Information Artificial Intelligence Research Articles

Endoscopic capsule robot-based diagnosis, navigation and localization in the gastrointestinal tract.

The proliferation of video capsule endoscopy (VCE) would not have been possible without continued technological improvements in imaging and locomotion. Advancements in imaging include both software and hardware improvements but perhaps the greatest software advancement in imaging comes in the form of artificial intelligence (AI). Current research into AI in VCE includes the diagnosis of tumors, gastrointestinal bleeding, Crohn’s disease, and celiac disease. Other advancements have focused on the improvement of both camera technologies and alternative forms of imaging. Comparatively, advancements in locomotion have just started to approach clinical use and include onboard controlled locomotion, which involves miniaturizing a motor to incorporate into the video capsule, and externally controlled locomotion, which involves using an outside power source to maneuver the capsule itself. Advancements in locomotion hold promise to remove one of the major disadvantages of VCE, namely, its inability to obtain targeted diagnoses. Active capsule control could in turn unlock additional diagnostic and therapeutic potential, such as the ability to obtain targeted tissue biopsies or drug delivery. With both advancements in imaging and locomotion has come a corresponding need to be better able to process generated images and localize the capsule’s position within the gastrointestinal tract. Technological advancements in computation performance have led to improvements in image compression and transfer, as well as advancements in sensor detection and alternative methods of capsule localization. Together, these advancements have led to the expansion of VCE across a number of indications, including the evaluation of esophageal and colon pathologies including esophagitis, esophageal varices, Crohn’s disease, and polyps after incomplete colonoscopy. Current research has also suggested a role for VCE in acute gastrointestinal bleeding throughout the gastrointestinal tract, as well as in urgent settings such as the emergency department, and in resource-constrained settings, such as during the COVID-19 pandemic. VCE has solidified its role in the evaluation of small bowel bleeding and earned an important place in the practicing gastroenterologist’s armamentarium. In the next few decades, further improvements in imaging and locomotion promise to open up even more clinical roles for the video capsule as a tool for non-invasive diagnosis of lumenal gastrointestinal pathologies.

Investigating a Key COVID-19 Question by Using Natural Language Processing on Scientific Publications

The COVID-19 pandemic has brought an unprecedented challenge to public health. Numerous scientific publications are published daily on COVID-19 to understand the unexplored facets of the disease. The sheer volume of these publications makes it daunting for researchers to quickly find information and evaluate data related to specific COVID-19 queries. Natural Language Processing (NLP), a form of artificial intelligence, assists in churning these huge piles of data with a sophisticated algorithmic approach. The purpose of this study is to investigate key a COVID-19 question by using NLP on scientific publications. Using the T5 (Text-To-Text Transfer Transformer) model, we analyzed 740,000 journal abstracts for specific answers an important COVID-19 question. We performed qualitative observations, T-Tests (p-values and inferences), and accuracy metrics (Precision, Recall, and F1 score) to evaluate the models in this study. As the number of scientific publications increases, our proposed methodology provides an efficient mechanism for performing specific information retrieval for emerging questions, diseases, and related conditions, especially for underrepresented populations.

Study of Integration of Neural Networks with Other Existing or Developing Technologies: A Review

The popularity of Artificial Neural Networks (ANN) as significant intelligence methods has been growing. Their applications in numerous fields have been greatly influenced by their capacity to emulate the cognitive processes and decision-making of the human brain. The fundamentals of an ANN's nature and operation are described in this study. This paper's main goal is to provide a quick overview of how neural networks might be combined with other forms of artificial intelligence, such as fuzzy logic, expert systems, and genetic algorithms. It describes the working models of these hybrid systems as well as the areas in which they can be used. Finally, it discusses current developments in the study of neural networks.

MEGACOSM – DETECTION AND CLASSIFICATION OF ASTRONOMICAL OBJECTS

The concept of existence started with the bigbang theory, which was a phenomenon where multiple objects collided to create multiple other things. This creation is said to be increasing at the rate of the cosmic acceleration due to which multiple new objects came into existence called as the astronomical objects. Space object (SO) detection, classification, and characterization are significant challenges in many research fields. In recent years, deep learning and other forms of artificial intelligence (AI) have drawn the attention of many astronomers and academics. Megacosm is project that is used for the classification and the identification of those newly created celestial objects. It works on the process of manually training the model with the data annotation techniques and the dataset is enhanced using the data augmentation technique. It uses YOLO as the core algorithm and also deep learning concepts like CNN (Convolution Neural Network) to predict results. It gives the output as a bounding box around the detected object along with the accuracy of that prediction.

