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The Pharma-safe Index Dataset comprises comprehensive information on several aspects of pharmaceuticals, including general medication data, usage guidelines, dosage, adverse effects, pricing, drug interactions, duration of use, composition, and contraindications. The dataset is provided in both CSV and JSON file formats, and it is available in both English and Bahasa Indonesia. By conducting interviews, distributing flyers, and using pharmacy books, the dataset was collected from over-the-counter medications that were sold at three pharmacies located in Yogyakarta, Indonesia. A medical professional performed cleansing, standardization, and validation on it before it was exported to JSON and CSV formats. The data collected on drug efficacy, safety, and patient outcomes in Indonesia can be utilized by researchers in order to uncover trends and developing patterns of prescription drug resistance. It is possible that this will direct future research, lead to improvements in drug formulations, treatment strategies, and public health policies, and expand our understanding of how drugs work and how they affect patient health., (© 2024 The Author(s).)

Background: Transtibial prosthetic sockets are often grouped into patella tendon bearing (PTB) or total surface bearing (TSB) designs, but many variations in rectifications are used to apply these principles to an individual's personalised socket. Prosthetists currently have little objective evidence to assist them as they make design choices., Aims: To compare rectifications made by experienced prosthetists across a range of patient demographics and limb shapes to improve understanding of socket design strategies., Methodology: 163 residual limb surface scans and corresponding CAD/CAM sockets were analysed for 134 randomly selected individuals in a UK prosthetics service. This included 142 PTB and 21 TSB designs. The limb and socket scans were compared to determine the location and size of rectifications. Rectifications were compiled for PTB and TSB designs, and associations between different rectification sizes were assessed using a variety of methods including linear regression, kernel density estimation (KDE) and a Naïve Bayes (NB) classification., Results: Differences in design features were apparent between PTB and TSB sockets, notably for paratibial carves, gross volume reduction and distal end elongation. However, socket designs varied across a spectrum, with most showing a hybrid of the PTB and TSB principles. Pairwise correlations were observed between the size of some rectifications (e.g., paratibial carves; fibular head build and gross volume reduction). Conversely, the patellar tendon carve depth was not associated significantly with any other rectification, indicating its relative design insensitivity. The Naïve Bayes classifier produced design patterns consistent with expert clinician practice. For example, subtle local rectifications were associated with a large volume reduction (i.e., a TSB-like design), whereas more substantial local rectifications (i.e., a PTB-like design) were associated with a low volume reduction., Clinical Implications: This study demonstrates how we might learn from design records to support education and enhance evidence-based socket design. The method could be used to predict design features for newly presenting patients, based on categorisations of their limb shape and other demographics, implemented alongside expert clinical judgement as smart CAD/CAM design templates., Competing Interests: ASD, JLB, CR, JWS and PRW are co-founders and/or employees and/or shareholders of Radii Devices Ltd., and DH, JB and ZT are employees of Opcare Ltd. However, the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest, and the research does not involve any products. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Dickinson, Steer, Rossides, Diment, Mbithi, Bramley, Hannett, Blinova, Tankard and Worsley.)

One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make an early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts and uses a Waterfall Paradigm and Unified Modeling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with a sensitivity of 87.5%. The system can also provide recommendations for the treatment of identified diseases. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Agus F et al.)

Preservation of cultural relics is crucial for cultural sustainability, particularly in the case of cave temples, which are a unique and immovable part of our heritage, nested within mountains. These structures bear immense historical significance and possess considerable economic value, making them vulnerable to criminal activities, notably theft. Establishing a robust physical protection system (PPS) is imperative to safeguard these relics from potential damage. This paper proposes a novel approach for assessing the vulnerability of cave temples' PPS. Based on the fuzzy Petri net (FPN) principle, a comprehensive vulnerability assessment index system was developed within the PPS framework, considering the unique characteristics of cave temples. This study refines the formal definition of the FPN, enhancing its precision and effectiveness for vulnerability assessment. The practicality and effectiveness of the proposed method are verified through simulation experiments. An illustrative example is provided to demonstrate this approach., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: The vocational guidance process in educational institutions faces important challenges in managing trials and errors in diagnoses. Technological tools are identified as an effective solution to address these problems. This research seeks to improve career guidance in educational institutions through the implementation of an expert system. The main objective is to reduce test processing time and achieve greater efficiency in students' self-knowledge regarding their interests, based on the personalities of the Holland Test., Methods: The development of the expert system followed a six-model approach. First, an organisational model was created to assess the scope and feasibility of the project. Next, a task and agent model was developed to investigate the impact and look for improvements. A knowledge model was then developed to analyse the relevant knowledge bases. A communication model was also developed to evaluate the communication interface of the system. Next, a design model was created to provide guidelines for the implementation of the system. Finally, the implementation of the knowledge system was carried out to ensure its correct functioning., Results: The implementation of the expert system has shown significant improvements in the vocational guidance process. It was possible to reduce the time needed to apply the test, thus optimising the psychologist's time and allowing a greater capacity for analysis. In addition, an improvement in the effectiveness of the students' self-knowledge in relation to their vocational interests based on the personalities of the Holland Test was observed., Conclusions: This study contributes to career guidance in educational institutions by introducing an innovative expert system. This technological solution optimizes the career guidance process, benefiting psychologists administering tests and students seeking self-knowledge about their career interests., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Huayta-Gómez J and Pacheco A.)

