Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases.

BACKGROUND: Despite technological advancement in the field of healthcare, the worldwide burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a sharp increase in developing nations that are undergoing fast health transitions. People have been experimenting with techniques to extend their lives since ancient times. Despite this, technology is still a long way from attaining the aim of lowering mortality rates. METHODS: From methodological perspective, a design Science Research (DSR) approach is adopted in this research. As such, to investigate the current healthcare and interaction systems created for predicting cardiac disease for patients, we first analyzed the body of existing literature. After that, a conceptual framework of the system was designed using the gathered requirements. Based on the conceptual framework, the development of different components of the system was completed. Finally, the evaluation study procedure was developed taking into account the effectiveness, usability and efficiency of the developed system. RESULTS: To attain the objectives, we proposed a system consisting of a wearable device and mobile application, which allows the users to know their risk levels of having CVDs in the future. The Internet of Things (IoT) and Machine Learning (ML) techniques were adopted to develop the system that can classify its users into three risk levels (high, moderate and low risk of having CVD) with an F1 score of 80.4% and two risk levels (high and low risk of having CVD) with an F1 score of 91%. The stacking classifier incorporating best-performing ML algorithms was used for predicting the risk levels of the end-users utilizing the UCI Repository dataset. CONCLUSION: The resultant system allows the users to check and monitor their possibility of having CVD in near future using real-time data. Also, the system was evaluated from the Human-Computer Interaction (HCI) point of view. Thus, the created system offers a promising resolution to the current biomedical sector. TRIAL REGISTRATION: Not Applicable.

Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda.

Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilizes the ML techniques to predict the optimal mode of childbirth and to detect various complications during childbirth. A total of 26 articles (published between 2000 and 2020) from an initial set of 241 articles were selected and reviewed following a Systematic Literature Review (SLR) approach. As outcomes, this review study highlighted the objectives or focuses of the recent studies conducted on pregnancy outcomes using ML; explored the adopted ML algorithms along with their performances; and provided a synthesized view of features used, types of features, data sources and its characteristics. Besides, the review investigated and depicted how the objectives of the prior studies have changed with time being; and the association among the objectives of the studies, uses of algorithms, and the features. The study also delineated future research opportunities to facilitate the existing initiatives for reducing maternal complacent and mortality rates, such as: utilizing unsupervised and deep learning algorithms for prediction, revealing the unknown reasons of maternal complications, developing usable and useful ML-based clinical decision support systems to be used by the expecting mothers and health professionals, enhancing dataset and its accessibility, and exploring the potentiality of surgical robotic tools. Finally, the findings of this review study contributed to the development of a conceptual framework for advancing the ML-based maternal healthcare system. All together, this review will provide a state-of-the-art paradigm of ML-based maternal healthcare that will aid in clinical decision-making, anticipating pregnancy problems and delivery mode, and medical diagnosis and treatment.

A deep learning based multimodal interaction system for bed ridden and immobile hospital admitted patients: design, development and evaluation.

BACKGROUND: Hospital cabins are a part and parcel of the healthcare system. Most patients admitted in hospital cabins reside in bedridden and immobile conditions. Though different kinds of systems exist to aid such patients, most of them focus on specific tasks like calling for emergencies, monitoring patient health, etc. while the patients' limitations are ignored. Though some patient interaction systems have been developed, only singular options like touch, hand gesture or voice based interaction were provided which may not be usable for bedridden and immobile patients. METHODS: At first, we reviewed the existing literature to explore the prevailing healthcare and interaction systems developed for bedridden and immobile patients. Then, a requirements elicitation study was conducted through semi-structured interviews. Afterwards, design goals were established to address the requirements. Based on these goals and by using computer vision and deep learning technologies, a hospital cabin control system having multimodal interactions facility was designed and developed for hospital admitted, bedridden and immobile patients. Finally, the system was evaluated through an experiment replicated with 12 hospital admitted patients to measure its effectiveness, usability and efficiency. RESULTS: As outcomes, firstly, a set of user-requirements were identified for hospital admitted patients and healthcare practitioners. Secondly, a hospital cabin control system was designed and developed that supports multimodal interactions for bedridden and immobile hospital admitted patients which includes (a) Hand gesture based interaction for moving a cursor with hand and showing hand gesture for clicking, (b) Nose teeth based interaction where nose is used for moving a cursor and teeth is used for clicking and (c) Voice based interaction for executing tasks using specific voice commands. Finally, the evaluation results showed that the system is efficient, effective and usable to the focused users with 100% success rate, reasonable number of attempts and task completion time. CONCLUSION: In the resultant system, Deep Learning has been incorporated to facilitate multimodal interaction for enhancing accessibility. Thus, the developed system along with its evaluation results and the identified requirements provides a promising solution for the prevailing crisis in the healthcare sector. TRIAL REGISTRATION: Not Applicable.

