Prediction of Future Healthcare Expenses of Patients from Chest Radiographs Using Deep Learning: A Pilot Study

Abstract Our objective was to develop deep learning models with chest radiograph data to predict healthcare costs and classify top-50% spenders. 21,872 frontal chest radiographs were retrospectively collected from 19,524 patients with at least 1-year spending data. Among the patients, 11,003 patients had 3 years of cost data, and 1678 patients had 5 years of cost data. Model performances were measured with area under the receiver operating characteristic curve (ROC-AUC) for classification of top-50% spenders and Spearman ρ for prediction of healthcare cost. The best model predicting 1-year (N=21,872) expenditure achieved ROC-AUC of 0.806 [95% CI, 0.793-0.819] for top-50% spender classification and ρ of 0.561 [0.536-0.586] for regression. Similarly, for predicting 3-year (N=12,395) expenditure, ROC-AUC of 0.771 [0.750-0.794] and ρ of 0.524 [0.489-0.559]; for predicting 5-year (N=1,779) expenditure ROC-AUC of 0.729 [0.667-0.729] and ρ of 0.424 [0.324-0.529]. Our deep learning model demonstrated the feasibility of predicting health care expenditure as well as classifying top 50% healthcare spenders at 1, 3, and 5 year(s), implying the feasibility of combining deep learning with information-rich imaging data to uncover hidden associations that may allude physicians. Such a model can be a starting point of making an accurate budget in reimbursement models in healthcare industries.

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On Teacher-Student Semi-Supervised Learning for Chest X-ray Image Classification

10.21528/cbic2021-80 ◽

2021 ◽

Author(s):

Roberto Augusto Philippi Martins ◽

Danilo Silva

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Learning Algorithm ◽

Unlabeled Data ◽

Imaging Data ◽

X Ray ◽

Teacher Student ◽

Chest X Ray ◽

Deep Learning Model

The lack of labeled data is one of the main prohibiting issues on the development of deep learning models, as they rely on large labeled datasets in order to achieve high accuracy in complex tasks. Our objective is to evaluate the performance gain of having additional unlabeled data in the training of a deep learning model when working with medical imaging data. We present a semi-supervised learning algorithm that utilizes a teacher-student paradigm in order to leverage unlabeled data in the classification of chest X-ray images. Using our algorithm on the ChestX-ray14 dataset, we manage to achieve a substantial increase in performance when using small labeled datasets. With our method, a model achieves an AUROC of 0.822 with only 2% labeled data and 0.865 with 5% labeled data, while a fully supervised method achieves an AUROC of 0.807 with 5% labeled data and only 0.845 with 10%.

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Automated Radiology Alert System for Pneumothorax Detection on Chest Radiographs Improves Efficiency and Diagnostic Performance

Diagnostics ◽

10.3390/diagnostics11071182 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1182

Author(s):

Cheng-Yi Kao ◽

Chiao-Yun Lin ◽

Cheng-Chen Chao ◽

Han-Sheng Huang ◽

Hsing-Yu Lee ◽

...

Keyword(s):

Deep Learning ◽

Diagnostic Performance ◽

Medical Center ◽

Characteristic Curve ◽

Learning Model ◽

Chest Radiographs ◽

Alert System ◽

Before And After ◽

Set Up ◽

Deep Learning Model

We aimed to set up an Automated Radiology Alert System (ARAS) for the detection of pneumothorax in chest radiographs by a deep learning model, and to compare its efficiency and diagnostic performance with the existing Manual Radiology Alert System (MRAS) at the tertiary medical center. This study retrospectively collected 1235 chest radiographs with pneumothorax labeling from 2013 to 2019, and 337 chest radiographs with negative findings in 2019 were separated into training and validation datasets for the deep learning model of ARAS. The efficiency before and after using the model was compared in terms of alert time and report time. During parallel running of the two systems from September to October 2020, chest radiographs prospectively acquired in the emergency department with age more than 6 years served as the testing dataset for comparison of diagnostic performance. The efficiency was improved after using the model, with mean alert time improving from 8.45 min to 0.69 min and the mean report time from 2.81 days to 1.59 days. The comparison of the diagnostic performance of both systems using 3739 chest radiographs acquired during parallel running showed that the ARAS was better than the MRAS as assessed in terms of sensitivity (recall), area under receiver operating characteristic curve, and F1 score (0.837 vs. 0.256, 0.914 vs. 0.628, and 0.754 vs. 0.407, respectively), but worse in terms of positive predictive value (PPV) (precision) (0.686 vs. 1.000). This study had successfully designed a deep learning model for pneumothorax detection on chest radiographs and set up an ARAS with improved efficiency and overall diagnostic performance.

