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Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. [ABSTRACT FROM AUTHOR]

Certain Saudi healthcare organizations transfer outpatients to medical imaging departments for radiological examinations in a manual process that relies on the use of paper-based forms. With the increased implementation of electronic medical records in Saudi Hospitals, little is known about the completeness and legibility of information captured in electronic-based medical imaging forms. The purpose of this study is to audit the completeness and legibility of medical imaging paper-based forms in comparison with electronic-based medical imaging forms. As a secondary objective, we also examined the number of errors found on the forms. An observational retrospective cross-sectional study was utilized to audit the completeness and legibility of both paper and electronic forms collected between March 1 and May 15, 2015. The study measured the association among categorical variables using Chi-Square analysis. The results of this investigation show a significant association between form completion and type of record (i.e., paper vs. electronic) where electronic-based systems were found to be more complete than paper-based records. Electrnoic based records were also found to improve form legibility, promote user adherence to complete the forms and minimize entry errors. In conclusion, electronic-based medical imaging forms are more complete and legible than paper based forms. Future studies should evaluate other hospitals and compare both legibility and completeness of electronic-based medical imaging forms and conduct usability evaluation studies with users to explore the impacts of system design on both completeness and legibility of electronic forms, in general, but more specifically, electronic-based medical imaging forms. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to review and analysis of the state of the art regarding triage applications (apps) for health emergencies. This research is based on a systematic review of the literature in scientific databases from 2010 to early 2021, following a prism methodology. In addition, a Google Play Store search of the triage apps found in the literature was performed for further evaluation. A total of 26 relevant papers were obtained for this study, of which 13 apps were identified. After searching for each of these apps in the Google Play Store platform, only 2 of them were obtained, and these were subsequently evaluated together with another app obtained from the link provided in the corresponding paper. In the analysis carried out, it was detected that from 2019 onwards there has been an increase in research interest in this area, since the papers obtained from this year onwards represent 38.5% of the relevant papers. This increase may be caused by the need for early selection of the most serious patients in such difficult times for the health service. According to the review carried out, an increase in mobile app research focused on Emergency Triage and a decrease in app studies for triage catastrophe have been identified. In this study it was also observed that despite the existence of many researches in this sense, only 3 apps contained in them are accessible. "TRIAGIST" does not allow the entry of an unidentified user, "Major Trauma Triage Tool" presents negative comments from users who have used it and "ESITriage" lacks updates to improve its performance. [ABSTRACT FROM AUTHOR]

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis. [ABSTRACT FROM AUTHOR]

Over the last two decades, metric-based instruments have garnered popularity in mental health. Self-administered surveys, such as the Patient Health Questionnaire 9 (PHQ 9), have been leveraged to inform treatment practice of Major Depressive Disorder (MDD). The aim of this study was to measure the reliability and usability of a novel voice-based delivery system of the PHQ 9 using Amazon Alexa within a patient population. Forty-one newly admitted patients to a behavioral medicine clinic completed the PHQ 9 at two separate time points (first appointment and one-month follow up). Patients were randomly assigned to a version (voice vs paper) completing the alternate format at the next appointment. Patients additionally completed a 26-item User Experience Questionnaire (UEQ) and open-ended questionnaire at each session. Assessments between PHQ 9 total scores for the Alexa and paper version showed a high degree of reliability (α =.86). Quantitative UEQ results showed significantly higher overall positive attitudes towards the Alexa format with higher subscale scores on attractiveness, stimulation, and novelty. Further qualitative responses supported these findings with 85.7% of participants indicating a willingness to use the device at home. With the benefit of user instruction in a clinical environment, the novel Alexa delivery system was shown to be consistent with the paper version giving evidence of reliability between the two formats. User experience assessments further showed a preference for the novel version over the traditional format. It is our hope that future studies may examine the efficacy of the Alexa format in improving the at-home clinical treatment of depression. [ABSTRACT FROM AUTHOR]