Objective: To compare the laboratory tests conducted in real-life settings for patients with anemia with the expected prescriptions derived from an optimal checkup., Methods: A panel of experts formulated an "optimal laboratory test assessment" specific to each anemia profile. A retrospective analysis was done of the laboratory tests conducted according to the type of anemia (microcytic, normocytic or macrocytic). Using an algorithmic system, the laboratory tests performed in real-life practice were compared with the recommendations suggested in the "optimal laboratory test assessment" and with seemingly "unnecessary" laboratory tests., Results: In the analysis of the "optimal laboratory test assessment", of the 1179 patients with microcytic anemia, 269 (22.8%) had had one of the three tests recommended by the expert system, and only 33 (2.8%) had all three tests. For normocytic anemia, 1054 of 2313 patients (45.6%) had one of the eleven recommended tests, and none had all eleven. Of the 384 patients with macrocytic anemia, 196 (51%) had one of the four recommended tests, and none had all four. In the analysis of "unnecessary laboratory tests", one lab test was unnecessarily done in 727/3876 patients (18.8%), i.e. 339 of 1179 (28.8%) microcytic, 171 of 2313 (7.4%) normocytic, and 217 of 384 (56.5 %) macrocytic anemias., Conclusion: Laboratory investigations of anemia remain imperfect as more than half of the cases did not receive the expected tests. Analyzing other diagnostic domains, the authors are currently developing an artificial intelligence system to assist physicians in enhancing the efficiency of their laboratory test prescriptions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier B.V.)

Objectives: Maternal complications are health challenges linked to pregnancy, encompassing conditions like gestational diabetes, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. The diagnosis of common pregnancy complications is challenging due to the similarity in signs and symptoms with general pregnancy indicators, especially in settings with scarce resources where access to healthcare professionals, diagnostic tools, and patient record management is limited. This paper presents a rule-based expert system tailored for diagnosing three prevalent maternal complications: preeclampsia, gestational diabetes mellitus (GDM), and maternal sepsis., Methods: The risk factors associated with each disease were identified from various sources, including local health facilities and literature reviews. Attributes and rules were then formulated for diagnosing the disease, with a Mamdani-style fuzzy inference system serving as the inference engine. To enhance usability and accessibility, a web-based user interface has been also developed for the expert system. This interface allows users to interact with the system seamlessly, making it easy for them to input relevant information and obtain accurate disease diagnose., Results: The proposed expert system demonstrated a 94% accuracy rate in identifying the three maternal complications (preeclampsia, GDM, and maternal sepsis) using a set of risk factors. The system was deployed to a custom-designed web-based user interface to improve ease of use., Conclusions: With the potential to support health services provided during antenatal care visits and improve pregnant women's health outcomes, this system can be a significant advancement in low-resource setting maternal healthcare., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Background: Menopause is a period of women's life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women's related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the "Healthy Menopause" expert system on the management of menopausal symptoms., Methods/design: A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women's experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts' opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves., Discussion: This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system "Healthy Menopause". This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner., (© 2024. The Author(s).)

Background: Owing to structural-level, interpersonal-level, and individual-level barriers, Latino men have disproportionately high rates of physical inactivity and experience related chronic diseases. Despite these disparities, few physical activity (PA) interventions are culturally targeted for Latino men., Objective: This study reported the feasibility and acceptability of Hombres Saludables PA intervention for Latino men. We also reported the preliminary efficacy of the intervention on PA change and provided the results of the exploratory moderator and mediator analysis., Methods: We completed a 6-month, single-blind, pilot randomized controlled trial of Hombres Saludables with Latino men aged between 18 and 65 years. Men were randomized to either (1) a theory-driven, individually tailored, internet-based and SMS text message-based, Spanish-language PA intervention arm or (2) a nutrition and wellness attention contact control arm that was also delivered via the web and SMS text message. We assessed the primary study outcomes of feasibility using participant retention and acceptability using postintervention survey and open-ended interview questions. We measured the preliminary efficacy via change in minutes of moderate to vigorous PA per week using ActiGraph wGT3X-BT accelerometry (primary measure) and self-reported minutes per week using 7-day Physical Activity Recall. Participants completed the assessments at study enrollment and after 6 months., Results: The 38 participants were predominantly Dominican (n=8, 21%) or Guatemalan (n=5, 13%), and the mean age was 38.6 (SD 12.43) years. Retention rates were 91% (21/23) for the PA intervention arm and 100% (15/15) for the control arm. Overall, 95% (19/20) of the intervention arm participants reported that the Hombres study was somewhat to very helpful in getting them to be more physically active. Accelerometry results indicated that participants in the intervention group increased their PA from a median of 13 minutes per week at study enrollment to 34 minutes per week at 6 months, whereas the control group participants showed no increases. On the basis of self-reports, the intervention group was more likely to meet the US PA guidelines of 150 minutes per week of moderate to vigorous PA at 6-month follow-up, with 42% (8/19) of the intervention participants meeting the PA guidelines versus 27% (4/15) of the control participants (odds ratio 3.22, 95% CI 0.95-13.69). Exploratory analyses suggested conditional effects on PA outcomes based on baseline stage of motivational readiness, employment, and neighborhood safety., Conclusions: The PA intervention demonstrated feasibility and acceptability. Results of this pilot study indicate that the Hombres Saludables intervention is promising for increasing PA in Latino men and suggest that a fully powered trial is warranted. Our technology-based PA intervention provides a potentially scalable approach that can improve health in a population that is disproportionately affected by low PA and related chronic disease., Trial Registration: ClinicalTrials.gov NCT03196570; https://classic.clinicaltrials.gov/ct2/show/NCT03196570., International Registered Report Identifier (irrid): RR2-10.2196/23690., (©Akilah J Dulin, Shira Dunsiger, Tanya Benitez, Britta Larsen, Bess H Marcus, Gregory Champion, Kim M Gans. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.12.2023.)

