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General General

Medical Fraud and Abuse Detection System Based on Machine Learning.

In International journal of environmental research and public health ; h5-index 73.0

It is estimated that approximately 10% of healthcare system expenditures are wasted due to medical fraud and abuse. In the medical area, the combination of thousands of drugs and diseases make the supervision of health care more difficult. To quantify the disease-drug relationship into relationship score and do anomaly detection based on this relationship score and other features, we proposed a neural network with fully connected layers and sparse convolution. We introduced a focal-loss function to adapt to the data imbalance and a relative probability score to measure the model's performance. As our model performs much better than previous ones, it can well alleviate analysts' work.

Zhang Conghai, Xiao Xinyao, Wu Chao


anomaly detection, healthcare fraud, medical abuse

Radiology Radiology

Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.

In Federal practitioner : for the health care professionals of the VA, DoD, and PHS

Background : Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. In only a few months, it has had a dramatic impact on society and world economies. COVID-19 has presented numerous challenges to all aspects of health care, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications for health care. Machine learning is a subset of AI that uses deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than can humans.

Methods : In this article, we explore the potential for the simple and widely available chest X-ray (CXR) to be used with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision.

Results : Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value.

Conclusions : We have used a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. The findings have implications for screening and triage, initial diagnosis, monitoring disease progression, and identifying patients at increased risk of morbidity and mortality. Based on the data, a website was created to demonstrate how such technologies could be shared and distributed to others to combat entities such as COVID-19 moving forward.

Borkowski Andrew A, Viswanadhan Narayan A, Thomas L Brannon, Guzman Rodney D, Deland Lauren A, Mastorides Stephen M


Ophthalmology Ophthalmology

Rethinking the Extraction and Interaction of Multi-Scale Features for Vessel Segmentation

ArXiv Preprint

Analyzing the morphological attributes of blood vessels plays a critical role in the computer-aided diagnosis of many cardiovascular and ophthalmologic diseases. Although being extensively studied, segmentation of blood vessels, particularly thin vessels and capillaries, remains challenging mainly due to the lack of an effective interaction between local and global features. In this paper, we propose a novel deep learning model called PC-Net to segment retinal vessels and major arteries in 2D fundus image and 3D computed tomography angiography (CTA) scans, respectively. In PC-Net, the pyramid squeeze-and-excitation (PSE) module introduces spatial information to each convolutional block, boosting its ability to extract more effective multi-scale features, and the coarse-to-fine (CF) module replaces the conventional decoder to enhance the details of thin vessels and process hard-to-classify pixels again. We evaluated our PC-Net on the Digital Retinal Images for Vessel Extraction (DRIVE) database and an in-house 3D major artery (3MA) database against several recent methods. Our results not only demonstrate the effectiveness of the proposed PSE module and CF module, but also suggest that our proposed PC-Net sets new state of the art in the segmentation of retinal vessels (AUC: 98.31%) and major arteries (AUC: 98.35%) on both databases, respectively.

Yicheng Wu, Chengwei Pan, Shuqi Wang, Ming Zhang, Yong Xia, Yizhou Yu


Public Health Public Health

Waist-to-height ratio and skipping breakfast are predictive factors for high blood pressure in adolescents.