AI, intelligent robots and Romanian drivers’ attitudes towards autonomous cars

Abstract Products and services based on some form of Artificial Intelligence are beginning to represent an ever-increasing presence in consumers’ daily lives. Already, technologies such as algorithms based on machine learning, digital personal assistants, chatbots, smart devices generically labeled as the Internet of Things, and ultimately physical robots, are all becoming a daily presence in consumers’ lives, conscientiously or unconscientiously, all incorporating a certain degree and level of development and application of Artificial Intelligence. Artificial Intelligence is not without controversy and the debate around it concentrates on several aspects. From the get go, even attempting to define AI will lead to unavoidable debates about what it is and what it is not, how broad can the definition be, so as to cover many of the things we presently call Artificial Intelligence. Once a definition is agreed, usually a broad one, several categories emerge that could represent current applications of Artificial Intelligence, such as recommender algorithms, intelligent assistants, chatbots, and intelligent robots – with respect to the latter, autonomous vehicles are perhaps the most common occurrence. All of these applications of AI have many things in common, but none like the fact that their success depends on being able to feed on enormous amounts of consumer generated data. This opens the door to how this data, largely personal in its nature, is used and the underlying privacy issues that accompany it. To compound the problem, some promote the idea that Artificial Intelligence could advance to a level called AGI where machines would develop self-conscious attributes capable of escaping human control with all accompanying perils to the very existence of humanity. Equally important is how AI may impact on people’s lives, at present, but especially in the future, and how individuals eventually perceive, position themselves and are willing to accept Artificial Intelligence. This paper presents the results of a survey conducted among Romanian drivers in 2021 aimed at revealing how they position themselves in relation to the emergence of autonomous, self-driving cars. It identifies both potential benefits which drivers may associate with self-driving cars, as well as risks and concerns they might have. The balance between the two is critical in driving up acceptance of these technological advancements in our vehicles which are set to become the norm in a nearer or more distant future.

Accuracy of eyemeter as a deep learning tool for identifying strabismus in pediatric patients

With its promising advances in image recognition, deep learning (DL) as a form of artificial intelligence is positioned to become integral to the field of ophthalmology. Eyemeter is a DL tool previously developed to assess external features of adult eyes. This study tested the functionality and accuracy of Eyemeter in assessing direction and magnitude of strabismus in primary gaze based on external photographs.

Application of Medical Knowledge Graphs in Cardiology and Cardiovascular Medicine: A Brief Literature Review.

A knowledge graph is defined as a collection of interlinked descriptions of concepts, relationships, entities and events. Medical knowledge graphs have been the most recent advances in technology, therapy and medicine. Nowadays, a number of specific uses and applications rely on knowledge graphs. The application of the knowledge graph, another form of artificial intelligence (AI) in cardiology and cardiovascular medicine, is a new concept, and only a few studies have been carried out on this particular aspect. In this brief literature review, the use and importance of disease-specific knowledge graphs in exploring various aspects of Kawasaki disease were described. A vision of individualized knowledge graphs (iKGs) in cardiovascular medicine was also discussed. Such iKGs would be based on a modern informatics platform of exchange and inquiry that could comprehensively integrate biologic knowledge with medical histories and health outcomes of individual patients. This could transform how clinicians and scientists discover, communicate and apply new knowledge. In addition, we also described how a study based on the comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease used a knowledge graph to show the dietary factors associated with cardiovascular diseases in Nurses’ Health Study data. To conclude, in this fast-developing world, medical knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. They could be a major and effective tool in cardiology and cardiovascular medicine and play an important role in reaching effective clinical decisions during treatment and management of patients in the cardiology department.

Development of a Visualization Deep Learning Model for Classifying Origins of Ventricular Arrhythmias.