Towards developing a secure medical image sharing system based on zero trust principles and blockchain technology.

BACKGROUND: Data security has been a critical topic of research and discussion since the onset of data sharing in e-health systems. Although digitalization of data has increased efficiency and speed, it has also made data vulnerable to cyber attacks. Medical records in particular seem to be the regular victims of hackers. Several data breach incidents throughout history have warranted the invention of security measures against these threats. Although various security procedures like firewalls, virtual private networks, encryption, etc are present, a mix of these approaches are required for maximum security in medical image and data sharing. METHODS: Relatively new, blockchain has become an effective tool for safeguarding sensitive information. However, to ensure overall protection of medical data (images), security measures have to be taken at each step, from the beginning, during and even after transmission of medical images which is ensured by zero trust security model. In this research, a number of studies that deal with these two concepts were studied and a decentralized and trustless framework was proposed by combining these two concepts for secured medical data and image transfer and storage. RESULTS: Research output suggested blockchain technology ensures data integrity by maintaining an audit trail of every transaction while zero trust principles make sure the medical data is encrypted and only authenticated users and devices interact with the network. Thus the proposed model solves a lot of vulnerabilities related to data security. CONCLUSIONS: A system to combat medical/health data vulnerabilities has been proposed. The system makes use of the immutability of blockchain, the additional security of zero trust principles, and the scalability of off chain data storage using Inter Planetary File Systems (IPFS). The adoption of this system suggests to enhance the security of medical or health data transmission.

Investigating usability of mobile health applications in Bangladesh.

BACKGROUND: Lack of usability can be a major barrier for the rapid adoption of mobile services. Therefore, the purpose of this paper is to investigate the usability of Mobile Health applications in Bangladesh. METHOD: We followed a 3-stage approach in our research. First, we conducted a keyword-based application search in the popular app stores. We followed the affinity diagram approach and clustered the found applications into nine groups. Second, we randomly selected four apps from each group (36 apps in total) and conducted a heuristic evaluation. Finally, we selected the highest downloaded app from each group and conducted user studies with 30 participants. RESULTS: We found 61% usability problems are catastrophe or major in nature from heuristic inspection. The most (21%) violated heuristic is aesthetic and minimalist design. The user studies revealed low System Usability Scale (SUS) scores for those apps that had a high number of usability problems based on the heuristic evaluation. Thus, the results of heuristic evaluation and user studies complement each other. CONCLUSION: Overall, the findings suggest that the usability of the mobile health apps in Bangladesh is not satisfactory in general and could be a potential barrier for wider adoption of mobile health services.

Proximal tubular dysfunction as a predictor of AKI in Hospitalized COVID-19 patients.

BACKGROUND: High concentration of Angiotensin converting enzyme receptors in the proximal tubules make kidneys an early target in COVID-19. Proximal tubular dysfunction (PTD) may act as an early predictor of acute kidney injury (AKI) and more severe disease. METHODS: This prospective observational study was conducted in the COVID unit, Bangabandhu Sheikh Mujib Medical University. 87 COVID-19 patients without known kidney disease were screened for 6 markers of PTD on admission-hyperuricosuria, normoglycemic glycosuria, proteinuria, renal phosphate leak, sodium leak and potassium leak. Positivity of 2 of the first 4 markers was considered as PTD. 35 patients with PTD and 35 without PTD were followed up throughout their hospital stay. RESULTS: 52.9% had PTD on admission. The most prevalent markers were renal sodium leak (67%), followed by proteinuria (66.7%), hyperuricosuria (42.5%), potassium leak (32.2%), phosphate leak (28.7%) and normoglycemic glycosuria (20.7%). Mean age was 55.7 years. 32.9% patients developed AKI. PTD group had higher odds of developing AKI (odds ratio 17.5 for stage 1, 24.8 for stage 2 and 25.5 for stage 3; p<0.0001). The mean duration of hospital stay was 9 days higher in the PTD group (p<0.001). PTD group also had higher odds of transferring to ICU (OR = 9.4, p = 0.002), need for mechanical ventilation (OR = 10.1, p = 0.002) and death (OR = 10.3, p = 0.001). 32.6% had complete PTD recovery during follow-up. CONCLUSION: Proximal tubular dysfunction is highly prevalent in COVID-19 patients very early in the disease and may act as a predictor of AKI, ICU transfer, need for mechanical ventilation and death.

Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson's coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques.

Polycystic Ovary Syndrome (PCOS) is among the most prevalent endocrinological abnormalities seen in reproductive female bodies posing serious health hazards. The correctness of interpreting this condition depends heavily on the wide spectrum of associated symptoms and the doctor's expertise, making real-time clinical detection quite challenging. Thus, investigations on computer-aided PCOS detection systems have recently been explored by several researchers worldwide as a potential replacement for manual assessment. This review study's objective is to analyze the relevant research works on computer-assisted methods for automatically identifying PCOS through a systematic literature review (SLR) methodology as well as investigate the research limitations and explore potential future research scopes in this domain. 28 articles have been selected using the PRISMA approach based on a set of inclusion-exclusion criteria for conducting the review. The data synthesis of the selected articles has been conducted using six data exploration themes. As outcomes, the SLR explored the topical association between the studies; their research profiles; objectives; data size, type, and sources; methodologies applied for the detection of PCOS; and lastly the research outcomes along with their evaluation measures and performances. The study also highlights areas for future research directions examining the study gaps to enhance the current efforts for autonomous PCOS identification; such as integrating advanced techniques with the current methods; developing interactive software systems; exploring deep learning and unsupervised machine learning techniques; enhancing datasets and country context; and investigating more unknown factors behind PCOS. Thus, this SLR provides a state-of-the-art paradigm of autonomous PCOS detection which will support significantly efficient clinical assessment, diagnosis and treatment of PCOS.

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique.

Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with 'Gradient Boosting' classifier as meta learner outperforms others with 95.7% accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.

Digital health interventions for cervical cancer care: A systematic review and future research opportunities.

BACKGROUND: Cervical cancer is a malignancy among women worldwide, which is responsible for innumerable deaths every year. The primary objective of this review study is to offer a comprehensive and synthesized overview of the existing literature concerning digital interventions in cervical cancer care. As such, we aim to uncover prevalent research gaps and highlight prospective avenues for future investigations. METHODS: This study adopted a Systematic Literature Review (SLR) methodology where a total of 26 articles were reviewed from an initial set of 1110 articles following an inclusion-exclusion criterion. RESULTS: The review highlights a deficiency in existing studies that address awareness dissemination, screening facilitation, and treatment provision for cervical cancer. The review also reveals future research opportunities like explore innovative approaches using emerging technologies to enhance awareness campaigns and treatment accessibility, consider diverse study contexts, develop sophisticated machine learning models for screening, incorporate additional features in machine learning research, investigate the impact of treatments across different stages of cervical cancer, and create more user-friendly applications for cervical cancer care. CONCLUSIONS: The findings of this study can contribute to mitigating the adverse effects of cervical cancer and improving patient outcomes. It also highlights the untapped potential of Artificial Intelligence and Machine Learning, which could significantly impact our society.

Exploring usability problems of mHealth applications developed for cervical cancer: An empirical study.

Objectives: Nowadays, mobile health applications are developed to raise awareness and facilitate screening and treatment of cervical cancer, while a very few studies have been conducted focusing on the measurement and assurance of usability and exploring the acceptable user experience of such applications. Usability issues become a crucial concern for such cervical-cancer-related applications because users with diverse backgrounds in terms of education, information technology literacy, and geographic reasons are required to access those applications. The objective of this research is to evaluate the usability of mobile health applications developed for cervical cancer patients. Methods: Two evaluation studies were conducted following the expert evaluation and a questionnaire-based user study. A total of four cervical-cancer-related applications that are focusing on the Awareness and Diagnosis theme were selected and each of the applications was evaluated by four usability experts. Then, a user study (n = 80) based on the Goal Question Metric was conducted to reveal the usability problems of four selected applications. Finally, findings of both evaluations were aggregated and analyzed. Results: Both approaches showed that all applications suffer from several usability problems while "Cervical Cancer Guide" performs better and "Cervical Cancer Tracker" showed the least in performance from the usability perspective. Again, the Goal Question Metric performs noticeably better in assessing the learnability of the applications, while the analytical heuristic evaluation performs better in identifying the issues that cause user annoyance. Conclusion: The methodology adopted and the usability problems revealed through this study can be well utilized by the information technology professionals or user interface designers for designing, evaluating, and developing the cervical-cancer-related applications with enhanced usability and user experience.

Clinical manifestations and outcomes of COVID-19 in maintenance hemodialysis patients of a high infectious epidemic country: a prospective cross-sectional study.