In Scientific reports ; h5-index 158.0

The purpose of this study was to estimate the prevalence of high blood pressure (HBP) in adolescents of the Valencian Autonomous Community (VC) in Spain. Besides, its association with other risk factors related to cardiovascular disease (CVD) or arterial hypertension (AHT) in order to increase our knowledge of public health and to provide advice about healthy diets. We conducted a multicentre, observational, cross-sectional, epidemiological study in a sample of 4402 adolescents from 15 schools during the 2015-2016 school year. The participants were aged between 11 and 18 years, and any individuals already diagnosed with AHT were excluded. In addition to the Physical Activity Questionnaire for Adolescents (PAQ-A), Evaluation of the Mediterranean Diet Quality Index (KIDMED), a lifestyle habits survey, the waist-to-height ratio (WtHR), and body mass index (BMI) were calculated for each participant. Informed Consent was obtained from Parents of the adolescents involved in the current study. The study received approval from the University ethics committee and all procedures were conducted in accordance with the tenets of the Declaration of Helsinki. Chi-squared, Student t-tests, and ANOVA statistical analyses showed that 653 (14.8%) adolescents had previously undiagnosed HBP and that was significantly associated with male sex (p < 0.001), age over 15 years (p < 0.05), and height, weight, waist circumference, WtHR, BMI, and skipping breakfast. Based on the data we obtained in this study, the modifiable factors that influence HBP in adolescents were WtHR, BMI, and skipping breakfast.

Aparicio-Cercós C, Alacreu M, Salar L, Moreno Royo L


General General

On the interpretability of predictors in spatial data science: the information horizon.

In Scientific reports ; h5-index 158.0

Two important theories in spatial modelling relate to structural and spatial dependence. Structural dependence refers to environmental state-factor models, where an environmental property is modelled as a function of the states and interactions of environmental predictors, such as climate, parent material or relief. Commonly, the functions are regression or supervised classification algorithms. Spatial dependence is present in most environmental properties and forms the basis for spatial interpolation and geostatistics. In machine learning, modelling with geographic coordinates or Euclidean distance fields, which resemble linear variograms with infinite ranges, can produce similar interpolations. Interpolations do not lend themselves to causal interpretations. Conversely, with structural modeling, one can, potentially, extract knowledge from the modelling. Two important characteristics of such interpretable environmental modelling are scale and information content. Scale is relevant because very coarse scale predictors can show nearly infinite ranges, falling out of what we call the information horizon, i.e. interpretation using domain knowledge isn't possible. Regarding information content, recent studies have shown that meaningless predictors, such as paintings or photographs of faces, can be used for spatial environmental modelling of ecological and soil properties, with accurate evaluation statistics. Here, we examine under which conditions modelling with such predictors can lead to accurate statistics and whether an information horizon can be derived for scale and information content.

Behrens Thorsten, Viscarra Rossel Raphael A


General General

Simulation of liquid flow with a combination artificial intelligence flow field and Adams-Bashforth method.

In Scientific reports ; h5-index 158.0

Direct numerical simulation (DNS) of particle hydrodynamics in the multiphase industrial process enables us to fully learn the process and optimize it on the industrial scale. However, using high-resolution computational calculations for particle movement and the interaction between the solid phase and other phases in fine timestep is limited to excellent computational resources. Solving the Eulerian flow field as a source of solid particle movement can be very time-consuming. However, by the revolution of the fast and accurate learning process, the Eulerian domain can be computed by smart modeling in a very short computational time. In this work, using the machine learning method, the flow field in the square shape cavity is trained, and then the Eulerian framework is replaced with a machine learning method to generate the artificial intelligence (AI) flow field. Then the Lagrangian framework is coupled with this AI flow field, and we simulate particle motion through the fully AI framework. The Adams-Bashforth finite element method is used as a conventional CFD method (Eulerian framework) to simulate the flow field in the cavity. After simulating fluid flow, the ANFIS method is used as an AI model to train the Eulerian data-set and represents AI fluid flow (framework). The Lagrangian framework is coupled with the AI method, and the particle freely migrates through this artificial framework. The results reveal that there is a great agreement between Euler-Lagrangian and AI- Lagrangian in the cavity. We also found that there is an excellent agreement between AI overview with the Adams-Bashforth approach, and the new combination of machine learning and CFD method can accelerate the calculation of the flow field in the square-shaped cavity. AI model can mimic the vortex structure in the cavity, where there is a zero-velocity structure in the center of the domain and maximum velocity near the moving walls.

Babanezhad Meisam, Behroyan Iman, Nakhjiri Ali Taghvaie, Marjani Azam, Shirazian Saeed