Objective: The objective of this paper is to review and analyze the current state of telemedicine and ehealth in the field of vascular surgery. Methods: This paper collects the relevant information obtained after reviewing the articles related to telemedicine in vascular surgery, published from 2012 to 2022 contained in scientific databases. In addition, the results obtained are statistically studied based on various factors, such as the year of publication or the search engine. In this way, we obtain a complete vision of the current state of telemedicine in the field of vascular surgery. Results: After performing this search and applying selection criteria, 29 articles were obtained for subsequent study and discussion, of which 20 were published in the second half of the decade, representing 70% of the results. In the analysis carried out according to the search criteria used, it can be seen that using the word telemedicine we obtained 69% of the articles while with the criteria mHealth and eHealth we only obtained 22% and 9% of the results, respectively. It can be seen that the filter with the most potential content articles was “vascular surgery AND telemedicine”. In the analysis performed according to the search engine, it was observed that the Google Scholar database contains 93% of the articles found in the massive search and the relevant articles contained therein represent 52% of the total. Conclusion: An upward trend has been observed in recent years, with a clear increase in the number of publications and much lower figures in the first years. One aspect to highlight is that 47.8% of the articles analyzed focus only on postoperative treatment, which may be due to the help provided by telemedicine in detecting surgical site infections by sending images and videos, this being one of the most common postoperative complications. The analyzed works show the importance of telemedicine in vascular surgery and identify possible future lines of research. In the analysis carried out on the origin of the selected relevant papers, an important interest of the US in this topic is demonstrated since more than 50% of the research contains authors from this country, it is also observed that there is no research from Spain, so this research would be an initial step to determine the weaknesses of telemedicine in this field of medicine and a good opportunity to open a research gap in this branch. [ABSTRACT FROM AUTHOR]

Over the past decade, healthcare systems have started to establish control centres to manage patient flow, with a view to removing delays and increasing the quality of care. Such centres—here dubbed Healthcare Capacity Command/Coordination Centres (HCCCs)—are a challenge to design and operate. Broad-ranging surveys of HCCCs have been lacking, and design for their human users is only starting to be addressed. In this review we identified 73 papers describing different kinds of HCCCs, classifying them according to whether they describe virtual or physical control centres, the kinds of situations they handle, and the different levels of Rasmussen's [1] risk management framework that they integrate. Most of the papers (71%) describe physical HCCCs established as control centres, whereas 29% of the papers describe virtual HCCCs staffed by stakeholders in separate locations. Principal functions of the HCCCs described are categorised as business as usual (BAU) (48%), surge management (15%), emergency response (18%), and mass casualty management (19%). The organisation layers that the HCCCs incorporate are classified according to the risk management framework; HCCCs managing BAU involve lower levels of the framework, whereas HCCCs handling the more emergent functions involve all levels. Major challenges confronting HCCCs include the dissemination of information about healthcare system status, and the management of perspectives and goals from different parts of the healthcare system. HCCCs that take the form of physical control centres are just starting to be analysed using human factors principles that will make staff more effective and productive at managing patient flow. [ABSTRACT FROM AUTHOR]

The release of smartphones and tablets, which offer more advanced communication and computing capabilities, has led to the strong emergence of mHealth on the market. mHealth systems are being used to improve patients' lives and their health, in addition to facilitating communication between doctors and patients. Researchers are now proposing mHealth applications for many health conditions such as dementia, autism, dysarthria, Parkinson's disease, and so on. Usability becomes a key factor in the adoption of these applications, which are often used by people who have problems when using mobile devices and who have a limited experience of technology. The aim of this paper is to investigate the empirical usability evaluation processes described in a total of 22 selected studies related to mHealth applications by means of a Systematic Literature Review. Our results show that the empirical evaluation methods employed as regards usability could be improved by the adoption of automated mechanisms. The evaluation processes should also be revised to combine more than one method. This paper will help researchers and developers to create more usable applications. Our study demonstrates the importance of adapting health applications to users' need. [ABSTRACT FROM AUTHOR]