Background: Comprehensive models of survivorship care are necessary to improve access to and coordination of care. New models of care provide the opportunity to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer treatment., Objective: This paper presents our expert-informed, rules-based survivorship algorithm to build a nurse-led model of survivorship care to support men living with prostate cancer (PCa). The algorithm is called No Evidence of Disease (Ned) and supports timelier decision-making, enhanced safety, and continuity of care., Methods: An initial rule set was developed and refined through working groups with clinical experts across Canada (eg, nurse experts, physician experts, and scientists; n=20), and patient partners (n=3). Algorithm priorities were defined through a multidisciplinary consensus meeting with clinical nurse specialists, nurse scientists, nurse practitioners, urologic oncologists, urologists, and radiation oncologists (n=17). The system was refined and validated using the nominal group technique., Results: Four levels of alert classification were established, initiated by responses on the Expanded Prostate Cancer Index Composite for Clinical Practice survey, and mediated by changes in minimal clinically important different alert thresholds, alert history, and clinical urgency with patient autonomy influencing clinical acuity. Patient autonomy was supported through tailored education as a first line of response, and alert escalation depending on a patient-initiated request for a nurse consultation., Conclusions: The Ned algorithm is positioned to facilitate PCa nurse-led care models with a high nurse-to-patient ratio. This novel expert-informed PCa survivorship care algorithm contains a defined escalation pathway for clinically urgent symptoms while honoring patient preference. Though further validation is required through a pragmatic trial, we anticipate the Ned algorithm will support timelier decision-making and enhance continuity of care through the automation of more frequent automated checkpoints, while empowering patients to self-manage their symptoms more effectively than standard care., International Registered Report Identifier (irrid): RR2-10.1136/bmjopen-2020-045806., (©Kaylen J Pfisterer, Raima Lohani, Elizabeth Janes, Denise Ng, Dan Wang, Denise Bryant-Lukosius, Ricardo Rendon, Alejandro Berlin, Jacqueline Bender, Ian Brown, Andrew Feifer, Geoffrey Gotto, Shumit Saha, Joseph A Cafazzo, Quynh Pham. Originally published in JMIR Cancer (https://cancer.jmir.org), 04.10.2023.)

The paediatric orthopaedic expert system analyses and predicts the healing time of limb fractures in children using machine learning. As far we know, no published research on the paediatric orthopaedic expert system that predicts paediatric fracture healing time using machine learning has been published. The University Malaya Medical Centre (UMMC) offers paediatric orthopaedic data, comprises children under the age of 12 radiographs limb fractures with ages recorded from the date and time of initial trauma. SVR algorithms are used to predict and discover variables associated with fracture healing time. This study developed an expert system capable of predicting healing time, which can assist general practitioners and healthcare practitioners during treatment and follow-up. The system is available online at https://kidsfractureexpert.com/., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Representing knowledge in a comprehensible and maintainable way and transparently providing inferences thereof are important issues, especially in the context of applications related to artificial intelligence in medicine. This becomes even more obvious if the knowledge is dynamically growing and changing and when machine learning techniques are being involved. In this paper, we present an approach for representing knowledge about cancer therapies collected over two decades at St.-Johannes-Hospital in Dortmund, Germany. The presented approach makes use of InteKRator, a toolbox that combines knowledge representation and machine learning techniques, including the possibility of explaining inferences. An extended use of InteKRator's reasoning system will be introduced for being able to provide the required inferences. The presented approach is general enough to be transferred to other data, as well as to other domains. The approach will be evaluated, e. g., regarding comprehensibility, accuracy and reasoning efficiency.

The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid ® V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.

Guava (Psidium guajava) is a nutritious fruit known for its origin in Mexico, Central or South America, and the Caribbean. Its production faces declining levels, infections, and disease outbreaks. This dataset focuses on the early identification of guava diseases using image processing and computer vision. Farmers can detect and address diseases promptly by developing an expert system, increasing yields and reducing economic losses. The technology behind this dataset enables sustainable guava farming and disease prevention. This dataset consists of digital and thermal images of guava fruits, including healthy, damaged, and various diseased conditions such as wilt, Anthracnose, canker, and rot. The images are categorized based on the fruit's maturity level (mature, half-mature, and mature) and captured under different drop heights. The dataset also includes information on the damage-inducing methods, storage conditions, image capture schedule, and specific diseases present. The thermal images were acquired using hot air with controlled temperature and velocity., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Background: Accurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates., Method: An iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design., Results: According to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7)., Conclusion: Through a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations., (© 2023. The Author(s).)

Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations.