Several algorithms have been proposed for differentiating the right and left outflow tracts (RVOT/LVOT) arrhythmia origins from 12-lead electrocardiograms (ECGs); however, the procedure is complicated. A deep learning (DL) model, a form of artificial intelligence, can directly use ECGs and depict the importance of the leads and waveforms. This study aimed to create a visualized DL model that could classify arrhythmia origins more accurately.Methods and Results: This study enrolled 80 patients who underwent catheter ablation. A convolutional neural network-based model that could classify arrhythmia origins with 12-lead ECGs and visualize the leads that contributed to the diagnosis using a gradient-weighted class activation mapping method was developed. The average prediction results of the origins by the DL model were 89.4% (88.2-90.6) for accuracy and 95.2% (94.3-96.2) for recall, which were significantly better than when a conventional algorithm is used. The ratio of the contribution to the prediction differed between RVOT and LVOT origins. Although leads V1 to V3 and the limb leads had a focused balance in the LVOT group, the contribution ratio of leads aVR, aVL, and aVF was higher in the RVOT group. This study diagnosed the arrhythmia origins more accurately than the conventional algorithm, and clarified which part of the 12-lead waveforms contributed to the diagnosis. The visualized DL model was convincing and may play a role in understanding the pathogenesis of arrhythmias.

O023 Artificial intelligence in photography of normothermic machine perfusion of livers

Abstract Introduction Livers donated for use in transplantation are underutilised. A lack of clear guidelines regarding the visual assessment of their quality means many borderline grafts are needlessly discarded; macroscopic visual assessment by transplanting clinicians is subjective and inconsistent. Deep learning (DL), a form of artificial intelligence, has previously been used to aid medical image analysis. Normothermic machine perfusion (NMP) is often used to evaluate borderline livers for transplantation. This study aimed to develop an objective DL model to visually assess organ transplantability during NMP. Methods Five DL models were trained and tested on 100 images of donor livers, each labelled with scores from three transplant clinicians on steatosis, perfusion, and transplantability. Models were trained to classify liver transplantability through either image data and transplantability scoring data, or image data and all clinician scoring data. Model accuracy, specificity, and sensitivity were calculated. Evaluations of clinician scoring agreement were carried out using intraclass correlation coefficient (ICC) and Fleiss kappa. Results In the classification of transplantability, the highest performing models achieved training and testing accuracies of 64.3% and 76%, respectively. Sensitivity and specificity ranged between 44.1%–94.1% and 6.3%–75%, respectively. ICC and Fleiss kappa values indicated a fair-to moderate scoring agreement between clinicians. Conclusion The performance of DL image analysis in assessing liver quality during NMP has been modest. Visual assessment during NMP is more challenging than on the back table at organ retrieval. Broader, more varied data sets are required to maximise model performance. Take-home message Livers donated for use in transplantation are underutilised due to a lack of clear guidelines and the subjective nature of their visual assessment. Artificial intelligence can be used to consistently and objectively assess donated livers in order to improve their utilisation.

Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.

Interfacing With the Electronic Health Record (EHR): A Comparative Review of Modes of Documentation.

Electronic health records (EHRs) have provided physicians with a systematic framework for collecting patient data, organizing notes from the healthcare team, and managing the daily workflow in the modern era of healthcare. Despite these advantages, EHRs have proven to be problematic for clinicians. The burdensome regulations requiring increased documentation with the EHR paradigm have led to inefficiencies from data-entry requirements forcing physicians to spend an inordinate amount of time on it, affecting the time available for direct patient care as well as leading to professional burnout. As a result, new modalities such as speech recognition, medical scribes, pre-made EHR templates, and digital scribes [a form of artificial intelligence (AI) based on ambient speech recognition] are increasingly being used to reduce charting time and increase the time available for patient care. The purpose of our review is to provide an up-to-date review of the literature on these modalities including their benefits and shortcomings, to help physicians and other medical professionals choose the best methods to document their patient-care encounters efficiently and effectively.

Evaluation of artificial intelligence models for osteoarthritis of the knee using deep learning algorithms for orthopedic radiographs