Within medical applications there are two main types of information design; paper-based and digital information [1]. As technology is constantly changing, information within healthcare management and delivery is continually being transitioned from traditional paper documents to digital and online resources. Activity of Daily Living (ADL) charts are still predominantly paper based and are therefore prone to 'human error' [2]. In light of this, an investigation has taken place into the design for reducing the amount of human error, between a paper based ADL, specifically the Barthel Index, and the same ADL created digitally. The digital ADL was developed as an online platform as this offers the best method of data capture for a large group of participants all together [3]. The aim of the study was to evaluate the usability of the Barthel Index ADL in paper format and then reproduce the same ADL digitally. This paper presents the findings of a study involving 26 participants who were familiar with ADL charts, and used three scenarios requiring them to complete both a paper ADL and a digital ADL. An evaluation was undertaken to ascertain if there were any 'human errors' in completing the paper ADL and also to find similarities/differences through using the digital ADL. The results from the study indicated that 22/26 participants agreed that the digital ADL was better, if not the same as a paper based ADL. Further results indicated that participants rate highly the added benefit of the digital ADL being easy to use and also that calculation of assessment scores were performed automatically. Statistically the digital BI offered a 100 % correction rate in the total calculation, in comparison to the paper based BI where it is more common for users to make mathematical calculation errors. Therefore in order to minimise handwriting and calculation errors, the digital BI proved superior than the traditional paper based method. [ABSTRACT FROM AUTHOR]

Singapore, like many other nations globally, had to contend with significant caseloads arising from the Coronavirus disease (COVID-19) pandemic. This paper focuses on using technology as an intervention for pandemic management. With scant scientific evidence on effective medications and vaccinations (i.e., pharmaceutical interventions) initially, disease containment strategies predominated during the early phases. Non-pharmaceutical interventions were critical in slowing disease transmission and preventing public healthcare institutions from being overwhelmed. Such interventions could be broadly divided into case-based interventions (e.g., contact tracing and quarantining of close contacts) and population-based measures (e.g., mask use and social distancing). The paper describes Singapore’s experience in the operational implementation of contact-based interventions, and illustrates how harnessing the digital edge enabled fast, accurate, resource-efficient, and flexible execution of ground operations. Singapore applied digital technology and developed an integrated system to facilitate issuance and acknowledgement of quarantine orders, submission of COVID-19 test results, and collection of antigen rapid test kits at the population level. Data was obtained from this proprietary centralised, automated platform. The paper demonstrates how such simple, yet elegant systems could have a direct impact on disease transmission in an outbreak setting and on population health. Moving forward, it is recommended that technology and digital solutions feature prominently in work process designs beyond COVID-19 such as in the management of emerging infectious diseases and non-communicable diseases. [ABSTRACT FROM AUTHOR]

The editorial independence of biomedical journals allows flexibility to meet a wide range of research interests. However, it also is a barrier for coordination between journals to solve challenging issues such as racial bias in the scientific literature. A standardized tool to screen for racial bias could prevent the publication of racially biased papers. Biomedical journals would maintain editorial autonomy while still allowing comparable data to be collected and analyzed across journals. A racially diverse research team carried out a three-phase study to generate and test a racial bias assessment tool for biomedical research. Phase 1, an in-depth, structured literature search to identify recommendations, found near complete agreement in the literature on addressing race in biomedical research. Phase 2, construction of a framework from those recommendations, provides the major innovation of this paper. The framework includes three dimensions of race: 1) context, 2) tone and terminology, and 3) analysis, which are the basis for the Race Equity Vetting Instrument for Editorial Workflow (REVIEW) tool. Phase 3, pilot testing the assessment tool, showed that the REVIEW tool was effective at flagging multiple concerns in widely criticized articles. This study demonstrates the feasibility of the proposed REVIEW tool to reduce racial bias in research. Next steps include testing this tool on a broader sample of biomedical research to determine how the tool performs on more subtle examples of racial bias. [ABSTRACT FROM AUTHOR]

Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes. [ABSTRACT FROM AUTHOR]

The aim of the study is to assess the feasibility of Drugs Rational Usage Guideline System (DRUGS)-supported clinical pathway (CP) for breast carcinoma, cataract, inguinal hernia and 2-diabetes mellitus whether the application of such a system could improve work efficiency, medical safety, and decrease hospital cost. Four kinds of diseases which included 1773 cases (where 901 cases using paper-based clinical pathways and 872 cases using DRUGS-supported clinical pathways) were selected and their demographic and clinical data were collected. The evaluation criteria were length of stay, preoperative length of stay, hospital cost, antibiotics prescribed during hospitalization, unscheduled surgery, complications and prognosis. The median total LOS was 1 to 3 days shorter in the DRUGS-supported CP group as compared to the Paper-based CP group for all types ( p < 0.05). Totel hospital cost decreased significantly in the DRUGS-supported CP group than that in Paper-based CP group. About antibiotics prescribed during hospitalization, there were no statistically differences in the time of initial dose of antibiotic and the duration of administration except the choice of antibiotic categories. The proportion of DRUGS-supported clinical pathway conditions where a broad-spectrum antibiotic was prescribed decreased from 63.6 to 34.5 % ( p < 0.01) in the Paper-based group. While after the intervention, the differences were statistically not significant in unscheduled surgery, complications and prognosis. In this study, DRUGS-supported clinical pathway for breast carcinoma, cataract, inguinal hernia, 2-diabetes mellitus was smoothly shifted from a paper-based to an electronic system, and confer benefits at the hospital level. [ABSTRACT FROM AUTHOR]

In clinical practice, many drug therapies are associated with prolongation of the QT interval. In literature, estimation of the risk of prescribing drug-induced QT prolongation is mainly executed by means of logistic regression; only one paper reported the use of machine learning techniques. In this paper, we compare the performance of both techniques on the same dataset. High risk for QT prolongation was defined as having a corrected QT interval (QTc) ≥ 450 ms or ≥ 470 ms for respectively male and female patients. Both conventional statistical methods (CSM) and machine learning techniques (MLT) were used. All algorithms were validated internally and with a hold-out dataset of respectively 512 and 102 drug-drug interactions with possible drug-induced QTc prolongation. MLT outperformed the best CSM in both internal and hold-out validation. Random forest and Adaboost classification performed best in the hold-out set with an equal harmonic mean of sensitivity and specificity (HMSS) of 81.2% and an equal accuracy of 82.4% in a hold-out dataset. Sensitivity and specificity were both high (respectively 75.6% and 87.7%). The most important features were baseline QTc value, C-reactive protein level, heart rate at baseline, age, calcium level, renal function, serum potassium level and the atrial fibrillation status. All CSM performed similarly with HMSS varying between 60.3% and 66.3%. The overall performance of logistic regression was 62.0%. MLT (bagging and boosting) outperform CSM in predicting drug-induced QTc prolongation. Additionally, 19.2% was gained in terms of performance by random forest and Adaboost classification compared to logistic regression (the most used technique in literature in estimating the risk for QTc prolongation). Future research should focus on testing the classification on fully external data, further exploring potential of other (new) machine and deep learning models and on generating data pipelines to automatically feed the data to the classifier used. [ABSTRACT FROM AUTHOR]

