Prediction of the breakthrough cancer pain using deep learning model.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e24085-e24085
Author(s):  
Yeong Hak Bang ◽  
Yoon Ho Choi ◽  
Mincheol Park ◽  
Geon Hee Lee ◽  
Soo-yong Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Pain ◽  
Rating Scale ◽  
Numerical Rating Scale ◽  
Onset Time ◽  
Learning Model ◽  
Solid Cancer ◽  
Test Dataset ◽  
Breakthrough Cancer Pain ◽  
Deep Learning Model

e24085 Background: Breakthrough cancer pain (BTcP), a transitory flare of pain that occurs on a background of relatively well-controlled baseline pain, is a challenging clinical problem in managing cancer pain. We hypothesized that the BTcP could be predictable according to the patients’ previous observed patterns. In this study, we report on the development of a deep learning model that predicts hourly individual-level breakthrough pain for patients with cancer. Methods: We defined the BTcP as the pain with numerical rating scale (NRS) score 4 or above and developed models predicting the onset time of BTcP with the temporal resolution of 1 hour. The datasets which have more than 20 records of NRS score during hospitalization were included in our study. All the pain records were obtained from patients hospitalized on the wards of hematology-oncology in Samsung Medical Center between July 2016 to February 2020. The model used the time windows of 3 days to predict NRS scores over the next 24 hours. To capture irregular pain patterns, we created the sequence of average pain patterns over 24 hours from the previous 3 days and used it for normalization. We trained a Bi-directional long-short term memory (LSTM) based deep learning model. The model was validated using the holdout method with 20% of the datasets. Its performance was assessed with the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AURPC). Results: We included pain log data containing 2,905 admissions from 2,176 patients with solid cancer and 1,755 admissions from 1,082 patients with hematologic cancer in the analysis. The median age was 57 (interquartile range (IQR), 47-64), the most frequent type of cancer was lung cancer (18.0%), and most patients had stage 4 (60.7%). Among the 103,948 hours from patients in whole datasets, 1,091 (4.7%) hours were labeled as the period of BTcP. The patients have the records of NRS score with a median of 3 (IQR, 2.0-4.5) and BTcP with a median of 1.1 (IQR, 0.5-2.0) per day. We allocated approximately 20% of patients (653 patients with 932 admissions) to the holdout test dataset. Our model showed the AUROC 0.719 and AUPRC 0.680 for predicting the BTcP in the test dataset. Conclusions: Our study showed that cancer pain could be predictive by using a deep learning model. Though our exploratory study has a limitation of generalizability, future warranted subgroup analysis and verification research could make our model more applicable in a real-world setting.

scholarly journals NIMG-08. PREDICTION OF LOWER-GRADE GLIOMA MOLECULAR SUBTYPES USING DEEP LEARNING

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii148-ii148
Author(s):  
Yoshihiro Muragaki ◽  
Yutaka Matsui ◽  
Takashi Maruyama ◽  
Masayuki Nitta ◽  
Taiichi Saito ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Molecular Subtype ◽  
Learning Model ◽  
Group Classification ◽  
Training Dataset ◽  
Lower Grade ◽  
Test Dataset ◽  
Ct Data ◽  
Deep Learning Model

Abstract INTRODUCTION It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively. METHODS A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients. RESULTS The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly. CONCLUSIONS AND FUTURE PERSPECTIVE A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype. We plan to apply the techniques of heat map and/or segmentation for an increase in prediction accuracy.

scholarly journals Identifying the Medical Lethality of Suicide Attempts Using Network Analysis and Deep Learning: Nationwide Study

10.2196/14500 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e14500
Author(s):  
Bora Kim ◽  
Younghoon Kim ◽  
C Hyung Keun Park ◽  
Sang Jin Rhee ◽  
Young Shin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Rating Scale ◽  
Suicide Attempts ◽  
Preventive Intervention ◽  
Learning Model ◽  
Psychiatric Evaluation ◽  
Medical Outcomes ◽  
Deep Learning Model

