An Expert-Knowledge-Based Graph Convolutional Network for Skeleton- Based Physical Rehabilitation Exercises Assessment.

Physical therapists play a crucial role in guiding patients through effective and safe rehabilitation processes according to medical guidelines. However, due to the therapist-patient imbalance, it is neither economical nor feasible for therapists to provide guidance to every patient during recovery sessions. Automated assessment of physical rehabilitation can help with this problem, but accurately quantifying patients' training movements and providing meaningful feedback poses a challenge. In this paper, an Expert-knowledge-based Graph Convolutional approach is proposed to automate the assessment of the quality of physical rehabilitation exercises. This approach utilizes experts' knowledge to improve the spatial feature extraction ability of the Graph Convolutional module and a Gated pooling module for feature aggregation. Additionally, a Transformer module is employed to capture long-range temporal dependencies in the movements. The attention scores and weight matrix obtained through this approach can serve as interpretability tools to help therapists understand the assessment model and assist patients in improving their exercises. The effectiveness of the proposed method is verified on the KIMORE dataset, achieving state-of-the-art performance compared to existing models. Experimental results also illustrate the interpretability of the method in both spatial and temporal dimensions.

Scoring systems, expert assessment, and identification of risk factors for the emergence of delirium in paediatric patients: Prospective cohort study.

INTRODUCTION: Emergence delirium (ED) is a transient irritative and dissociative state that arises after the cessation of anaesthesia in patients who do not respond to calming measures. There are many risk factors for ED, but the exact cause and underlying mechanism have not been determined because the definition of ED is still unclear in consensus. This study aims to determine ED incidence, identify ED risk factors and external validation of Watcha, Cravero and expert assessment to Pediatric Anesthesia Emergence Delirium (PAED) scoring system in ED prediction. MATERIALS AND METHODS: This study is a prospective cohort study on 79 paediatrics who underwent elective surgery with general anaesthesia. Parameter measures include the incidence of ED, ED risk factors, and the relationship between PAED, Watcha, Cravero score and expert assessment. The ED risk factor was analysed using univariate and multivariate analysis. The relationship between PAED, Watcha, Cravero score, and expert assessment was determined using Receiver Operating Characteristic (ROC) curve analysis. RESULTS: The incidence of ED was 22.8%. All parameters examined in this study showed p < 0.05. Watcha's scoring correlates with the PAED scoring and shows the highest discrimination ability with AUC 0.741 and p < 0.05. CONCLUSION: The incidence of ED in paediatrics is relatively high. Compared to others, Watcha score are more reliable for ED prediction. However, some demographic and perioperative factors are not the risk factor of ED.

Assessment and personalised advice for fatigue in systemic lupus erythematosus using an innovative digital tool: the Lupus Expert system for the Assessment of Fatigue (LEAF) study.

BACKGROUND: Fatigue is reported as the most prevalent symptom by patients with systemic lupus erythematosus (SLE). Fatigue management is complex due to its multifactorial nature. The aim of the study was to assess the usefulness of an innovative digital tool to manage fatigue in SLE, in a completely automated manner. METHODS: The «Lupus Expert System for Assessment of Fatigue¼ (LEAF) is free digital tool which measures the intensity and characteristics of fatigue and assesses disease activity, pain, insomnia, anxiety, depression, stress, fibromyalgia and physical activity using validated patient-reported instruments. Then, LEAF automatically provides personalised feedback and recommendations to cope with fatigue. RESULTS: Between May and November 2022, 1250 participants with SLE were included (95.2% women, median age 43yo (IQR: 34-51)). Significant fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue <34) was reported by 78.9% of patients. In univariate analysis, SLE participants with fatigue were more likely to be women (p=0.01), perceived their disease as more active (p<0.0001), had higher levels of pain (p<0.0001), anxiety (p<0.0001), depression (p<0.0001), insomnia (p<0.0001), stress (p<0.0001) and were more likely to screen for fibromyalgia (p<0.0001), compared with patients without significant fatigue. In multivariable analysis, parameters independently associated with fatigue were insomnia (p=0.0003), pain (p=0.002), fibromyalgia (p=0.008), self-reported active SLE (p=0.02) and stress (p=0.045). 93.2% of the participants found LEAF helpful and 92.3% would recommend it to another patient with SLE. CONCLUSION: Fatigue is commonly severe in SLE, and associated with insomnia, pain, fibromyalgia and active disease according to patients' perspective. Our study shows the usefulness of an automated digital tool to manage fatigue in SLE.