Purpose: Wii Fit exergames have been less commonly used for the rehabilitation of athletes after Anterior Cruciate Ligament Reconstruction (ACLR). This study aims to investigate the effects of an expert system using Wii Fit exergames compared to conventional rehabilitation following ACLR. A forward-chaining rule-based expert system was developed which proposed a rehabilitation program that included the number and type of exercise in terms of difficulty and ease and the duration of each exercise in a progressive manner according to the patient's physical condition., Materials and Methods: Twenty eligible athletes aged 20-30 who underwent ACLR were enrolled in this study and randomly assigned to two groups; and received 12 sessions of either Wii Fit exergames as Wii group (n = 10) or conventional rehabilitation as CL group (n = 10)., Results: The main outcomes consisted of pain (Visual Analogue Scale (VAS)), knee effusion, knee flexion range (KFR), thigh girth (TG), single-leg hop for distance (SLHD), and for time (SLHT), static and dynamic balance tests. Both groups had considerable improvement in all outcomes, also there were significantly differences between Wii and CL groups as follows; VAS (P < 0.001), knee effusion (P < 0.001), TG (P = 0.001), KFR (P = 0.012), static balance in stable position (P < 0.001) and in unstable position (P = 0.001), dynamic balance in the anterior (P < 0.001), posteromedial (P < 0.001), posterolateral (P = 0.004) directions, symmetry index of SLHD (P < 0.001) and symmetry index of SLHT (P = 0.013)., Conclusions: The findings showed that using Wii Fit exergames in post-ACLR patients reduced pain and effusion while also improving function and balance significantly. Iranian Registry of Clinical Trials registration number is IRCT20191013045090N1, and the registration date is 03-03-2020., (© 2023. The Author(s).)

Gout is a systemic disease that is caused by the deposition of monosodium urate crystals in various tissues which leads to inflammation in them. This disease is often misdiagnosed. It leads to the lack of adequate medical care and development of serious complications, such as urate nephropathy and disability. The current situation can be improved by optimizing the medical care provided to patients, which requires searching for new strategies in terms of diagnosis. One of these strategies is the development of an expert system for providing information assistance to medical specialists which was a purpose of this study. The developed prototype expert system for gout diagnosis has knowledge base including 1144 medical concepts and 5 640 522 links, intelligent knowledge base editor and software which helps practitioner make the final decision. It has sensitivity of 91,3% [95% CI, 89,1%-93,1%], specificity of 85,4% [95% CI, 82,9%-87,6%] and AUROC 0,954 [95% CI, 0,944-0,963].

Competing Interests: Declaration of competing interest None.

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.

Introduction: The prevalence of thalassemia among the Vietnamese population was studied, and clinical decision support systems for prenatal screening of thalassemia were created. The aim of this report was to investigate the prevalence of thalassemia in the Vietnamese population, building a clinical decision support system for prenatal screening for thalassemia., Methods: A cross-sectional study was conducted on pregnant women and their husbands visiting the Vietnam National Hospital of Obstetrics and Gynecology from October 2020 to December 2021. A total of 10112 medical records of first-time pregnant women and their husbands were collected., Results: A clinical decision support system was built, including 2 different types of systems for prenatal screening for thalassemia (an expert system and 4 AI-based CDSS). One thousand nine hundred ninety-two cases were used to train and test machine learning models, while 1555 cases were used for specialized expert system evaluation. There were ten key variables for AI-based CDSS for machine learning. The four most important features in thalassemia screening were identified. The accuracy of the expert system and AI-based CDSS was compared. The rate of patients with Alpha thalassemia is 10.73% (1085 patients), the rate of patients with beta-thalassemia is 2.24% (227 patients), and 0.29% (29 patients) of patients carry both alpha-thalassemia and beta-thalassemia gene mutations. The expert system showed an accuracy of 98.45%. Among the AI-based CDSS developed, the multilayer perceptron (MLP) model was the most stable regardless of the training database (accuracy of 98,5% using all features and 97% using only the four most important features)., Conclusions: When comparing the expert system with the AI-based CDSS, the accuracy of the expert system and AI-based models was comparable. The developed expert system for prenatal thalassemia screening showed high accuracy. AI-based CDSS showed satisfactory results. Further development of such systems is promising with a view to their introduction into clinical practice., Competing Interests: Competing interests: The authors declare no conflict of Interest.

In professional training, it is important to provide students with opportunities to make judgments on practical cases. However, most training courses are conducted in a one-to-many teaching mode, and it is not easy to consider the needs of individual students. In this study, a technology-supported Decision, Reflection, and Interaction (DRI)-based professional training approach is proposed to cope with this problem for those courses aiming at fostering students' competence in making correct judgments when facing real cases. To verify the effectiveness of the proposed method, an experiment was conducted. Two classes of 38 students from a nursing school were the participants. One class was an experimental group using the DRI-based professional training approach, and the other class was the control group using the conventional technology-assisted training approach. The experimental results showed that applying the proposed approach significantly improved the students' learning achievement and self-efficacy more than the conventional technology-assisted approach. In addition, based on the interview results, the students generally believed that learning through the DRI-based professional training approach benefited them from several perspectives, including "increasing the value of activities", "enhancing the planning and expensive capacity of conspicuous approaches", "promoting decision-making", "improving learning reflection", and "providing students with personalized interaction".

Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error. To overcome this problem some intelligent models have been proposed; however, most of them are single classifier-based models and due to this these models cannot handle noisy and imbalanced data properly and thus sometimes overfit the model. To reduce bias and variance, and to avoid overfitting of a single classifier-based model, this paper proposes an ensemble-based PD diagnosis model, named Ensembled Expert System for Diagnosis of Parkinson's Disease (EESDPD) with relevant features and a simple stacking ensemble technique. The proposed EESDPD aggregates diverse assumptions for making the prediction. The performance of the proposed EESDPD is compared with the performances of logistic regression, SVM, Naïve Bayes, Random Forest, XGBoost, simple Decision Tree, B-TDS-PD and B-TESM-PD in terms of classification accuracy, precision, recall and F1-score measures.

Background: Smartphone applications offer a novel platform for delivering health information to parents. This study created and evaluated an app-based symptom checker that recommends educational tools to parents based on their child's symptoms., Methods: Symptoms extracted from 23 knowledge translation (KT) tools for 10 children's illnesses comprised a set of plain-language symptoms. The symptom checker works by producing confusion matrices evaluating a child's reported symptoms against possible illnesses, comparing precision scores to examine how well each illness matched reported symptoms, and ordering possible illnesses by performance score. Performance was evaluated by extracting symptoms from 8 clinical vignettes, and examining correct first-try matches., Results: We created a final list of 54 plain-language symptoms. Visualizations of the symptom set creation process and logic mapping are presented, as well as images of the working symptom checker. The symptom checker matched 100% (8/8) of tested clinical vignettes to the appropriate illness resource., Discussion: Symptom checkers are a potentially useful tool to integrate into apps that parents use for their children's health. The design of these systems has the potential to change parents' relationship with technology, affecting both their adoption and acceptance of symptom checkers. Our design choices contribute to addressing current barriers to the adoption of symptom checkers, reducing functional, critical, and interactive literacy requirements for parents., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: James Benoit reports a relationship with Oracle for Research that includes: funding grants. All authors work with PEC Innovation Board member Sarah Elliott (JB, LH, SDS)., (© 2023 The Authors.)

Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks , related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk , also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk . This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global Risk is obtained, which after interpretation can be used to establish the patient's status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis., Competing Interests: The authors declare no conflict of interest.

In this study, we develop an artificial intelligence model to predict the vulnerability of broiler production systems (broilers and facilities) to heat conditions using a fuzzy model approach. The model was designed with a multiple-input and a single-output (MISO) approach (input: physical environment and broilers age; output: degree of vulnerability of broilers system). For the validation of the fuzzy model, two approaches were used: (1) records from the scientific literature and (2) meteorological forecasts. First, we validated the model fuzzy with data from the scientific literature; second, we validate the model with data from meteorological forecasts. Both validation approaches were performed in different scenarios of the thermal environment (comfort, discomfort, and discomfort + low heat exchange), broilers' age (21-35 days, 25-39 days, and 28-42 days), and relative cooling efficiency (0% inefficient; and 80% efficient). Then, we applied the model to predict the degree of vulnerability of the broiler system with the help of weather forecasts. The recall and precision of the fuzzy model were high (> 0.9) for the thermal comfort and thermal discomfort + low heat exchange scenarios. In contrast, the fuzzy model was moderate agreement (recall 0.45; precision 0.64) for the thermal discomfort scenario compared to the scientific literature. The application of the model with the weather forecast showed the interaction between the physical and biological systems when submitted to a thermal environment challenge. Regardless of the broilers' age, a high degree of vulnerability was observed in facilities with inefficient cooling system. The fuzzy model developed in this study was efficient to predict the vulnerability of the broiler production system to heat conditions, further, to identify the uncertain conditions associated with broilers' age, relative humidity, and the relative cooling efficiency of the facilities., (© 2023. The Author(s) under exclusive licence to International Society of Biometeorology.)

Background: The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue., Methods: This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses., Results: 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy., Conclusions: Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy., (© 2023. The Author(s).)

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%).

Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee's physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems.