Background Suicide is one of the leading causes of death among young and middle-aged people. However, little is understood about the behaviors leading up to actual suicide attempts and whether these behaviors are specific to the nature of suicide attempts. Objective The goal of this study was to examine the clusters of behaviors antecedent to suicide attempts to determine if they could be used to assess the potential lethality of the attempt. To accomplish this goal, we developed a deep learning model using the relationships among behaviors antecedent to suicide attempts and the attempts themselves. Methods This study used data from the Korea National Suicide Survey. We identified 1112 individuals who attempted suicide and completed a psychiatric evaluation in the emergency room. The 15-item Beck Suicide Intent Scale (SIS) was used for assessing antecedent behaviors, and the medical outcomes of the suicide attempts were measured by assessing lethality with the Columbia Suicide Severity Rating Scale (C-SSRS; lethal suicide attempt >3 and nonlethal attempt ≤3). Results Using scores from the SIS, individuals who had lethal and nonlethal attempts comprised two different network nodes with the edges representing the relationships among nodes. Among the antecedent behaviors, the conception of a method’s lethality predicted suicidal behaviors with severe medical outcomes. The vectorized relationship values among the elements of antecedent behaviors in our deep learning model (E-GONet) increased performances, such as F1 and area under the precision-recall gain curve (AUPRG), for identifying lethal attempts (up to 3% for F1 and 32% for AUPRG), as compared with other models (mean F1: 0.81 for E-GONet, 0.78 for linear regression, and 0.80 for random forest; mean AUPRG: 0.73 for E-GONet, 0.41 for linear regression, and 0.69 for random forest). Conclusions The relationships among behaviors antecedent to suicide attempts can be used to understand the suicidal intent of individuals and help identify the lethality of potential suicide attempts. Such a model may be useful in prioritizing cases for preventive intervention.

Deep Learning Model for Epileptic Seizure Prediction

2021 ◽  
Vol 11 (12) ◽  
pp. 3199-3208
Author(s):  
K. Ganapriya ◽  
N. Uma Maheswari ◽  
R. Venkatesh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Normal Values ◽  
Learning Model ◽  
Seizure Prediction ◽  
Test Dataset ◽  
Time Stamps ◽  
Explained Variance ◽  
Deep Learning Model

Prediction of occurrence of a seizure would be of greater help to make necessary precaution for taking care of the patient. A Deep learning model, recurrent neural network (RNN), is designed for predicting the upcoming values in the EEG values. A deep data analysis is made to find the parameter that could best differentiate the normal values and seizure values. Next a recurrent neural network model is built for predicting the values earlier. Four different variants of recurrent neural networks are designed in terms of number of time stamps and the number of LSTM layers and the best model is identified. The best identified RNN model is used for predicting the values. The performance of the model is evaluated in terms of explained variance score and R2 score. The model founds to perform well number of elements in the test dataset is minimal and so this model can predict the seizure values only a few seconds earlier.

scholarly journals Reliability of Machine and Human Examiners for Detection of Laryngeal Penetration or Aspiration in Videofluoroscopic Swallowing Studies

2021 ◽  
Vol 10 (12) ◽  
pp. 2681
Author(s):  
Yuna Kim ◽  
Hyun-Il Kim ◽  
Geun-Seok Park ◽  
Seo-Young Kim ◽  
Sang-Il Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Computer Assisted ◽  
Intrarater Reliability ◽  
Rater Reliability ◽  
Test Dataset ◽  
Video Images ◽  
Laryngeal Penetration ◽  
Assisted Analysis ◽  
Deep Learning Model

Computer-assisted analysis is expected to improve the reliability of videofluoroscopic swallowing studies (VFSSs), but its usefulness is limited. Previously, we proposed a deep learning model that can detect laryngeal penetration or aspiration fully automatically in VFSS video images, but the evidence for its reliability was insufficient. This study aims to compare the intra- and inter-rater reliability of the computer model and human raters. The test dataset consisted of 173 video files from which the existence of laryngeal penetration or aspiration was judged by the computer and three physicians in two sessions separated by a one-month interval. Intra- and inter-rater reliability were calculated using Cohen’s kappa coefficient, the positive reliability ratio (PRR) and the negative reliability ratio (NRR). Intrarater reliability was almost perfect for the computer and two experienced physicians. Interrater reliability was moderate to substantial between the model and each human rater and between the human raters. The average PRR and NRR between the model and the human raters were similar to those between the human raters. The results demonstrate that the deep learning model can detect laryngeal penetration or aspiration from VFSS video as reliably as human examiners.

scholarly journals Deep Learning for Early Dental Caries Detection in Bitewing Radiographs

2021 ◽  
Author(s):  
Shinae Lee ◽  
Sang-il Oh ◽  
Junik Jo ◽  
Sumi Kang ◽  
Yooseok Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dental Caries ◽  
Diagnostic Performance ◽  
Preventive Treatment ◽  
Learning Model ◽  
Caries Detection ◽  
Test Dataset ◽  
Accurate Performance ◽  
Deep Cnn ◽  
Deep Learning Model

