  
Xinpei Chen ◽  
Weijia Wang ◽  
Qingxi Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Test Set ◽  
Deep Learning Algorithm ◽  
Invasive Method ◽  
T1 And T2 ◽  
Magnetic Resonance Imaging Mri ◽  
Validation Set ◽  
Development And Validation ◽  
Human Assessment

BackgroundDevelopment and validation of a deep learning method to automatically segment the peri-ampullary (PA) region in magnetic resonance imaging (MRI) images.MethodsA group of patients with or without periampullary carcinoma (PAC) was included. The PA regions were manually annotated in MRI images by experts. Patients were randomly divided into one training set, one validation set, and one test set. Deep learning methods were developed to automatically segment the PA region in MRI images. The segmentation performance of the methods was compared in the validation set. The model with the highest intersection over union (IoU) was evaluated in the test set.ResultsThe deep learning algorithm achieved optimal accuracies in the segmentation of the PA regions in both T1 and T2 MRI images. The value of the IoU was 0.68, 0.68, and 0.64 for T1, T2, and combination of T1 and T2 images, respectively.ConclusionsDeep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images. This automated non-invasive method helps clinicians to identify and locate the PA region using preoperative MRI scanning.

scholarly journals Deep Learning-Based Crack Identification for Steel Pipelines by Extracting Features from 3D Shadow Modeling

2021 ◽  
Vol 11 (13) ◽  
pp. 6063
Author(s):  
Wael A. Altabey ◽  
Mohammad Noori ◽  
Tianyu Wang ◽  
Ramin Ghiasi ◽  
Sin-Chi Kuok ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Crack Identification ◽  
Automatic Identification ◽  
Light Sources ◽  
3D Images ◽  
Deep Learning Algorithm ◽  
Satisfactory Performance ◽  
A New Technique

Automatic crack identification for pipeline analysis utilizes three-dimensional (3D) image technology to improve the accuracy and reliability of crack identification. A new technique that integrates a deep learning algorithm and 3D shadow modeling (3D-SM) is proposed for the automatic identification of corrosion cracks in pipelines. Since the depth of a corrosion crack is below the surrounding area of the crack, a shadow of the crack is projected when the crack is exposed under light sources. In this study, we analyze the shadow areas of cracks through 3D shadow modeling (3D-SM) and identify the evolving cracks through the shape analysis of the shadows. To denoise the 3D images, the connected domain analysis is implemented so that the shadow groups of the evolving cracks can be retained and the scattered shadow groups that occur due to insignificant defects can be eliminated. Moreover, a novel deep neural network is developed to process the 3D images. The proposed automatic crack identification method successfully processes the 3D images efficiently and accurately diagnoses the corrosion cracks. Experimental results show that the proposed method achieves satisfactory performance with 93.53% accuracy and a 92.04% regression rate.

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