BACKGROUNDDeep learning, a form of artificial intelligence, has shown promising results for interpreting radiographs. In order to develop this niche machine learning (ML) program of interpreting orthopedic radiographs with accuracy, a project named deep learning algorithm for orthopedic radiographs was conceived. In the first phase, the diagnosis of knee osteoarthritis (KOA) as per the standard Kellgren-Lawrence (KL) scale in medical images was conducted using the deep learning algorithm for orthopedic radiographs.AIMTo compare efficacy and accuracy of eight different transfer learning deep learning models for detecting the grade of KOA from a radiograph and identify the most appropriate ML-based model for the detecting grade of KOA.METHODSThe study was performed on 2068 radiograph exams conducted at the Department of Orthopedic Surgery, Sir HN Reliance Hospital and Research Centre (Mumbai, India) during 2019-2021. Three orthopedic surgeons reviewed these independently, graded them for the severity of KOA as per the KL scale and settled disagreement through a consensus session. Eight models, namely ResNet50, VGG-16, InceptionV3, MobilnetV2, EfficientnetB7, DenseNet201, Xception and NasNetMobile, were used to evaluate the efficacy of ML in accurately classifying radiographs for KOA as per the KL scale. Out of the 2068 images, 70% were used initially to train the model, 10% were used subsequently to test the model, and 20% were used finally to determine the accuracy of and validate each model. The idea behind transfer learning for KOA grade image classification is that if the existing models are already trained on a large and general dataset, these models will effectively serve as generic models to fulfill the study’s objectives. Finally, in order to benchmark the efficacy, the results of the models were also compared to a first-year orthopedic trainee who independently classified these models according to the KL scale.RESULTSOur network yielded an overall high accuracy for detecting KOA, ranging from 54% to 93%. The most successful of these was the DenseNet model, with accuracy up to 93%; interestingly, it even outperformed the human first-year trainee who had an accuracy of 74%.CONCLUSIONThe study paves the way for extrapolating the learning using ML to develop an automated KOA classification tool and enable healthcare professionals with better decision-making.

A CPPS-architecture and workflow for bringing agent-based technologies as a form of artificial intelligence into practice

Abstract Due to the increase in Artificial Intelligence in the production systems domain, Industry 4.0 (I4.0) experts must collaborate with autonomous systems. Industrial AI raises several concerns about existing standards, which provide guidelines and design patterns. One way to realize I4.0 systems are Industrial Agents (IAs) due to their inherent autonomy and collaboration. Multi-Agent Systems (MASs) are well suited for realizing distributed AI in I4.0 components. Considering the properties of IAs and existing standards, an MAS architecture is presented for flexible and intelligent Cyber-Physical Production Systems. The article compares I4.0 standardization efforts relevant to adapt AI in the form of IAs, illustrates how IA design patterns can be used, and introduces the Multi-Agent aRchitecture for Industrial Automation applying desigN patterNs practicEs “MARIANNE”. An implementation guideline is presented to put this CPPS into practice.

Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review.

BackgroundAccurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population.ObjectiveThe objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes.MethodsLiterature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers.ResultsThis review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia.ConclusionsThe findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing.

Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms.

Vaccination can help control the coronavirus disease 2019 (COVID-19) pandemic but is undermined by vaccine hesitancy. Social media disseminates information and misinformation regarding vaccination. Tracking and analyzing social media vaccine sentiment could better prepare health professionals for vaccination conversations and campaigns. A real-time big data analytics framework was developed using natural language processing sentiment analysis, a form of artificial intelligence. The framework ingests, processes, and analyzes tweets for sentiment and content themes, such as natural health or personal freedom, in real time. A later dataset evaluated the relationship between Twitter sentiment scores and vaccination rates in the United States. The real-time analytics framework showed a widening gap in sentiment with more negative sentiment after vaccine rollout. After rollout, using a static dataset, an increase in positive sentiment was followed by an increase in vaccination. Lag cross-correlation analysis across US regions showed evidence that once all adults were eligible for vaccination, the sentiment score consistently correlated with vaccination rate with a lag of around 1 week. The Granger causality test further demonstrated that tweet sentiment scores may help predict vaccination rates. Social media has influenced the COVID-19 response through valuable information and misinformation and distrust. This tool was used to collect and analyze tweets at scale in real time to study sentiment and key terms of interest. Separate tweet analysis showed that vaccination rates tracked regionally with Twitter vaccine sentiment and might forecast changes in vaccine uptake and/or guide targeted social media and vaccination strategies. Further work is needed to analyze the interplay between specific populations, vaccine sentiment, and vaccination rates.