Hemostasis laboratories play a crucial role in the diagnosis and treatment of individuals with bleeding or thrombotic disorders. Routine coagulation assays, including the prothrombin time (PT)/international normalized ratio (INR), and activated partial thromboplastin time (APTT), are used for various purposes. These include as a screen of hemostasis function/dysfunction (e.g., possible factor deficiency) and for monitoring of anticoagulant therapy, such as vitamin K antagonists (PT/INR) and unfractionated heparin (APTT). Clinical laboratories are also under increasing pressure to improve services, especially response (test turnaround) times. There is also a need for laboratories to try to reduce error rates and for laboratory networks to standardize/harmonize processes and policies. Accordingly, we describe our experience with the development and implementation of automated processes for reflex testing and validation of routine coagulation test results. This has been implemented in a large pathology network compromising 27 laboratories and is under consideration for expansion to our larger network (of 60 laboratories). These rules have been custom-built within our laboratory information system (LIS), perform reflex testing of abnormal results, and fully automate the process of routine test validation for appropriate results. These rules also permit adherence to standardized pre-analytical (sample integrity) checks, automate reflex decisions, automate verification, and provide an overall alignment of network practices in a large network of 27 laboratories. In addition, the rules enable clinically significant results to be quickly referred to hematopathologists for review. We also documented an improvement in test turnaround times, with savings in operator time and thus operating costs. Finally, the process was generally well received and determined to be beneficial for most laboratories in our network, in part identified by improved test turnaround times., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In silico models are often built solely on publicly available data which may mean that they are less predictive for proprietary chemical space. Data sharing initiatives can improve the performance of such models, but organisations are often unable to share their data due to the need to protect their business interests and maintain the confidentiality of the chemicals in their research and development programmes. In silico models like Derek Nexus, which use expert knowledge to develop structural alerts based on chemical toxicity, can use proprietary data to identify new areas of chemical space and/or refine existing alerts whilst still preserving the privacy of the confidential data. Five hundred and thirty seven proprietary chemicals with skin sensitisation data were shared which led to the implementation of 7 new alerts and 5 modified alerts, with a concomitant 19% increase in sensitivity and 3% increase in specificity of the model., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Martyn Chilton reports a relationship with Lhasa Limited that includes: employment. Corresponding author (Donna Macmillan) was previously employed by, and co-author Martyn Chilton is currently employed by, Lhasa Limited. Lhasa Limited produce Derek Nexus, the in silico tool used in the data sharing project and described within the manuscript., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Learning from past experiences is essential for the adoption of Nature-Based Solutions (NBS). There is a growing number of knowledge repositories sharing the experience of NBS projects implemented worldwide. These repositories provide access to a large amount of information, however, acquiring knowledge from them remains a challenge. This paper outlines the technical details of the NBS Case-Based System (NBS-CBS), an expert system that facilitates knowledge acquisition from an NBS case repository. The NBS-CBS is a hybrid system integrating a black-box Artificial Neural Network (ANN) with a white-box Case-Based Reasoning model. The system involves:•a repository that stores the information of past NBS projects, and an input collection component, guiding the collection and encoding of the user's inputs;•a classifier that predicts solutions (i.e., generates a hypothesis), based on user input (target case), drawing on a pre-trained ANN model to guide the case retrieval, and a case retrieval engine that identifies cases similar to the target case;•a case adaption and retainment process in which the user assesses the provided recommendations and retains the solved problem as a new case in the repository., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Deriving knowledge and learning from past experiences is essential for the successful adoption of Nature-Based Solutions (NBS) as novel integrative solutions that involve many uncertainties. Past experiences in implementing NBS have been collected in a number of repositories; however, it is a major challenge to derive knowledge from the huge amount of information provided by these repositories. This calls for information systems that can facilitate the knowledge extraction process. This paper introduces the NBS Case-Based System (NBS-CBS), an expert system that uses a hybrid architecture to derive information and recommendations from an NBS experience repository. The NBS-CBS combines a 'black-box' artificial neural networks model with a 'white-box' case-based reasoning model to deliver an intelligent, adaptive, and explainable system. Experts have tested this system to assess its functionality and accuracy. Accordingly, the NBS-CBS appears to provide inspirational recommendations and information for the NBS planning and design process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Radial, ulnar, or median nerve injuries are common peripheral nerve injuries. They usually present specific abnormal signs on the hands as evidence for hand surgeons to diagnose. However, without specialized knowledge, it is difficult for primary healthcare providers to recognize the clinical meaning and the potential nerve injuries through the abnormalities, often leading to misdiagnosis. Developing technologies for automatically detecting abnormal hand gestures would assist general medical service practitioners with an early diagnosis and treatment., Methods: Based on expert experience, we selected three hand gestures with predetermined features and rules as three independent binary classification tasks for abnormal gesture detection. Images from patients with unilateral radial, ulnar, or median nerve injuries and healthy volunteers were obtained using a smartphone. The landmark coordinates were extracted using Google MediaPipe Hands to calculate the features. The receiver operating characteristic curve was employed for feature selection. We compared the performance of rule-based models with logistic regression, support vector machine and of random forest machine learning models by evaluating the accuracy, sensitivity, and specificity., Results: The study included 1,344 images, twenty-two patients, and thirty-four volunteers. In rule-based models, eight features were finally selected. The accuracy, sensitivity, and specificity were (1) 98.2, 91.7, and 99.0% for radial nerve injury detection; (2) 97.3, 83.3, and 99.0% for ulnar nerve injury detection; and (3) 96.4, 87.5, and 97.1% for median nerve injury detection, respectively. All machine learning models had accuracy above 95% and sensitivity ranging from 37.5 to 100%., Conclusion: Our study provides a helpful tool for detecting abnormal gestures in radial, ulnar, or median nerve injuries with satisfying accuracy, sensitivity, and specificity. It confirms that hand pose estimation could automatically analyze and detect the abnormalities from images of these patients. It has the potential to be a simple and convenient screening method for primary healthcare and telemedicine application., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gu, Fan, Cai, Wang, Liu, Yang and Zhu.)

Mosquito identification and classification are the most important steps in a surveillance program of mosquito-borne diseases. With conventional approach of data collection, the process of sorting and classification are laborious and time-consuming. The advancement of computer vision with transfer learning provides excellent alternative to the challenge. Transfer learning is a type of machine learning that is viable and durable in image classification with limited training images. This protocol aims to develop step-by-step procedure in developing a classification system with transfer learning algorithm for mosquito, we demonstrate the protocol to classify two species of Aedes mosquito - Aedes aegypti L. and Aedes albopitus L, but user can adopt the protocol for higher number of species classification. We demonstrated the way of start from the scratch, fine-tuning two pre-trained model performance by using different combination of hyperparameters - batch size and learning rate, and explain the terminology in the Appendix. This protocol target on the domain expert such as entomologist and public health practices to develop their own model to solve the task of mosquito/insect classification., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Background and Objectives: Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community., Methods: Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded., Results: In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others., Conclusion: We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest., (Copyright © 2022 Elsevier B.V. All rights reserved.)