Medical Fuzzy-Expert System for Assessment of the Degree of Anatomical Lesion of Coronary Arteries.

BACKGROUND: Today, cardiovascular diseases cause 47% of all deaths among the European population, which is 4 million cases every year. In Ukraine, CAD accounts for 65% of the mortality rate from circulatory system diseases of the able-bodied population and is the main cause of disability. The aim of this study is to develop a medical expert system based on fuzzy sets for assessing the degree of coronary artery lesions in patients with coronary artery disease. METHODS: The method of using fuzzy sets for the implementation of an information expert system for solving the problems of medical diagnostics, in particular, when assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease, has been developed. RESULTS: The paper analyses the main areas of application of mathematical methods in medical diagnostics, and formulates the principles of diagnostics, based on fuzzy logic. The developed models and algorithms of medical diagnostics are based on the ideas and principles of artificial intelligence and knowledge engineering, the theory of experiment planning, the theory of fuzzy sets and linguistic variables. The expert system is tested on real data. Through research and comparison of the results of experts and the created medical expert system, the reliability of supporting the correct decision making of the medical expert system based on fuzzy sets for assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease with the assessment of experts was 95%, which shows the high efficiency of decision making. CONCLUSIONS: The practical value of the work lies in the possibility of using the automated expert system for the solution of the problems of medical diagnosis based on fuzzy logic for assessing the degree of anatomical lesion of the coronary arteries in patients with various forms of coronary artery disease. The proposed concept must be further validated for inter-rater consistency and reliability. Thus, it is promising to create expert medical systems based on fuzzy sets for assessing the degree of disease pathology.

Electronic Health Record-Integrated Clinical Decision Support for Clinicians Serving Populations Facing Health Care Disparities: Literature Review.

OBJECTIVES: To review current studies about designing and implementing clinician-facing clinical decision support (CDS) integrated or interoperable with an electronic health record (EHR) to improve health care for populations facing disparities. METHODS: We searched PubMed to identify studies published between January 1, 2011 and October 22, 2021 about clinician-facing CDS integrated or interoperable with an EHR. We screened abstracts and titles and extracted study data from articles using a protocol developed by team consensus. Extracted data included patient population characteristics, clinical specialty, setting, EHR, clinical problem, CDS type, reported user-centered design, implementation strategies, and outcomes. RESULTS: There were 28 studies (36 articles) included. Most studies were performed at safety net institutions (14 studies) or Indian Health Service sites (6 studies). CDS tools were implemented in primary care outpatient settings in 24 studies (86%) for screening or treatment. CDS included point-of-care alerts (93%), order facilitators (46%), workflow support (39%), relevant information display (36%), expert systems (11%), and medication dosing support (7%). Successful outcomes were reported in 19 of 26 studies that reported outcomes (73%). User-centered design was reported during CDS planning (39%), development (32%), and implementation phase (25%). Most frequent implementation strategies were education (89%) and consensus facilitation (50%). CONCLUSIONS: CDS tools may improve health equity and outcomes for patients who face disparities. The present review underscores the need for high-quality analyses of CDS-associated health outcomes, reporting of user-centered design and implementation strategies used in low-resource settings, and methods to disseminate CDS created to improve health equity.

Assessment of version 2.5 of QMAC-dRAST for rapid antimicrobial susceptibility testing with reduced sample-to-answer turnaround time and an integrated expert system.

