Background. Cerebrovascular disease has been the leading cause of death in China since 2017, and the control of medical expenses for these diseases is an urgent issue. Diagnosis-related groups (DRG) are increasingly being used to decrease the costs of healthcare worldwide. However, the classification variables and rules used vary from region to region. Of these variables, the question of whether the length of stay (LOS) should be used as a grouping variable is controversial. Aim. To identify the factors influencing inpatient medical expenditure in cerebrovascular disease patients. The performance of two sets of classification rules, and the effects of the extent of control of unreasonable medical treatment, were compared, to investigate whether the classification variables should include LOS. Methods. Data from 45,575 inpatients from a Healthcare Security Administration of a city in western China were used. Kruskal–Wallis H tests were used for single-factor analysis, and multiple linear stepwise regression was used to determine the main factors. A chi-squared automatic interaction detector (CHAID) algorithm was built as a decision tree model for grouping related data. The intensity of oversupply of service was controlled step by step from 10% to 100%, and the performance was calculated for each group. Results. The average hospitalization cost was 1,284 US dollars, and the total was 51.17 million US dollars. Of this, 43.42 million were paid by the government, and 7.75 million were paid by individuals. Factors including gender, age, type of insurance, level of hospital, LOS, surgery, therapeutic outcomes, main concomitant disease, and hypertension significantly influenced inpatient expenditure (<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="19.289pt" style="vertical-align:-0.6370001pt" id="M1" height="9.2729pt" version="1.1" viewBox="-0.0498162 -8.6359 19.289 9.2729"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-81" d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"/></g><g transform="matrix(.013,0,0,-0.013,11.658,0)"><path id="g117-91" d="M512 -3V55L134 254V256L512 456V514L75 281V230L512 -3Z"/></g></svg><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.918pt" style="vertical-align:-0.6370001pt" height="9.2729pt" version="1.1" viewBox="22.8711838 -8.6359 21.918 9.2729"><g transform="matrix(.013,0,0,-0.013,22.921,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,29.161,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,32.125,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.365,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"/></g></svg>). Incorporating LOS, the patients were divided into seven DRG groups, while without LOS, the patients were divided into eight DRG groups. More clinical variables were needed to achieve good results without LOS. Of the two rule sets, smaller coefficient of variation (CV) and a lower upper limit for patient costs were found in the group including LOS. Using this type of economic control, 3.35 million US dollars could be saved in one year.

Siyu Zeng

Li Luo

Yuanchen Fang

Xiaozhou He

Cost Control of Treatment for Cerebrovascular Patients Using a Machine Learning Model in Western China

Inguinal hernia repair is one of the most frequently conducted surgical procedures worldwide. Laparoscopic inguinal hernia repair is considered to be technically challenging. Artificial intelligence technology has made significant progress in medical imaging, but its application in laparoscopic surgery has not been widely carried out. Our aim is to detect vas deferens images in laparoscopic inguinal hernial repair using the convolutional neural network (CNN) and help surgeons to identify the vas deferens in time. We collected surgery videos from 35 patients with inguinal hernia who underwent laparoscopic hernia repair. We classified and labeled the images of the vas deferens and used the CNN to learn the image features. Totally, 2,600 images (26 patients) were labeled for training and validating the neural network and 1,200 images (6 patients) and 6 short video clips (3 patients) for testing. We adjusted the model parameters and tested the performance of the model under different confidence levels and IoU and used the chi-square to analyze the statistical difference in the video test dataset. We evaluated the model performance by calculating the true positive rate (TPR), true negative rate (TNR), accuracy (ACC), positive predictive value (PPV), and F1-score at different confidence levels of 0.1 to 0.9. In confidence level 0.4, the results were TPR 90.61&#x0025;, TNR 98.67&#x0025;, PPV 98.57&#x0025;, ACC 94.61&#x0025;, and F1 94.42&#x0025;, respectively. The average precision (AP) was 92.38&#x0025; at IoU 0.3. In the video test dataset, the average values of TPR and TNR were 90.11&#x0025; and 95.76&#x0025;, respectively, and there was no significant difference among the patients. The results suggest that the CNN can quickly and accurately identify and label vas deferens images in laparoscopic inguinal hernia repair.