It is widely accepted that brain death (BD) is a diagnosis based on clinical examination. However, false-positive and false-negative evaluation results may be serious limitations. Ancillary tests are used when there is uncertainty about the reliability of the neurologic examination. Computed tomography angiography (CTA) is an ancillary test that tends to have the lowest false-positive rates. However, there are various influencing factors that can have an unfavorable effect on the validity of the examination method. There are inconsistent protocols regarding the evaluation criteria such as scoring systems. Among the most widely used different scoring systems the 4-point CTA-scoring system has been accepted as the most reliable method. Appropriate timing and/or Doppler pre-testing could reduce the number of possible premature examinations and increase the sensitivity of CTA in diagnosing cerebral circulatory arrest (CCA). In some cases of inconclusive CTA, the whole brain computed tomography perfusion (CTP) could be a crucial adjunct. Due to the increasing significance of CTA/CTP in determining BD, the methodology (including benefits and limitations) should also be conveyed via innovative electronic training tools, such as the BRAINDEXweb teaching tool based on an expert system.

Background: Public community engagement is crucial for mosquito surveillance programs. To support community participation, one of the approaches is assisting the public in recognizing the mosquitoes that carry pathogens. Therefore, this study aims to build an automatic recognition system to identify mosquitos at the public community level. We construct a customized image dataset consisting of three mosquito species in either damaged or un-damaged body conditions. To distinguish the mosquito in harsh conditions, we explore two state-of-the-art deep learning (DL) architectures: (i) a freezing convolutional base, with partial trainable weights, and (ii) training the entire model with most of the trainable weights. We project a weighted feature map on different layers of the model to visualize the morphological region used by the model in classification and compared it with the morphological key used by the expert., Result: It was found that the model with architecture two and the Adam optimizer achieves at least 98% accuracy in mosquito and conditions identification and when implemented on an independent dataset, the Xception model generalizes the best result with an accuracy of 0.7775 and 0.795 precision. Moreover, most of the morphological regions used by the model are able to match those of the human expert., Conclusion: We report a customized DL model for performing pest mosquito taxonomy identification, and through visualization, some regions using computers to discriminate mosquito species could be adopted later in systematic identification. © 2022 Society of Chemical Industry., (© 2022 Society of Chemical Industry.)

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources., Competing Interests: The authors declare no conflicts of interest.

The process of linking an offender to a crime scene via their fingerprints has historically required significant human effort to compare latent fingerprints recovered from the scene with known fingerprints of a suspect. Increasing the speed of such comparisons, whilst maintaining accuracy and reliability and minimising error, is crucial for providing rapid intelligence to police investigators. One major opportunity for streamlining fingerprint examination is the adaptation of 'lights-out' technology to the comparison and matching of latent fingerprints. Here, we review the development, trial and validation process undertaken by the Queensland Police Service (QPS), Australia, to support implementation of a lights-out latent (LOL) workflow for automated latent fingerprint searching that is fully integrated with the existing case management systems. Targeted trials were undertaken using random selections of previously identified latent fingerprints that were searched using the LOL workflow against a local 10-print database. The results suggested that the LOL workflow could identify up to 44% of latent fingerprints with minimal human intervention and supported its implementation for all latent fingerprint comparisons in QPS casework. Review of LOL casework comparison outcomes for 2019 revealed that LOL-based identifications contributed approximately one quarter of all fingerprint identifications. Several procedural and technical factors that influenced the speed and efficiency of the LOL workflow are discussed, along with opportunities for improvement and future validation as an expert system., Competing Interests: Declaration of Interest Troy O’Malley and Rechelle Cook are employees of bdna, the owner of the forensic-register., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The necessity to improve the metallographic analysis systems to automate diagnostics of the condition of the metals for all their characteristics has been substantiated. The metallographic analysis algorithm based on the use of neural networks for recognizing metal microstructures and a case-based reasoning approach for determining the metal grade is proposed. The structure of a multilayer neural network to determine the metals quantitative parameters has been developed. The recognizing results by neural networks for determining the metal quantitative parameters are shown. The high accuracy of determining the metals quantitative parameters by neural networks is presented. The specialized metallographic software to automate the recognition of metal microstructures and to determine the metal grade has been developed. Comparative results of carrying out metallographic studies with the developed neural network software to determine the metals quantitative parameters are shown., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be "black box", which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hua, Cui, Liu, Shi, Guo, Zhang and Li.)

Aiming at the dilemma of expensive and difficult maintenance, lack of technical data and insufficient maintenance force for modern medical equipment, an intelligent fault diagnosis expert system of multi-parameter monitor based on fault tree was proposed in this study. Firstly, the fault tree of multi-parameter monitor was established and analyzed qualitatively and quantitatively, then based on the analysis results of fault tree, the expert system knowledge base and inference engine were constructed and the overall framework of the system was determined, finally the intelligent fault diagnosis expert system for multi-parameter monitor was developed by using the page hypertext preprocessor (PHP) language, with an accuracy rate of 80% in fault diagnosis. The results showed that technology fusion on the basis of fault tree and expert system can effectively realize intelligent fault diagnosis of multi-parameter monitors and provide troubleshooting suggestions, which can not only provide experience accumulation for fault diagnosis of multi-parameter monitors, but also provide a new idea and technical support for fault diagnosis of medical equipment.