OBJECTIVE: To assess the performance of the new rapid antimicrobial susceptibility testing (AST) QMAC-dRAST V2.5 system. METHODS: ASTs were performed using QMAC-dRAST-V2.5 and a disk diffusion method, directly from positive blood bottles with Gram-negative bacteria. Discrepancies between the results obtained using the two methods were categorized into very major errors (VME, S with dRAST vs. R with disk diffusion), major errors (ME, R vs. S, respectively), minor errors (mE, S vs. I or I vs. R, respectively), and very minor errors (Vme, I vs. S or R vs. I, respectively). For each AST, results were recorded after 4, 5, and 6h of incubation. RESULTS: From 106 bacteremia, 1416 individual AST results were obtained. Overall agreement between results using the two methods was 91%, ranging from 76.9% to 99.1% depending upon the antibiotic, with 128 errors, i.e. 14/1416 (1%) VME, 59/1416 (4.2%) ME, 25/1416 (1.8%) mE and 30/1416 (2.1%) Vme. VMEs were encountered for Klebsiellasp and Serratia marcescens isolates with low-level piperacillin and amikacin resistance, respectively. Using the integrated QMAC-dRAST-V2.5 expert system, all 14 VMEs and 3 mEs were eliminated, leading to 92.2% categorical agreement. After 45min of pre-incubation in the QMAC-dRAST-V2.5 device, 22.2% of the 1416 AST results were obtained after 4h, an additional 31.4% after 5h and a further 46.3% after 6h. CONCLUSION: QMAC-dRAST-V2.5 is an optimized version of QMAC-dRAST V2.0, particularly with respect to utilization of an expert system and reduced TAT according to the antibiotic tested.

A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis.

Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. However, the following are among the most used: the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), of the American Psychiatric Association; the Revised Autistic Diagnostic Observation Schedule (ADOS-R); the Autistic Diagnostic Interview (ADI); and the International Classification of Diseases, 10th edition (ICD-10), published by the World Health Organization (WHO) and adopted in Brazil by the Unified Health System (SUS). The application of machine learning models helps make the diagnostic process of Autism Spectrum Disorder more precise, reducing, in many cases, the number of criteria necessary for evaluation, denoting a form of attribute engineering (feature engineering) efficiency. This work proposes a hybrid approach based on machine learning algorithms' composition to discover knowledge and concepts associated with the multicriteria method of decision support based on Verbal Decision Analysis to refine the results. Therefore, the study has the general objective of evaluating how the mentioned hybrid methodology proposal can make the protocol derived from ICD-10 more efficient, providing agility to diagnosing Autism Spectrum Disorder by observing a minor symptom. The study database covers thousands of cases of people who, once diagnosed, obtained government assistance in Brazil.

Your Path to Transplant: A randomized controlled trial of a tailored expert system intervention to increase knowledge, attitudes, and pursuit of kidney transplant.

Individually tailoring education over time may help more patients, especially racial/ethnic minorities, get waitlisted and pursue deceased and living donor kidney transplant (DDKT and LDKT, respectively). We enrolled 802 patients pursuing transplant evaluation at the University of California, Los Angeles Transplant Program into a randomized education trial. We compared the effectiveness of Your Path to Transplant (YPT), an individually tailored coaching and education program delivered at 4 time points, with standard of care (SOC) education on improving readiness to pursue DDKT and LDKT, transplant knowledge, taking 15 small transplant-related actions, and pursuing transplant (waitlisting or LDKT rates) over 8 months. Survey outcomes were collected prior to evaluation and at 4 and 8 months. Time to waitlisting or LDKT was assessed with at least 18 months of follow-up. At 8 months, compared to SOC, the YPT group demonstrated increased LDKT readiness (47% vs 33%, P = .003) and transplant knowledge (effect size [ES] = 0.41, P < .001). Transplant pursuit was higher in the YPT group (hazard ratio: 1.44, 95% confidence interval: 1.15-1.79, P = .002). A focused, coordinated education effort can improve transplant-seeking behaviors and waitlisting rates. ClinicalTrials.gov registration: NCT02181114.

A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer's Disease Through Expert Curation of Key Protein Targets.

BACKGROUND: The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality. OBJECTIVE: To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer's disease research. METHODS: We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community. RESULTS: Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset. CONCLUSION: This represents a significant enhancement in the expert curated data pertinent to Alzheimer's disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

Formulation of Direct Compression Zidovudine Tablets to Correlate the SeDeM Diagram Expert System and the Rotary Press Simulator Styl'ONE Results.

The SeDeM diagram expert system has been applied to study Zidovudine and some excipients. From the obtained diagrams, a pharmaceutical formula has been designed. SeDeM diagram ascertains the critical parameters that are suitable for a direct compression. The formula is compressed using a rotary tablet press simulator which emulates rotary tablet press' compression profiles. From these compressions, we study the formula behavior under different industrial production conditions but saving a huge amount of material. The study is done at different compression forces and compression speeds and taking into account the influence of the pre-compression force. The differences observed between the compression profiles are hereby described. The results indicate that the formulation is able to be compressed adequately with the emulated compression profiles and no differences are observed between the final products. Therefore, we can assure that the SeDeM diagram expert system is accurate and robust. Moreover, its results are comparable with the compression results in a rotary tablet press, which has never been described in the pharmaceutical literature before. From the obtained results, it is possible to select the best rotary press to scale-up this formulation.