To help clinicians find better evidence, a metadata schema for Evidence Based Medicine (EBM) is developed. Dublin Core metadata standard (DC) was adopted to help build a metadata schema. An experimental system was developed to test the validity of the metadata and full text papers of clinical therapy on stomach ulcer extracted using PubMed. An EBM metadata schema was developed. Citations were created from original papers using the metadata schema. Three clinicians evaluated papers by utilizing metadata and full texts respectively. Agreement of evaluation was analyzed, and the result on weighted kappa was 0.55 (95%CI, 0.42–0.67). It reveals that there is moderate agreement between evaluation of metadata citations and full texts. It is possible to use the metadata to select papers before reading the full texts. A further study should be made to prove the applicability of the metadata in the real world setting. [ABSTRACT FROM AUTHOR]

The authors report on the 2013 workshop which critically reflected on the prognoses and theses made for the year 2013 and introduces 3 papers where the workshop results have been summarized. They together with Wolfgang Herzog wanted to identify priorities in medical informatics research and necessary activities by policymakers as they try to forecast how health care could like in the year 2013.

The use of mobile health apps to improve diet and nutrition behaviors has increased in recent years. Several studies have described the benefits and advantages of this technology as a complement to interventions for improving nutrition behaviors and nutrition‐related health outcomes, including obesity indices and clinical parameters. Few of these works have developed clinical mobile health apps for children, and although parents play a critical role in children's nutrition behaviors, work targeting parents is scarce. The work presented in this paper describes the development of the PersuHabit app, a stand-alone mobile health app targeting parents to promote the intake of fruits and vegetables (FVs) and reduce the intake of ultra-processed foods (UPF) in children aged 6 to 10 years. The paper also presents the execution of an exploratory pilot study to assess the feasibility, acceptability, and preliminary effects of the PersuHabit app. The results are presented and discussed, and actions for further improvement of the PersuHabit app are identified. [ABSTRACT FROM AUTHOR]

Resource coordination in surgical scheduling remains challenging in health care delivery systems. This is especially the case in highly-specialized settings such as coordinating Intraoperative Neurophysiologic Monitoring (IONM) resources. Inefficient coordination yields higher costs, limited access to care, and creates constraints to surgical quality and outcomes. To maximize utilization of IONM resources, optimization-based algorithms are proposed to effectively schedule IONM surgical cases and technologists and evaluate staffing needs. Data with 10 days of case volumes, their surgery durations, and technologist staffing was used to demonstrate method effectiveness. An iterative optimization-based model that determines both optimal surgery and technologist start time (operational scenario 4) was built in an Excel spreadsheet along with Excel's Solver settings. It was compared with current practice (operational scenario 1) and optimization solution on only surgery start time (operational scenario 2) or technologist start time (operational scenario 3). Comparisons are made with respect to technologist overtime and under-utilization time. The results conclude that scenario 4 significantly reduces overtime by 74% and under-utilization time by 86% as well as technologist needs by 10%. For practices that do not have flexibility to alter surgeon preference on surgery start time or IONM technologist staffing levels, both scenarios 2 and 3 also result in substantial reductions in technologist overtime and under-utilization. Moreover, IONM technologist staffing options are discussed to accommodate technologist preferences and set constraints for surgical case scheduling. All optimization-based approaches presented in this paper are able to improve utilization of IONM resources and ultimately improve the coordination and efficiency of highly-specialized resources. [ABSTRACT FROM AUTHOR]

Emergency departments (EDs) have a key role in the public health system. They are facing a constant growth of their volume. Forecasting the daily volume is a major tool to adapt the allocation of resources. In this paper, we focus on pediatric EDs. They are specific by their strong seasonal variation, determined by the academic pace. The main contribution of this paper is to integrate the effects of this pace to the annual seasonality. We also tried out to improve the daily forecasting by forecasting the week means of the flow first. We trained and tested these models specifically on the pediatric EDs of Paris university hospital trust. For the eight pediatric EDs gathered, on average for the years 2016 to 2019, we forecasted the daily volume with a Mean Absolute Percentage Error (MAPE) of 6.6% for a 7-days forecasting, 7.1% for a 14-days forecasting and 7.6% for a 28-days forecasting. Account of rhythm allows a performance increase, with results respectively 7%, 10.1% and 8.4% better relatively to a baseline model based on a periodic regression on the weeks. [ABSTRACT FROM AUTHOR]