Introduction: The outbreak of COVID-19 poses great challenges for patients on maintenance haemodialysis. Here, we reported the clinical characteristics and laboratory features of maintenance haemodialysis (MHD) patients with COVID-19 in Bangladesh. Methods: Altogether, 67 MHD patients were enroled in the study from two dedicated tertiary-level hospitals for COVID-19 after the prospective cross-sectional execution of selection criteria. Data were collected from medical records and interviews. Different statistical analysis was carried out in the data analysis. Results: The mean age was 55.0±9.9 years, with 40 males (59.7%). The mean dialysis duration was 23.4±11.5 months. The most common symptoms were fever (82.1%), cough (53.7%), and shortness of breath (55.2%), while the common comorbid condition was hypertension (98.5%), followed by diabetes (56.7%). Among MHD patients, 52.2% to 79.1% suffered from severe to critical COVID-19, 48 patients (71.6%) had 26-75% lung involvement on high resolution computed tomography of the chest, 23 patients (34.3%) did not survive, 20 patients (29.9%) were admitted to ICU, and nine patients (13.4%) needed mechanical ventilation. Patients who did not survive were significantly older (mean age: 63.0 vs. 50.86 years, P=0.0001), had significantly higher cardiovascular risk factors (69.6% vs. 43.2%, P=0.04), severe shortness of breath (82.6% vs. 40.9%, P=0.0001), and longer hospital stays (mean days: 17.9 vs. 13.0, P=0,0001) compared to the survivor group. The white blood cell count, C-reactive protein, lactate dehydrogenase, pro-calcitonin, and thrombocytopenia were significantly (P<0.0001) higher, while the albumin level was significantly lower (P=0.0001) in non-survivor compared to patients who survived. Conclusion: Maintenance haemodialysis patients had severe to critical COVID-19 and had a higher risk of non-survival if they were older and had comorbidities such as hypertension and diabetes. Therefore, MHD patients with COVID-19 need close monitoring to improve their outcomes.

An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image.

Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality and one of the primary causes of anovulatory infertility in women globally. The detection of multiple cysts using ovary ultrasonograpgy (USG) scans is one of the most reliable approach for making an accurate diagnosis of PCOS and creating an appropriate treatment plan to heal the patients with this syndrome. Instead of depending on error-prone manual identification, an intelligent computer-aided cyst detection system can be a viable approach. Therefore, in this research, an extended machine learning classification technique for PCOS prediction has been proposed, trained and tested over 594 ovary USG images; where the Convolutional Neural Network (CNN) incorporating different state-of-the-art techniques and transfer learning has been employed for feature extraction from the images; and then stacking ensemble machine learning technique using conventional models as base learners and bagging or boosting ensemble model as meta-learner have been used on that reduced feature set to classify between PCOS and non-PCOS ovaries. The proposed technique significantly enhances the accuracy while also reducing training execution time comparing with the other existing ML based techniques. Again, following the proposed extended technique, the best performing results are obtained by incorporating the "VGGNet16" pre-trained model with CNN architecture as feature extractor and then stacking ensemble model with the meta-learner being "XGBoost" model as image classifier with an accuracy of 99.89% for classification.

COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets.

The COVID-19 outbreak has created effects on everyday life worldwide. Many research teams at major pharmaceutical companies and research institutes in various countries have been producing vaccines since the beginning of the outbreak. There is an impact of gender on vaccine responses, acceptance, and outcomes. Worldwide promotion of the COVID-19 vaccine additionally generates a huge amount of discussions on social media platforms about diverse factors of vaccines including protection and efficacy. Twitter is considered one of the most well-known social media platforms which have been widely used to share a public opinion on vaccine-related problems in the COVID-19 pandemic. However, there is a lack of research work to analyze the public perception of COVID-19 vaccines systematically from a gender perspective. In this paper, we perform an in-depth analysis of the coronavirus vaccine-related tweets to understand the people's sentiment towards various vaccine brands corresponding to the gender level. The proposed method focuses on the effect of COVID-19 vaccines on gender by taking into account descriptive, diagnostic, predictive, and prescriptive analytics on the Twitter dataset. We also conduct experiments with deep learning models to determine the sentiment polarities of tweets, which are positive, neutral, and negative. The results reveal that LSTM performs better compared to other models with an accuracy rate of 85.7%.

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective.

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1-score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID-19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.

UVC-PURGE: A Novel Cost-Effective Disinfection Robot for Combating COVID-19 Pandemic.