Neurotoxicology is the study of adverse effects on the structure or function of the developing or mature adult nervous system following exposure to chemical, biological, or physical agents. The development of more informative alternative methods to assess developmental (DNT) and adult (NT) neurotoxicity induced by xenobiotics is critically needed. The use of such alternative methods including in silico approaches that predict DNT or NT from chemical structure (e.g., statistical-based and expert rule-based systems) is ideally based on a comprehensive understanding of the relevant biological mechanisms. This paper discusses known mechanisms alongside the current state of the art in DNT/NT testing. In silico approaches available today that support the assessment of neurotoxicity based on knowledge of chemical structure are reviewed, and a conceptual framework for the integration of in silico methods with experimental information is presented. Establishing this framework is essential for the development of protocols, namely standardized approaches, to ensure that assessments of NT and DNT based on chemical structures are generated in a transparent, consistent, and defendable manner., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Myatt reports grants from NIH during the conduct of the study; and FDA’s Center for Drug Evaluation and Research (CDER) and Leadscope Inc. (an Instem company) are parties to a formal Research Collaboration Agreement (RCA).

Based on expert system theory and fluid-structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design for additive manufacturing (DfAM). By changing design parameters, a series of flow channel models can be obtained. According to the design characteristics, the system can intelligently allocate suitable computational models to compute the flow field of a specific model. The pressure-based normal stress is abstracted from the results and transmitted to the solid region by the fluid-structure (FS) interface to analyze the strength of the structure. The design space is obtained by investigating the simulation results with the metamodeling method, which is further applied for pursuing design objectives under constraints. Finally, the improved design is derived by gradient-based optimization. This system can improve the accuracy of the FSI simulation and the efficiency of the optimization process. The design optimization of a flow channel in a simplified hydraulic manifold is applied as the case study to validate the feasibility of the proposed system.

In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

This article studies how different systems of policy advice are suited to provide relevant knowledge in times of acute crisis. The notion of evidence-based policymaking (EBP) originated in the successful 1997 New Labour program in the United Kingdom to formulate policy based not on ideology but on sound empirical evidence. We provide a brief overview of the history of the concept and the current debates around it. We then outline the main characteristics of the policy advisory systems in Germany, Switzerland, and Italy through which scientific knowledge-in the form of either person-bound expertise or evidence generated through standard scientific processes-was fed into policy formulation processes before the COVID-19 crisis. Whereas EBP takes place in the form of institutionalized advisory bodies and draws on expertise rather than on evidence in Germany, the system in Switzerland focuses more on the use of evidence provided through external mandates. Italy has a hybrid politicized expert system. The article then analyzes how this different prioritization of expertise vs. evidence in the three countries affects policymakers' capacity to include scientific knowledge in policy decisions in times of acute crisis. The comparison of the three countries implies that countries with policy advisory systems designed to use expertise are better placed to incorporate scientific knowledge into their decisions in times of acute crisis than are countries with policy advisory systems that relied primarily on evidence before the COVID-19 crisis., Supplementary Information: The online version of this article (10.1007/s11615-022-00382-x) contains supplementary material, which is available to authorized users., Competing Interests: Conflict of interestS. Hadorn, F. Sager, C. Mavrot, A. Malandrino, and J. Ege declare that they have no competing interests., (© The Author(s) 2022.)

Background: Artificial intelligence (AI) holds the promise of supporting nurses' clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios., Objective: This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care., Methods: Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria., Results: The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail., Conclusions: The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care-specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved., (©Kathrin Seibert, Dominik Domhoff, Dominik Bruch, Matthias Schulte-Althoff, Daniel Fürstenau, Felix Biessmann, Karin Wolf-Ostermann. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.11.2021.)

Drug-excipient compatibility study is the essential basis for excipient selection at the pre-formulation stage. According to the pharmaceutical Quality by Design (QbD) principles, a comprehensive understanding of the ingredients' physicochemical properties and a theoretical evaluation of the interaction risk between the drugs and excipients are required for conducting rational compatibility experimental design. Currently, there is an urgent need to establish an artificial intelligence system for researchers to easily get through the problem because it is very inconvenient and hard to utilize those drug-excipient incompatibility data scattered in scientific literature. Here, we designed a knowledge-driven expert system named PharmDE for drug-excipient incompatibility risk evaluation. PharmDE firstly developed an information-rich database to store incompatibility data, covering 532 data items from 228 selected articles. Then, 60 drug-excipient interaction rules were created based on our knowledge and formulation research experiences. Finally, the expert system was developed by organically integrating the database searching and rule-based incompatibility risk prediction, which resulted in four main functionalities: basic search of incompatibility database, data matching by similarity search, drug incompatibility risk evaluation, and formulation incompatibility risk evaluation. PharmDE is expected to be a useful tool for drug-excipient compatibility study and accelerate drug formulation design. It is now freely available at https://pharmde.computpharm.org., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Expert systems have a long tradition in both medical informatics and artificial intelligence research. Traditionally, such systems are created by implementing knowledge provided by experts in a system that can be queried for answers. To automatically generate such knowledge directly from data, the lightweight InteKRator toolbox will be introduced here, which combines knowledge representation and machine learning approaches. The learned knowledge is represented in the form of rules with exceptions that can be inspected and that are easily comprehensible. An inference module allows for the efficient answering of queries, while at the same time offering the possibility of providing explanations for the inference results. The learned knowledge can be revised manually or automatically with new evidence after learning.