Biostatistics are indispensable in clinical medicine because doctors increasingly need to publish and understand medical articles that use statistical methods. An opportunity is needed for medical doctors to interact with biostatisticians on statistical issues. This paper's objective is to determine how much it costs to establish a biostatistics clinic and what revenue is required to run the clinic. A balance sheet was used to outline the revenue and expenses of a statistics clinic using a simple method introduced by the author. Expenses for personnel, material, and tenancy costs were 41.9, 18.1, and 25.1% of the total, respectively. To run, the biostatistics clinic required 200 research design engagements, 1200 h of hourly consultation, and 6600 h of data processing. To acquire scientific grants, each of four biostatisticians had to write one academic paper annually. The results show that a biostatistics clinic could be managed profitably based on the assumptions in this study. [ABSTRACT FROM AUTHOR]

With the advent of cryptocurrencies and blockchain, the growth and adaptation of cryptographic features and capabilities were quickly extended to new and underexplored areas, such as healthcare. Currently, blockchain is being implemented mainly as a mechanism to secure Electronic Health Records (EHRs). However, new studies have shown that this technology can be a powerful tool in empowering patients to control their own health data, as well for enabling a fool-proof health data history and establishing medical responsibility. Additionally, with the proliferation of mobile health (m-Health) sustained on service-oriented architectures, the adaptation of blockchain mechanisms into m-Health applications creates the possibility for a more decentralized and available healthcare service. Hence, this paper presents a review of the current security best practices for m-Health and the most used and widely known implementations of the blockchain protocol, including blockchain technologies in m-Health. The main goal of this comprehensive review is to further discuss and elaborate on identified open-issues and potential use cases regarding the uses of blockchain in this area. Finally, the paper presents the major findings, challenges and advantages on future blockchain implementations for m-Health services and applications. [ABSTRACT FROM AUTHOR]

The main goal of this article is to identify the main dimensions of a model proposal for increasing the potential of big data research in Healthcare for medical doctors' (MDs') learning, which appears as a major issue in continuous medical education and learning. The paper employs a systematic literature review of main scientific databases (PubMed and Google Scholar), using the VOSviewer software tool, which enables the visualization of scientific landscapes. The analysis includes a co-authorship data analysis as well as the co-occurrence of terms and keywords. The results lead to the construction of the learning model proposed, which includes four health big data key areas for MDs' learning: 1) data transformation is related to the learning that occurs through medical systems; 2) health intelligence includes the learning regarding health innovation based on predictions and forecasting processes; 3) data leveraging regards the learning about patient information; and 4) the learning process is related to clinical decision-making, focused on disease diagnosis and methods to improve treatments. Practical models gathered from the scientific databases can boost the learning process and revolutionise the medical industry, as they store the most recent knowledge and innovative research. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O’Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python. [ABSTRACT FROM AUTHOR]

Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians. [ABSTRACT FROM AUTHOR]

In the ongoing expansion of medical systems toward prevention and health promotion, primary care is increasingly recognized as a key vehicle and agent. In order to establish a comprehensive health dialogue in the population, there is a need for innovative instruments corresponding to the information and linked media revolution that this shift is a part of. Based upon the experiences of the Department of Preventive Medicine in Malmö, Sweden, a self-mediated, interactive health testing and promotion instrument called "healthometer" has been developed and tested for feasibility. The instrument uses a special variety of a quintile-distributed visual analog scale with the thermometer as reference and allows: (a) easy summation and averaging of single or different aggregates of the test items as a "wellness" profile and score with emphasis upon the positive aspects of health, and (b) recognition against this on the whole favorable background of the weak points motivating further action. The instrument, which can be distributed on paper or electronic medium, supports participation and insight in the initial stages of a directed individual health program for the continuation of which the utilization of professional counsel is stimulated.