Demand Analysis and Function Design of Health Decision Support System in China.

In order to improve the level of health decision-making, based on health information resources and decision support function types, this study summarized five core functions of health decision support system: information support, monitoring and early warning, analysis and evaluation, trend prediction, comprehensive optimization; And from the perspective of business functions, business processes and business activities of business domains, the demand of Health Decision Support System is refined in to six parts, such as public health, medical care, drug management, medical insurance, comprehensive management, grass-roots health. On this basis, the overall design of the system is carried out.

Readmission prediction using deep learning on electronic health records.

Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We evaluate the contribution of each element towards prediction performance (ROC-AUC, F1-measure) and cost-savings. We show that the model with all key elements achieves higher discrimination ability (AUC: 0.77; F1: 0.51; Cost: 22% of maximum possible savings) outperforming the reduced models in at least two evaluation metrics. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients.

A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.

PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the application of the BI-RADS lexicon, the decision to biopsy varies greatly and results in overdiagnosis and excessive biopsies. The false-positive rate from mammograms is estimated to be 7% to approximately 10% overall, but within the BI-RADS 4 category, it is greater than 70%. Therefore, we developed the Breast Cancer Risk Calculator (BRISK) to target a well-characterized and specific patient subgroup (BI-RADS 4) rather than a broad heterogeneous group in assessing breast cancer risk. METHODS: BRISK provides a novel precise risk assessment model to reduce overdiagnosis and unnecessary biopsies. It was developed by applying natural language processing and deep learning methods on 5,147 patient records archived in the Houston Methodist systemwide data warehouse from 2006 to May 2015, including imaging and pathology reports, mammographic images, and patient demographics. Key characteristics for BI-RADS 4 patients were collected and computed to output an index measure for biopsy recommendation that is clinically relevant and informative and improves upon the traditional BI-RADS 4 scores. RESULTS: For the validation set, we assessed data from 1,247 BI-RADS 4 patients, including mammographic images and medical reports. The BRISK model sensitivity to predict malignancy was 100%, whereas the specificity was 74%. The total accuracy of our implemented model in BRISK was 81%. Overall area under the curve was 0.93. CONCLUSION: BRISK for abnormal mammogram uses integrative artificial intelligence technology and has demonstrated high sensitivity in the prediction of malignancy. Prospective evaluation is under way and can lead to improvement in patient-physician engagement in making informed decisions with regard to biopsy.

Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES)-An Approach To Use Artificial Intelligence in Determining Food Odor Codes.

Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory system, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-based expert system (SEBES) that is able to reliably predict the key aroma compounds of a given food in a limited number of runs without using the human olfactory system. First, a successful method for the quantitation of nearly 100 (out of the 226 known KFOs) components was developed in combination with a software allowing the direct use of the identification and quantitation data for the calculation of odor activity values (OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples, the quantitative results obtained by the new SEBES method were compared to data obtained by applying an aroma extract dilution analysis and stable isotope dilution assays required in the classical Sensomics approach. A good agreement of the results was found with differences below 20% for most of the compounds considered. By implementing the GC × GC data analysis software with the in-house odor threshold database, odor activity values (ratio of concentration to odor threshold) were directly displayed in the software pane. The OAVs calculated by the software were in very good agreement with data manually calculated on the basis of the data obtained by SIDA. Thus, it was successfully shown that it is possible to characterize key food odorants with one single analytical platform and without using the human olfactory system, that is, by "artificial intelligence smelling".

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain).

Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.

Computational assessment of long-term memory structures from SDA-M related to action sequences.

Assistance systems should be able to adapt to individual task-related skills and knowledge. Structural-dimensional analysis of mental representations (SDA-M) is an established method for retrieving human memory structures related to specific activities. For this purpose, SDA-M involves a semi-automatized survey of users (the "split procedure"), which yields data about users' associations between action representations in long-term memory. Up to now this data about associations has commonly been clustered and visualized by SDA-M software in the form of dendrograms that can be used by human experts as a tool to (manually) assess users' individual expertise and identify potential issues with respect to predefined action sequences. This article presents new algorithmic approaches for automatizing the process of assessing task-related memory structures based on SDA-M data to predict probable errors in action sequences. This automation enables direct integration into technical systems, e.g. user-adaptive assistance systems. An evaluation study has compared the automatized computational assessments to predictions made by human scholars based on visualizations of SDA-M data. The different algorithms' outputs matched human experts' manual assessments in 84% to 86% of the test cases.