Abstract The early detection of incipient dental caries enables preventive treatment, and bitewing radiography is a good diagnostic tool for posterior incipient caries. In the field of medical imaging, the utilization of deep learning with convolutional neural networks (CNNs) to process various types of images has been actively researched and has shown promising performance. In this study, we developed a CNN model using a U-shaped deep CNN (U-Net) for dental caries detection on bitewing radiographs and investigated whether this model can improve clinicians’ performance. In total, 304 bitewing radiographs were used to train the deep learning model and 50 radiographs were used for performance evaluation. The diagnostic performance of the CNN model on the total test dataset was as follows: precision, 63.29%; recall, 65.02%; and F1-score, 64.14%, showing quite accurate performance. When three dentists detected dental caries using the results of the CNN model as reference data, the overall diagnostic performance of all three clinicians significantly improved, as shown by an increased recall ratio (D1, 85.34%; D1', 92.15%; D2, 85.86%; D2', 93.72%; D3, 69.11%; D3', 79.06%). These increases were especially significant in the incipient and moderate caries subgroups. The deep learning model may help clinicians to diagnose dental caries more accurately.

scholarly journals Identifying the Medical Lethality of Suicide Attempts Using Network Analysis and Deep Learning: Nationwide Study (Preprint)

2019 ◽  
Author(s):  
Bora Kim ◽  
Younghoon Kim ◽  
C Hyung Keun Park ◽  
Sang Jin Rhee ◽  
Young Shin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Rating Scale ◽  
Suicide Attempts ◽  
Preventive Intervention ◽  
Learning Model ◽  
Psychiatric Evaluation ◽  
Medical Outcomes ◽  
Deep Learning Model

BACKGROUND Suicide is one of the leading causes of death among young and middle-aged people. However, little is understood about the behaviors leading up to actual suicide attempts and whether these behaviors are specific to the nature of suicide attempts. OBJECTIVE The goal of this study was to examine the clusters of behaviors antecedent to suicide attempts to determine if they could be used to assess the potential lethality of the attempt. To accomplish this goal, we developed a deep learning model using the relationships among behaviors antecedent to suicide attempts and the attempts themselves. METHODS This study used data from the Korea National Suicide Survey. We identified 1112 individuals who attempted suicide and completed a psychiatric evaluation in the emergency room. The 15-item Beck Suicide Intent Scale (SIS) was used for assessing antecedent behaviors, and the medical outcomes of the suicide attempts were measured by assessing lethality with the Columbia Suicide Severity Rating Scale (C-SSRS; lethal suicide attempt >3 and nonlethal attempt ≤3). RESULTS Using scores from the SIS, individuals who had lethal and nonlethal attempts comprised two different network nodes with the edges representing the relationships among nodes. Among the antecedent behaviors, the conception of a method’s lethality predicted suicidal behaviors with severe medical outcomes. The vectorized relationship values among the elements of antecedent behaviors in our deep learning model (E-GONet) increased performances, such as F1 and area under the precision-recall gain curve (AUPRG), for identifying lethal attempts (up to 3% for F1 and 32% for AUPRG), as compared with other models (mean F1: 0.81 for E-GONet, 0.78 for linear regression, and 0.80 for random forest; mean AUPRG: 0.73 for E-GONet, 0.41 for linear regression, and 0.69 for random forest). CONCLUSIONS The relationships among behaviors antecedent to suicide attempts can be used to understand the suicidal intent of individuals and help identify the lethality of potential suicide attempts. Such a model may be useful in prioritizing cases for preventive intervention.

scholarly journals Segmentation of Polyp Instruments using UNet based deep learning model

2021 ◽  
Vol 1 (1) ◽  
pp. 44-46
Author(s):  
Ashar Mirza ◽  
Rishav Kumar Rajak
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Learning Model ◽  
Learning Method ◽  
Data Set ◽  
Test Dataset ◽  
Segmentation Task ◽  
Deep Learning Model

In this paper, we present a UNet architecture-based deep learning method that is used to segment polyp and instruments from the image data set provided in the MedAI Challenge2021. For the polyp segmentation task, we developed a UNet based algorithm for segmenting polyps in images taken from endoscopies. The main focus of this task is to achieve high segmentation metrics on the supplied test dataset. Similarly for the polyp segmentation task, in the instrument segmentation task, we have developed UNet based algorithms for segmenting instruments present in colonoscopy videos.

scholarly journals Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

2020 ◽  
Author(s):  
Aaron E. Kornblith ◽  
Newton Addo ◽  
Ruolei Dong ◽  
Robert Rogers ◽  
Jacqueline Grupp-Phelan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Learning Model ◽  
Pediatric Emergency ◽  
Test Dataset ◽  
Video Clips ◽  
Multi Stage ◽  
Development And Validation ◽  
Deep Learning Model

