Computer-assisted Medical Decision-making System for Diagnosis of Urticaria

Computer-Assisted Clinical Diagnosis and Treatment

PURPOSE OF REVIEW

Computer-assisted diagnosis and treatment (CAD/CAT) is a rapidly growing field of medicine that uses computer technology and telehealth to aid in the diagnosis and treatment of various diseases. The purpose of this paper is to provide a review on computer-assisted diagnosis and treatment. This technology gives providers access to diagnostic tools and treatment options so that they can make more informed decisions leading to improved patient outcomes.

RECENT FINDINGS

CAD/CAT has expanded in allergy and immunology in the form of digital tools that enable remote patient monitoring such as digital inhalers, pulmonary function tests, and E-diaries. By incorporating this information into electronic medical records (EMRs), providers can use this information to make the best, evidence-based diagnosis and to recommend treatment that is likely to be most effective. A major benefit of CAD/CAT is that by analyzing large amounts of data, tailored recommendations can be made to improve patient outcomes and reduce the risk of adverse events. Machine learning can assist with medical data acquisition, feature extraction, interpretation, and decision support. It is important to note that this technology is not meant to replace human professionals. Instead, it is designed to assist healthcare professionals to better diagnose and treat patients.

A moment kernel machine for clinical data mining to inform medical decision making

Machine learning-aided medical decision making presents three major challenges: achieving model parsimony, ensuring credible predictions, and providing real-time recommendations with high computational efficiency. In this paper, we formulate medical decision making as a classification problem and develop a moment kernel machine (MKM) to tackle these challenges. The main idea of our approach is to treat the clinical data of each patient as a probability distribution and leverage moment representations of these distributions to build the MKM, which transforms the high-dimensional clinical data to low-dimensional representations while retaining essential information. We then apply this machine to various pre-surgical clinical datasets to predict surgical outcomes and inform medical decision making, which requires significantly less computational power and time for classification while yielding favorable performance compared to existing methods. Moreover, we utilize synthetic datasets to demonstrate that the developed moment-based data mining framework is robust to noise and missing data, and achieves model parsimony giving an efficient way to generate satisfactory predictions to aid personalized medical decision making.

COVID-19 and artificial intelligence: Experts and dermatologists perspective

INTRODUCTION

Artificial intelligence (AI) has an important role to play in future healthcare offerings. Machine learning and artificial neural networks are subsets of AI that refer to the incorporation of human intelligence into computers to think and behave like humans.

OBJECTIVE

The objective of this review article is to discuss perspectives on the AI in relation to Coronavirus disease (COVID-19).

METHODS

Google Scholar and PubMed databases were searched to retrieve articles related to COVID-19 and AI. The current evidence is analysed and perspectives on the usefulness of AI in COVID-19 is discussed.

RESULTS

The coronavirus pandemic has rendered the entire world immobile, crashing economies, industries, and health care. Telemedicine or tele-dermatology for dermatologists has become one of the most common solutions to tackle this crisis while adhering to social distancing for consultations. While it has not yet achieved its full potential, AI is being used to combat coronavirus disease on multiple fronts. AI has made its impact in predicting disease onset by issuing early warnings and alerts, monitoring, forecasting the spread of disease and supporting therapy. In addition, AI has helped us to build a model of a virtual protein structure and has played a role in teaching as well as social control.

CONCLUSION

Full potential of AI is yet to be realized. Expert data collection, analysis, and implementation are needed to improve this advancement.

Access, charge and quality of tele-dermatology e-consults in China: A standardized patients study

OBJECTIVE

Direct-to-consumer telemedicine is rapidly changing the way that patients seek medical care. This study provided the first report on access, cost and quality of text- and image-based tele-dermatology e-consults, in China.

METHODS

We adopted the Standardized Patients approach to evaluate the services of tele-dermatology e-consults in two metropolitan cities, that is, Beijing and Hangzhou, in China. We measured quality from four dimensions: service process, diagnosis accuracy, prescription and treatment comprehensiveness, based on China's national clinical guidelines. We performed logistic regressions to investigate factors that were associated with high-quality care.