Peng Cui

Song Zhao

Wenxi Chen

Identification of the Vas Deferens in Laparoscopic Inguinal Hernia Repair Surgery Using the Convolutional Neural Network

Adverse drug reactions (ADRs) pose health threats to humans. Therefore, the risk re-evaluation of post-marketing drugs has become an important part of the pharmacovigilance work of various countries. In China, drugs are mainly divided into three categories, from high-risk to low-risk drugs, namely, prescription drugs (Rx), over-the-counter drugs A (OTC-A), and over-the-counter drugs B (OTC-B). Until now, there has been a lack of automated evaluation methods for the three status switch of drugs. Based on China Food and Drug Administration&#x2019;s (CFDA) spontaneous reporting database (CSRD), we proposed a classification model to predict risk level of drugs by using feature enhancement based on Generative Adversarial Networks (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE). A total of 985,960 spontaneous reports from 2011 to 2018 were selected from CSRD in Jiangsu Province as experimental data. After data preprocessing, a class-imbalance data set was obtained, which contained 887 Rx (accounting for 84.72&#x0025;), 113 OTC-A (10.79&#x0025;), and 47 OTC-B (4.49&#x0025;). Taking drugs as the samples, ADRs as the features, and signal detection results obtained by proportional reporting ratio (PRR) method as the feature values, we constructed the original data matrix, where the last column represents the category label of each drug. Our proposed model expands the ADR data from both the sample space and the feature space. In terms of feature space, we use feature selection (FS) to screen ADR symptoms with higher importance scores. Then, we use GAN to generate artificial data, which are added to the feature space to achieve feature enhancement. In terms of sample space, we use SMOTE technology to expand the minority samples to balance three categories of drugs and minimize the classification deviation caused by the gap in the sample size. Finally, we use random forest (RF) algorithm to classify the feature-enhanced and balanced data set. The experimental results show that the accuracy of the proposed classification model reaches 98&#x0025;. Our proposed model can well evaluate drug risk levels and provide automated methods for status switch of post-marketing drugs.

Jianxiang Wei

Guanzhong Feng

Zhiqiang Lu

Pu Han

Yunxia Zhu

Weidong Huang

Evaluating Drug Risk Using GAN and SMOTE Based on CFDA&#x2019;s Spontaneous Reporting Data

People often concern the relationships between symptoms and diseases when seeking medical advices. In this paper, medical data are divided into three copies, records related to main disease categories, records related to subclass disease types, and records of specific diseases firstly; then two disease recognition methods only based on symptoms for the main disease category identification, subclass disease type identification, and specific disease identification are given. In the methods, a neural network and a support vector machine (SVM) algorithms are adopted, respectively. In the method validation part, accuracy of the two diagnosis methods is tested and compared. Results show that automatic disease prediction only based on symptoms is possible for intelligent medical triage and common disease diagnosis.

Fangfang Luo

Xu Luo

Intelligent Disease Prediagnosis Only Based on Symptoms

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Journal of Healthcare Engineering

Medical College of Wisconsin, Milwaukee, USA

This Issue is now open for submissions.
Papers are published upon acceptance, regardless of the Special Issue publication date.

Call for papers

<div>
With the advent of the Big Data era, traditional statistical algorithms face various challenges such as analyzing unstructured data and efficiently processing a massive amount of data. Data mining algorithms and other machine learning methods can be applied to extract hidden and potentially useful patterns and discover new relationships among substantial data sets.

Modern medicine generates a great deal of data stored in medical databases. Clinical databases can be categorized as big data and include large quantities of information about patients and their medical conditions. Analyzing the quantitative and qualitative clinical data in addition to discovering relationships among a massive number of samples using data mining techniques could unveil hidden medical knowledge in terms of correlation and association of apparently independent variables. Because medical information has the characteristics of redundancy, multi-attribution, incompletion, and is closely related to time, medical data mining differs. 

This Special Issue seeks original, high-quality contributions that present novel medical data mining approaches. Original research and review articles are welcome.
Potential topics include but are not limited to the following:<ul class="li_special"><li>
Data mining and knowledge discovery in medicine
</li><li>
Medical expert systems
</li><li>
Medical knowledge engineering
</li><li>
Natural language processing in medical documents
</li><li>
Deep learning and artificial intelligence for medical images/videos
</li></ul></div>

Journal of Healthcare Engineering

Data Mining in Medicine

Data Mining in Medicine

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Articles

Cost Control of Treatment for Cerebrovascular Patients Using a Machine Learning Model in Western China

Identification of the Vas Deferens in Laparoscopic Inguinal Hernia Repair Surgery Using the Convolutional Neural Network

Evaluating Drug Risk Using GAN and SMOTE Based on CFDA’s Spontaneous Reporting Data

Intelligent Disease Prediagnosis Only Based on Symptoms