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Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning

Frontiers in Oncology ◽

10.3389/fonc.2021.700158 ◽

2021 ◽

Vol 11 ◽

Author(s):

Tianle Shen ◽

Runping Hou ◽

Xiaodan Ye ◽

Xiaoyang Li ◽

Junfeng Xiong ◽

...

Keyword(s):

Deep Learning ◽

Operating Characteristic ◽

Characteristic Curve ◽

Three Dimensional ◽

Ct Images ◽

Optimal Decision ◽

Preoperative Ct ◽

3D Cnn ◽

Deep Learning Model

BackgroundTo develop and validate a deep learning–based model on CT images for the malignancy and invasiveness prediction of pulmonary subsolid nodules (SSNs).Materials and MethodsThis study retrospectively collected patients with pulmonary SSNs treated by surgery in our hospital from 2012 to 2018. Postoperative pathology was used as the diagnostic reference standard. Three-dimensional convolutional neural network (3D CNN) models were constructed using preoperative CT images to predict the malignancy and invasiveness of SSNs. Then, an observer reader study conducted by two thoracic radiologists was used to compare with the CNN model. The diagnostic power of the models was evaluated with receiver operating characteristic curve (ROC) analysis.ResultsA total of 2,614 patients were finally included and randomly divided for training (60.9%), validation (19.1%), and testing (20%). For the benign and malignant classification, the best 3D CNN model achieved a satisfactory AUC of 0.913 (95% CI: 0.885–0.940), sensitivity of 86.1%, and specificity of 83.8% at the optimal decision point, which outperformed all observer readers’ performance (AUC: 0.846±0.031). For pre-invasive and invasive classification of malignant SSNs, the 3D CNN also achieved satisfactory AUC of 0.908 (95% CI: 0.877–0.939), sensitivity of 87.4%, and specificity of 80.8%.ConclusionThe deep-learning model showed its potential to accurately identify the malignancy and invasiveness of SSNs and thus can help surgeons make treatment decisions.

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Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

Applied Sciences ◽

10.3390/app9224871 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4871 ◽

Cited By ~ 4

Author(s):

Quan Liu ◽

Chen Feng ◽

Zida Song ◽

Joseph Louis ◽

Jian Zhou

Keyword(s):

Deep Learning ◽

Model Comparison ◽

Surveillance Systems ◽

Comparison Study ◽

Learning Models ◽

The Core ◽

Dump Trucks ◽

Deep Learning Model ◽

Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

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Sea Ice and Open Water Classification of SAR Images Using a Deep Learning Model

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323990 ◽

2020 ◽

Author(s):

Yibin Ren ◽

Huan Xu ◽

Bin Liu ◽

Xiaofeng Li

Keyword(s):

Deep Learning ◽

Sea Ice ◽

Open Water ◽

Learning Model ◽

Sar Images ◽

Water Classification ◽

Deep Learning Model

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Automatic Prediction and Classification of Diseases in Melons using Stacked RNN based Deep Learning Model

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) ◽

10.1109/icscan49426.2020.9262414 ◽

2020 ◽

Author(s):

D. Jayakumar ◽

A. Elakkiya ◽

R. Rajmohan ◽

M.O. Ramkumar

Keyword(s):

Deep Learning ◽

Learning Model ◽

Classification Of Diseases ◽

Deep Learning Model

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Detection and classification of arrhythmia using an explainable deep learning model

Journal of Electrocardiology ◽

10.1016/j.jelectrocard.2021.06.006 ◽

2021 ◽

Author(s):

Yong-Yeon Jo ◽

Joon-myoung Kwon ◽

Ki-Hyun Jeon ◽

Yong-Hyeon Cho ◽

Jae-Hyun Shin ◽

...

Keyword(s):

Deep Learning ◽

Learning Model ◽

Deep Learning Model

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Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

Mathematics ◽

10.3390/math9091002 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1002

Author(s):

Mohammad Khishe ◽

Fabio Caraffini ◽

Stefan Kuhn

Keyword(s):

Deep Learning ◽

Early Detection ◽

Iterative Process ◽

High Accuracy ◽

X Ray ◽

Starting Point ◽

Reliable Model ◽

Minimum Number ◽

Chest X Ray ◽

Deep Learning Model

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

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