RESULTS

For 114 physicians eligible for inclusion, we succeeded in 87 (76%) validated visits. The median waiting time was 100 minutes (IQR 19-243 minutes) and the median length of consultation was 636 minutes (about 10 hours, IQR 188-1528 minutes). Per visit costs varied from $0 to $38, with a median of $8 (IQR 4-9). Among all, 15% of visits showed high quality in service process, 84% arrived in the correct diagnosis, 24% provided high-quality prescriptions and 71% provided comprehensive treatment. Providing images was associated with high quality in service process (OR 7.22, 95% CI 1.49-34.88). Visits in metropolitan Beijing and on non-work days had better prescription quality than that in metropolitan Hangzhou (OR 6.05, 95% CI 1.75-20.95) and that on workdays (OR 3.75, 95%CI 1.27-11.04), respectively.

CONCLUSIONS

Tele-dermatology e-consults seem to be easy to access and less expensive in China. However, great efforts are warranted to ensure that service processes and prescriptions adhere to clinical guidelines.

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases

Clinical decision support systems (CDSS) aid health care professionals (HCP) in evaluating large sets of information and taking informed decisions during their clinical routine. CDSS are becoming particularly important in the perspective of precision medicine, when HCP need to consider growing amounts of data to create precise patient profiles for personalized diagnosis, treatment and outcome monitoring. In allergy care, several CDSS are being developed and investigated, mainly for respiratory allergic diseases. Although the proposed solutions address different stakeholders, the majority aims at facilitating evidence-based and shared decision-making, incorporating guidelines, and real-time clinical data. We offer here an overview on existing tools, new developments and novel concepts and discuss the potential of digital CDSS in improving prevention, diagnosis and monitoring of allergic diseases.

Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network

A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented. The clinical data are subjected to data preprocessing, feature selection, and classification. Hot deck imputation has been used for handling missing values and min-max normalization is used for data transformation. Wrapper approach that employs bioinspired algorithms, namely, Differential Evolution, Lion Optimization, and Glowworm Swarm Optimization with accuracy of AdaBoostSVM classifier as fitness function has been used for feature selection. Each bioinspired algorithm selects a subset of features yielding three feature subsets. Correlation-based ensemble feature selection is performed to select the optimal features from the three feature subsets. The optimal features selected through correlation-based ensemble feature selection are used to train a gradient descendant backpropagation neural network. Ten-fold cross-validation technique has been used to train and test the performance of the classifier. Hepatitis dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine (UCI) Machine Learning repository have been used to evaluate the classification accuracy. An accuracy of 98.47% is obtained for Wisconsin Diagnostic Breast Cancer dataset, and 95.51% is obtained for Hepatitis dataset. The proposed framework can be tailored to develop clinical decision-making systems for any health disorders to assist physicians in clinical diagnosis.

How the Smartphone Is Changing Allergy Diagnostics

PURPOSE OF REVIEW

Evidence-based clinical diagnosis of allergic disorders is increasingly challenging. Clinical decision support systems implemented in mobile applications (apps) are being developed to assist clinicians in diagnostic decisions at the point of care. We reviewed apps for allergic diseases general diagnosis, diagnostic refinement and diagnostic personalisation. Apps designed for specific medical devices are not addressed.

RECENT FINDINGS

Apps with potential usefulness in the initial diagnosis and diagnostic refinement of respiratory, food, skin and drug allergies are described. Apps to support diagnostic personalisation are not yet available. There is an urgent need to increase the scientific evidence on the real usefulness of these apps, as well as to develop new scientifically grounded apps designed and validated to support all allergic diseases and diagnostic levels. Apps have the potential to change the diagnosis of allergic diseases becoming part of the routine diagnostics toolset, but its usefulness needs to be established.