“Please understand we cannot provide further information”: evaluating content and transparency of GDPR-mandated AI disclosures

AbstractThe General Data Protection Regulation (GDPR) of the EU confirms the protection of personal data as a fundamental human right and affords data subjects more control over the way their personal information is processed, shared, and analyzed. However, where data are processed by artificial intelligence (AI) algorithms, asserting control and providing adequate explanations is a challenge. Due to massive increases in computing power and big data processing, modern AI algorithms are too complex and opaque to be understood by most data subjects. Articles 15 and 22 of the GDPR provide a modest regulatory framework for automated data processing by, among other things, mandating that data controllers inform data subjects about when it is being used, and its logic and ramifications. Nevertheless, due to the phrasing of the articles and the numerous exceptions they allow, doubts have arisen about their effectiveness. In this paper, we empirically evaluate the quality and effectiveness of AI disclosures as mandated by the GDPR. By means of an online survey (N = 835), we investigated how data subjects expect to be informed about the automated processing of their data. We then conducted a content analysis of the AI disclosures of N = 100 companies and organizations. The combined findings reveal that current GDPR-mandated disclosures do not meet the expectations and needs of data subjects. Explanations drawn up following the guidelines of the generic formulations of the GDPR differ widely and are often vague, incomplete and lack transparency. In our conclusions we identify a path towards standardizing and optimizing AI information notices.

Development of an Automatic Interpretation Algorithm for Uroflowmetry Results: Application of Artificial Intelligence.

PurposeTo develop an automatic interpretation system for uroflowmetry (UFM) results using machine learning (ML), a form of artificial intelligence (AI).MethodsA prospectively collected 1,574 UFM results (1,031 males, 543 females) with voided volume>150 mL was labelled as normal, borderline, or abnormal by 3 urologists. If the 3 experts disagreed, the majority decision was accepted. Abnormality was defined as a condition in which a urologist judges from the UFM results that further evaluation is required and that the patient should visit a urology clinic. To develop the optimal automatic interpretation system, we applied 4 ML algorithms and 2 deep learning (DL) algorithms. ML models were trained with all UFM parameters. DL models were trained to digitally analyze 2-dimensional images of UFM curves.ResultsThe automatic interpretation algorithm achieved a maximum accuracy of 88.9% in males and 90.8% in females when using 6 parameters: voided volume, maximum flow rate, time to maximal flow rate, average flow rate, flow time, and voiding time. In females, the DL models showed a dramatic improvement in accuracy over the other models, reaching 95.4% accuracy in the convolutional neural network model. The performance of the DL models in clinical discrimination was outstanding in both genders, with an area under the curve of up to 0.957 in males and 0.974 in females.ConclusionsWe developed an automatic interpretation algorithm for UFM results by training AI models using 6 key parameters and the shape of the curve; this algorithm agreed closely with the decisions of urology specialists.

Using machine learning methods to predict prolonged operative time in elective total shoulder arthroplasty

BackgroundMachine learning (ML) is a form of artificial intelligence in which computer algorithms improve automatically with experience. Recently, ML has been utilized to predict operative characteristics and patient outcomes for orthopedic procedures, thereby allowing for better patient selection and preoperative planning. This study sought to develop ML models to aid in predicting operative time and 30-day postoperative complications for elective total shoulder arthroplasty and to compare them to regression models. MethodsThis cross-sectional national database study identified patients who underwent elective total shoulder arthroplasty from 2012 to 2018 in the American College of Surgeons National Surgical Quality Improvement Program registry. Boosted decision tree and artificial neural network (ANN) ML models were developed to predict prolonged operative time and 30-day postoperative complication rates. Model performance was measured using the area under the receiver operating characteristic curve and overall accuracy. Multivariate binary logistic regression analyses were also used to identify variables that predicted prolonged operative time and 30-day postoperative complication rates. ML model performance was then compared to the regression models in predicting outcomes. ResultsIn total, 21,544 elective total shoulder arthroplasty procedures met inclusion criteria. Variables associated with greater odds of prolonged operative time included male sex (odds ratio [OR] = 0.66; 95% confidence interval [CI] = 0.61-0.71; P < .001), obesity (OR = 1.19; 95% CI = 1.09-1.29; P < .001), age under 70 years (OR = 0.77; 95% CI = 0.71-0.85; P < .001), smoking history (OR = 1.16; 95% CI = 1.03-1.32; P = .022), and history of cancer (OR = 2.91; 95% CI = 1.52-5.54; P = .001). The boosted decision tree model yielded an area under the curve (AUC) of 0.642, with an overall accuracy of 85.6% for predicting prolonged operative time. The ANN model had an AUC of 0.906 and overall accuracy of 84.7%, while the regression model had an AUC of 0.590 with overall accuracy of 85.6%. Thirty-day complication rate (7.7% vs. 3.9%, respectively; P < .001) and reoperation rate (1.8% vs. 1.2%, respectively; P = .006) also differed significantly between the prolonged operative duration and normal operative duration cohorts. ConclusionThis is the first study to successfully develop ML models for predicting operative time in total shoulder arthroplasty and compare them to existing methods of data analysis. The ANN model was superior to the other models in predicting prolonged operative time. With regard to 30-day postoperative complications, both ML models displayed fair predictive capacity, compared to the regression model, which had poor predictive performance. Level of evidenceLevel III; Database Retrospective Comparative Cohort Study