ABSTRACTThe pediatric Focused Assessment with Sonography for Trauma (FAST) is a sequence of ultrasound views rapidly performed by the clinician to diagnose hemorrhage. One limitation of FAST is inconsistent acquisition of required views. We sought to develop a deep learning model and classify FAST views using a heterogeneous dataset of pediatric FAST. This study of diagnostic test developed and tested a deep learning model for view classification of archived real-world pediatric FAST studies collected from two pediatric emergency departments. FAST frames were randomly distributed to training, validation, and test datasets in a 70:20:10 ratio; each patient was represented in only one dataset to maintain sample independence. The outcome was the prediction accuracy of the model in classifying FAST frames and video clips. FAST studies performed by 30 different clinicians from 699 injured children included 4,925 videos representing 1,062,612 frames from children who were a median of 9 years old. On test dataset, the overall view classification accuracy for the model was 93.4% (95% CI: 93.3-93.6) for frames and 97.8% (95% CI: 96.0-99.0) for video clips. Frames were correctly classified with an accuracy of 96.0% (95% CI: 95.9-96.1) for cardiac, 99.8% (95% CI: 99.8-99.8) for thoracic, 95.2% (95% CI: 95.0-95.3) for abdominal upper quadrants, and 95.9% (95% CI: 95.8-96.0) for suprapubic. A deep learning model can be developed to accurately classify pediatric FAST views. Accurate view classification is the important first step to support developing a consistent and accurate multi-stage deep learning model for pediatric FAST interpretation.

A Novel Deep Learning Model for Automated Mapping of Laboratory Test Terminologies to Logical Observation Identifiers Names and Codes (LOINC) (Preprint)

2021 ◽  
Author(s):  
Di-Ge Ai ◽  
Yu He ◽  
Sheng-Hai Jin ◽  
Xue-Min Liu ◽  
Nian-Yi Sun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Laboratory Test ◽  
High Precision ◽  
Learning Model ◽  
Learning System ◽  
Medical Laboratory ◽  
Test Dataset ◽  
Attention Model ◽  
Public Data ◽  
Deep Learning Model

BACKGROUND A standardized method for identifying medical laboratory observations, such as Logical Observation Identifiers Names and Codes (LOINC), is critical for creating accurate and effective public data models. However, such standards are not being used effectively. Standardized mapping facilitates consistency in medical terminologies and data sharing in multicenter treatment. OBJECTIVE To address the problem of standardizing laboratory test terminologies, a deep learning–based high-precision end-to-end terminology standardization matching system was developed to map laboratory test terms (LTTs) to LOINC. METHODS We manually constructed a laboratory test terminology mapping dataset containing 15,349 data items extracted from the information system of the Shengjing Hospital of China Medical University and matched 2,375 LOINC. We developed Attribute-wised Graph Attention Siamese Network (AGASN), a deep learning–based high-precision laboratory test terminology mapping model, to separately extract LTT features and LOINC term features and calculate the matching rate. We designed an attribute pooling mechanism to convert terminology strings to attribute sequences. Moreover, we developed a graph attention model based on attribute relations, which increased the interpretability of the proposed model. The problem of inconsistency in training and testing objectives was solved by improving the training objectives of the model. RESULTS The proposed a novel deep learning model achieved an accuracy of 82.33% ± 0.6% on the test dataset where the LOINC were visible, corresponding to a 10.9% improvement compared with that obtained using a random forest classifier. Furthermore, the proposed system achieved an accuracy of 63.14% ± 0.2% on the test dataset where the LOINC were invisible, constituting a 10.0% improvement compared with that obtained using SimCSE. Manual validation of the system performance showed accuracies of 82.33% and 70.66% on labeled and unlabeled datasets, respectively. Finally, we constructed a visual attribute relational strength network using an attribute graph attention model. CONCLUSIONS Herein, a Chinese laboratory test terminology mapping dataset was created and a deep learning system for the standardized mapping of LTTs was proposed. The results demonstrate that the proposed system can map LTTs to LOINC with high accuracy.

scholarly journals Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines

2021 ◽  
Vol 9 ◽  
Author(s):  
Azade Tabaie ◽  
Evan W. Orenstein ◽  
Shamim Nemati ◽  
Rajit K. Basu ◽  
Gari D. Clifford ◽  
...  
Keyword(s):  
Deep Learning ◽  
Clinical Features ◽  
Characteristic Curve ◽  
Time Windows ◽  
Vital Signs ◽  
Onset Time ◽  
Learning Model ◽  
Central Venous ◽  
Serious Infection ◽  
Deep Learning Model

Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*), among pediatric patients with Central Venous Lines (CVLs).Design: Retrospective cohort study.Setting: Single academic children's hospital.Patients: All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h.Measurements and Main Results: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI* during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI* by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence.Conclusion: A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection.

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