Healthcare predictive analytics: An overview with a focus on Saudi Arabia

The current state, challenges, and future directions of artificial intelligence in healthcare in Saudi Arabia: systematic review

Background

The use of artificial intelligence has been part of the healthcare technologies used in managing various aspects of healthcare processes. In Saudi Arabia, the use of artificial intelligence for managing healthcare has been influenced by the increasing use of healthcare technologies within the healthcare system. The aim of this study is to systematically review the current state, challenges, and future directions of artificial intelligence in healthcare in Saudi Arabia.

Methods

The study used a systematic review methodology, which used the critical appraisal of articles on the use of artificial intelligence in healthcare. The critical appraisal used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Joanna Briggs Institute (JBI) to implement the inclusion and exclusion criteria. The initial search for articles led to 88 articles, which were screened to 13, based on the inclusion and exclusion criteria.

Results

The current state of the use of artificial intelligence in Saudi's healthcare system has been slowed down by the gradual uptake of healthcare technologies and the investments required. The main challenges identified included lack of policies to support artificial intelligence, lack of adequate capital for infrastructure and human resources and lack of cultures to accommodate the artificial intelligence in Saudi Arabia. With the current privatization and increased use of the artificial intelligence, the future of artificial intelligence in Saudi's healthcare system would see an increase in their utilization. Specific findings indicate the potential of artificial intelligence in improving clinical practice through blockchain, and that investments in artificial intelligence have encompasses various applications, including radiology. Skills gaps expected among healthcare professionals and the adoption of new technology are difficulties impacting the utilization of artificial intelligence in the healthcare sector.

Conclusion

The use of artificial intelligence in Saudi's healthcare system requires the investments into infrastructure, human resource development and gradual commitments towards the healthcare technologies. The use of artificial intelligence would have benefits such as effectiveness in access to care and ability to meet the healthcare outcomes.

A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment

The integration of artificial intelligence (AI) into dental care holds the promise of revolutionizing the field by enhancing the accuracy of dental diagnosis and treatment. This paper explores the impact of AI in dental care, with a focus on its applications in diagnosis, treatment planning, and patient engagement. AI-driven dental imaging and radiography, computer-aided detection and diagnosis of dental conditions, and early disease detection and prevention are discussed in detail. Moreover, the paper delves into how AI assists in personalized treatment planning and provides predictive analytics for dental care. Ethical and privacy considerations, including data security, fairness, and regulatory aspects, are addressed, highlighting the need for a responsible and transparent approach to AI implementation. Finally, the paper underscores the potential for a collaborative partnership between AI and dental professionals to offer the best possible care to patients, making dental care more efficient, patient-centric, and effective. The advent of AI in dentistry presents a remarkable opportunity to improve oral health outcomes, benefiting both patients and the healthcare community.

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.

Digital health transformation in Saudi Arabia: A cross-sectional analysis using Healthcare Information and Management Systems Society’ digital health indicators

Background

The digital revolution has had a huge impact on healthcare around the world. Digital technology could dramatically improve the accuracy of diagnosis, treatment, health outcomes, efficiency of care, and workflow of healthcare operations. Using health information technology will bring major improvements in patient outcomes.

Purpose

This study aims to measure the readiness for digital health transformation at different hospitals in the Eastern Province, Saudi Arabia in relation to Saudi Vision 2030 based on the four dimensions adopted by the Healthcare Information and Management Systems Society: person-enabled health, predictive analytics, governance and workforce, and interoperability.

Methods

The study was conducted with a cross-sectional design using data collected through an online questionnaire from 10 healthcare settings, the questionnaire consists of the four digital health indicators. The survey was developed by Healthcare Information and Management Systems Society for the purpose of assessing the level of digital maturity in healthcare settings.

Results

Ten healthcare facilities in the Eastern Province, both private and governmental, were included in the study. The highest total scores for digital health transformation were reported in private healthcare facilities (median score for private facilities = 77, public facilities = 71). The ‘governance and workforce’ was the most implemented dimension among the healthcare facilities in the study (median = 80), while the dimension that was least frequently implemented was predictive analytics (median score = 70). In addition, tertiary hospitals scored the least in digital transformation readiness (median = 74) compared to primary and secondary healthcare facilities in the study.

Conclusion

The results of the study show that private healthcare facilities scored higher in digital health transformation indicators. These results will be useful for promoting policymakers’ understanding of the level of digital health transformation in the Eastern Province and for the creation of a strategic action plan.

Health informatics publication trends in Saudi Arabia: a bibliometric analysis over the last twenty-four years

OBJECTIVE

Understanding health informatics (HI) publication trends in Saudi Arabia may serve as a framework for future research efforts and contribute toward meeting national "e-Health" goals. The authors' intention was to understand the state of the HI field in Saudi Arabia by exploring publication trends and their alignment with national goals.

METHODS

A scoping review was performed to identify HI publications from Saudi Arabia in PubMed, Embase, and Web of Science. We analyzed publication trends based on topics, keywords, and how they align with the Ministry of Health's (MOH's) "digital health journey" framework.