Industry 4.0 oriented predictive analytics of cardiovascular diseases using machine learning, hyperparameter tuning and ensemble techniques

PurposeThe world is shifting towards the fourth industrial revolution (Industry 4.0), symbolising the move to digital, fully automated habitats and cyber-physical systems. Industry 4.0 consists of innovative ideas and techniques in almost all sectors, including Smart health care, which recommends technologies and mechanisms for early prediction of life-threatening diseases. Cardiovascular disease (CVD), which includes stroke, is one of the world’s leading causes of sickness and deaths. As per the American Heart Association, CVDs are a leading cause of death globally, and it is believed that COVID-19 also influenced the health of cardiovascular and the number of patients increases as a result. Early detection of such diseases is one of the solutions for a lower mortality rate. In this work, early prediction models for CVDs are developed with the help of machine learning (ML), a form of artificial intelligence that allows computers to learn and improve on their own without requiring to be explicitly programmed.Design/methodology/approachThe proposed CVD prediction models are implemented with the help of ML techniques, namely, decision tree, random forest, k-nearest neighbours, support vector machine, logistic regression, AdaBoost and gradient boosting. To mitigate the effect of over-fitting and under-fitting problems, hyperparameter optimisation techniques are used to develop efficient disease prediction models. Furthermore, the ensemble technique using soft voting is also used to gain more insight into the data set and accurate prediction models.FindingsThe models were developed to help the health-care providers with the early diagnosis and prediction of heart disease patients, reducing the risk of developing severe diseases. The created heart disease risk evaluation model is built on the Jupyter Notebook Web application, and its performance is calculated using unbiased indicators such as true positive rate, true negative rate, accuracy, precision, misclassification rate, area under the ROC curve and cross-validation approach. The results revealed that the ensemble heart disease model outperforms the other proposed and implemented models.Originality/valueThe proposed and developed CVD prediction models aims at predicting CVDs at an early stage, thereby taking prevention and precautionary measures at a very early stage of the disease to abate the predictive maintenance as recommended in Industry 4.0. Prediction models are developed on algorithms’ default values, hyperparameter optimisations and ensemble techniques.

Can AI Help Us Solve the Population Health Crisis?

Inside Precision MedicineVol. 9, No. 1 ArticlesCan AI Help Us Solve the Population Health Crisis?We live in an age of wide-reaching population health problems, such as diabetes, cardiovascular disease, and obesity, that have a huge physical, mental, and financial cost for patients and providers. However, new forms of artificial intelligence (AI) could be the answer to solving these population health problems in an efficient and cost-effective way.Helen Albert, Senior EditorHelen Albert, Senior EditorHelen Albert is Senior Editor at Inside Precision Medicine and a freelance science journalist. Prior to going freelance, she was Editor-in-Chief at Labiotech, an English-language, digital publication based in Berlin focusing on the European biotech industry. Before moving to Germany, she worked at a range of different science and health-focused publications in London. She was Editor of The Biochemist magazine and blog, but also worked as a Senior Reporter at Springer Nature's medwireNews for a number of years, as well as freelancing for various international publications. She has written for New Scientist, Chemistry World, Biodesigned, The BMJ, Forbes, Science Business, Cosmos magazine, and GEN. Helen has academic degrees in genetics and anthropology, and also spent some time early in her career working at the Sanger Institute in Cambridge before deciding to move into journalism.Search for more papers by this authorPublished Online:3 Mar 2022https://doi.org/10.1089/ipm.09.01.02AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 9Issue 1Feb 2022 InformationCopyright © GEN PublishingTo cite this article:Helen Albert, Senior Editor.Can AI Help Us Solve the Population Health Crisis?.Inside Precision Medicine.Feb 2022.6-10.http://doi.org/10.1089/ipm.09.01.02Published in Volume: 9 Issue 1: March 3, 2022PDF download