During the COVID-19 pandemic, surface disinfection using prevailing chemical disinfection methods had several limitations. Due to cost-inefficiency and the inability to disinfect shaded places, static UVC lamps cannot address these limitations properly. Moreover, the average market price of the prevailing UVC robots is huge, approximately 55,165 USD. In this research firstly, a requirement elicitation study was conducted using a semi-structured interview approach to reveal the requirements to develop a cost-effective UVC robot. Secondly, a semi-autonomous robot named UVC-PURGE was developed based on the revealed requirements. Thirdly, a two-phased evaluation study was undertaken to validate the effectiveness of UVC-PURGE to inactivate the SARS-CoV-2 virus and the capability of semi-autonomous navigation in the first phase and to evaluate the usability of the system through a hybrid approach of SUPR-Q forms and subjective evaluation of the user feedback in the second phase. Pre-treatment swab testing revealed the presence of both Gram-positive and Gram-Negative bacteria at 17 out of 20 test surfaces in the conducted tests. After the UVC irradiation of the robot, the microbial load was detected in only 2 (1D and 1H) out of 17 test surfaces with significant reductions (95.33% in 1D and 90.9% in 1H) of microbial load. Moreover, the usability evaluation yields an above-average SUPR-Q score of 81.91% with significant scores in all the criteria (usability, trust, loyalty, and appearance) and the number of positive themes from the subjective evaluation using thematic analysis is twice the number of negative themes. Additionally, compared with the prevailing UVC disinfection robots in the market, UVC-PURGE is cost-effective with a price of less than 800 USD. Moreover, small form factor along with the real time camera feedback in the developed system helps the user to navigate in congested places easily. The developed robot can be used in any indoor environment in this prevailing pandemic situation and it can also provide cost-effective disinfection in medical facilities against the long-term residual effect of COVID-19 in the post-pandemic era.

A Systematic Review of the Digital Interventions for Fighting COVID-19: The Bangladesh Perspective.

The objective of this paper is to synthesize the digital interventions initiatives to fight against COVID-19 in Bangladesh and compare with other countries. In order to obtain our research objective, we conducted a systematic review of the online content. We first reviewed the digital interventions that have been used to fight against COVID-19 across the globe. We then reviewed the initiatives that have been taken place in Bangladesh. Thereafter, we present a comparative analysis between the initiatives taken in Bangladesh and the other countries. Our findings show that while Bangladesh is capable to take benefits of the digital intervention approaches, tighter cooperation between government and private organizations as well as universities would be needed to get the most benefits. Furthermore, the government needs to make sure that the privacy of its citizens are protected.

A Review on the Mobile Applications Developed for COVID-19: An Exploratory Analysis.

The objective of this research is to explore the existing mobile applications developed for the COVID-19 pandemic. To obtain this research objective, firstly the related applications were selected through the systematic search technique in the popular application stores. Secondly, data related to the app objectives, functionalities provided by the app, user ratings, and user reviews were extracted. Thirdly, the extracted data were analyzed through the affinity diagram, noticing-collecting-thinking, and descriptive analysis. As outcomes, the review provides a state-of-the-art view of mobile apps developed for COVID-19 by revealing nine functionalities or features. It revealed ten factors related to information systems design characteristics that can guide future app design. The review outcome highlights the need for new development and further refinement of the existing applications considering not only the revealed objectives and their associated functionalities, but also revealed design characteristics such as reliability, performance, usefulness, supportive, security, privacy, flexibility, responsiveness, ease of use, and cultural sensitivity.

A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.

Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic. From an initial set of 634 articles, a total of 49 articles were finally selected through an inclusion-exclusion process. In this article, we have explored the objectives of the existing studies (i.e., the role of AI/ML in fighting the COVID-19 pandemic); the context of the studies (i.e., whether it was focused on a specific country-context or with a global perspective; the type and volume of the dataset; and the methodology, algorithms, and techniques adopted in the prediction or diagnosis processes). We have mapped the algorithms and techniques with the data type by highlighting their prediction/classification accuracy. From our analysis, we categorized the objectives of the studies into four groups: disease detection, epidemic forecasting, sustainable development, and disease diagnosis. We observed that most of these studies used deep learning algorithms on image-data, more specifically on chest X-rays and CT scans. We have identified six future research opportunities that we have summarized in this paper. Impact Statement: Artificial intelligence (AI) and machine learning(ML) methods have been widely used to assist in the fight against COVID-19 pandemic. A very few in-depth literature reviews have been conducted to synthesize the knowledge and identify future research agenda including a previously published review on data science for COVID-19 in this article. In this article, we synthesized reviewed recent literature that focuses on the usages and applications of AI and ML to fight against COVID-19. We have identified seven future research directions that would guide researchers to conduct future research. The most significant of these are: develop new treatment options, explore the contextual effect and variation in research outcomes, support the health care workforce, and explore the effect and variation in research outcomes based on different types of data.