RESULTS

The total number of publications included was 242. We found 1 (0.4%) publication in 1995-1999, 11 (4.5%) publications in 2000-2009, and 230 (95.0%) publications in 2010-2019. We categorized publications into 3 main HI fields and 4 subfields: 73.1% (n=177) of publications were in clinical informatics (85.1%, n=151 medical informatics; 5.6%, n=10 pharmacy informatics; 6.8%, n=12 nursing informatics; 2.3%, n=4 dental informatics); 22.3% (n=54) were in consumer health informatics; and 4.5% (n=11) were in public health informatics. The most common keyword was "medical informatics" (21.5%, n=52). MOH framework-based analysis showed that most publications were categorized as "digitally enabled care" and "digital health foundations."

CONCLUSIONS

The years of 2000-2009 may be seen as an infancy stage of the HI field in Saudi Arabia. Exploring how the Saudi Arabian MOH's e-Health initiatives may influence research is valuable for advancing the field. Data exchange and interoperability, artificial intelligence, and intelligent health enterprises might be future research directions in Saudi Arabia.

Epidemiology of cancer in Saudi Arabia thru 2010-2019: a systematic review with constrained meta-analysis

Background

Cancer is emerging as a major global health-care system challenge with a growing burden worldwide. Due to the inconsistent cancer registry system in Saudi Arabia, the epidemiology of cancer is still dispersed in the country. Consequently, this review aimed to assemble the epidemiological metrics of cancer in Saudi Arabia in light of the available published data during the period from (2010–2019).

Methods

Published literature from Saudi Arabia relating to cancer incidence, prevalence, risk factors, and other epidemiological metrics were accessed through electronic search in Medline/PubMed, Cochrane, Scopus, Web of Knowledge, Google Scholar, and public database that meet the inclusion criteria. Relevant keywords were used during the electronic search about different types of cancers in Saudi Arabia. No filters were used during the electronic searches. Data were pooled and odds ratios (ORs) and 95% confidence interval (95%CI) were calculated. A random-effects meta-analysis was performed to assess the well-determined risk factors associated with different types of cancers.

Results

The most common cancers in Saudi Arabia are breast, colorectal, prostate, brain, lymphoma, kidney and thyroid outnumbering respectively. Their prevalence rates and OR (95%CI) as follow: breast cancer 53% and 0.93 (0.84–1.00); colon-rectal cancer (CRC) 50.9% and 1.2 (0.81–1.77); prostate cancer 42.6% and 3.2 (0.88–31.11); brain/Central Nervous System cancer 9.6% and 2.3 (0.01–4.2); Hodgkin and non-Hodgkin's lymphoma 9.2% and 3.02 (1.48–6.17); kidney cancer 4.6% and 2.05 (1.61–2.61), and thyroid cancer 12.9% and 6.77 (2.34–19.53).

Conclusion

Within the diverse cancers reported from Saudi Arabia, the epidemiology of some cancers magnitude 3-fold in the latest years. This increase might be attributed to the changing in the Saudi population lifestyle (adopting western model), lack of cancer awareness, lack of screening & early detection programs, social barriers toward cancer investigations. Obesity, genetics, sedentary lifestyle, tobacco use, viral infection, and iodine & Vit-D deficiency represent the apparent cancer risk factors in Saudi Arabia.

The Internet of Things and Big Data Analytics for Chronic Disease Monitoring in Saudi Arabia

Background: Saudi Arabia is lagging behind developed countries in devising specific real projects, roadmaps, and policies for the Internet of Things (IoT) and big data adoption despite having a vision for providing the best-quality health care services to its citizens. As a result, Saudi Arabia is going to host an event for the third time, in 2020, promoting the widescale adoption of the IoT. While a nationwide study has identified the risk that many participants were previously undiagnosed for hypertension and other chronic diseases in Saudi Arabia, the application of the IoT and big data technologies could be very useful in minimizing such risks by predicting chronic disease earlier, and on a large scale. Materials and Methods: A framework that consists of four modules, (1) data collection, (2) data storage, (3) Hadoop/Spark cluster, and (4) Google Cloud, was developed in which decision tree and support vector machine (SVM) techniques were used for predicting hypertension. There were 140 participants in total and 20% of participants were used for training the model. Results: The results show that age and diabetes play a very significant part in diagnosing hypertension in older people. Also, it was found that the possibility of hypertension because of smoking is less than that of diabetes, and older people should have a lower intake of salty food. Moreover, it was found that SVM techniques yielded better results than C4.5 in our study. Conclusions: Although it was found that the algorithms examined in this study can be used for disease prediction, the ability to classify and predict disease is not yet sufficiently satisfactory. To achieve this, more training data and a longer duration are required. Finally, by supporting such study for developing custom-made smart wristbands, custom-made smart clothing, and custom-made smart homes that can predict and detect a wide range of chronic diseases, the Saudi government can achieve its health-related goals of Vision 2030.