Integral Homeostasis Index in a Decision Support System for the Assessment and Prediction of the Clinical Condition of Children with Severe Mechanical Trauma.

OBJECTIVE: Daily assessment of the acid-base balance (ABB) in blood is one of the important elements of multi-parameter patient monitoring at intensive care units (ICUs). The present work aims to determine the effectiveness and validity of the integral homeostasis index IHx calculated from ABB blood test data for the assessment and prognosis of children with critical traumatic conditions. METHODS: 345 patients were studied. IHx was calculated and the data were subjected to statistical evaluation. An Arden-Syntax-based clinical decision support (CDS) platform was used. One purpose of the study was to incorporate the platform into the ICU IT landscape of the hospital, and the second purpose was to develop a CDS module for the calculation of IHx and present the results in real time to the attending physician. RESULTS: Integral homeostasis index IHx calculations as well as their prompt assessment permit better and more rapid treatment of children with severe traumatic injury.

FESAEI: a fuzzy rule-based expert system for the assessment of environmental impacts : A fuzzy logic approach to impact assessment.

Currently, the method mostly used by practitioners of environmental impact assessment (EIA) is the "crisp numbers" method. Nevertheless, this arithmetic method is far away of giving correct values due to its rigidity and the lack of consideration of important aspects as the imprecision and incompleteness of data and the uncertainty that usually pervade our knowledge of environment. A more flexible model that considers uncertainty of knowledge and imprecision of data is necessary. Among the different approaches for the assessment of environmental impacts, the fuzzy logic-based one takes account of the aspects said before; this was our primal assumption. On this paper, we explain the structure and performance of the fuzzy rule-based inference model we built, how it works, and what can be obtained when used to assess environmental impacts. Our fuzzy expert system for the assessment of environmental impacts (FESAEI) is built as the combination of five subsystems, using a total of 120 fuzzy rules, and being the output and input for the next subsystem. We assessed the parameters of rarity, robustness, quality, recoverability, intrinsic value, extension, intensity, persistence, impact_character, cumulativeness, transmissivity, and impact prevalue in four subsystems. The fifth subsystem gives the definitive impact value corresponding to the impact type of "compatible," "moderate," "severe," and "critical." The model is verified and statistically validated. Weighted Cohen's kappa shows an almost perfect concordance among experts and FESAEI's evaluations.

A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: highlighting the predictive power of lactose and electrical conductivity.

Sub-clinical mastitis (SCM) affects milk composition. In this study, we hypothesise that large-scale mining of milk composition features by pattern recognition models can identify the best predictors of SCM within the milk composition features. To this end, using data mining algorithms, we conducted a large-scale and longitudinal study to evaluate the ability of various milk production parameters as indicators of SCM. SCM is the most prevalent disease of dairy cattle, causing substantial economic loss for the dairy industry. Developing new techniques to diagnose SCM in its early stages improves herd health and is of great importance. Test-day Somatic Cell Count (SCC) is the most common indicator of SCM and the primary mastitis surveillance approach worldwide. However, test-day SCC fluctuates widely between days, causing major concerns for its reliability. Consequently, there would be great benefit to identifying additional efficient indicators from large-scale and longitudinal studies. With this intent, data was collected at every milking (twice per day) for a period of 2 months from a single farm using in-line electronic equipment (346 248 records in total). The following data were analysed: milk volume, protein concentration, lactose concentration, electrical conductivity (EC), milking time and peak flow. Three SCC cut-offs were used to estimate the prevalence of SCM: Australian ≥ 250 000 cells/ml, European ≥200 000 cells/ml and New Zealand ≥ 150 000 cells/ml. At first, 10 different Attribute Weighting Algorithms (AWM) were applied to the data. In the absence of SCC, lactose concentration featured as the most important variable, followed by EC. For the first time, using attribute weighted modelling, we showed that the concentration of lactose in milk can be used as a strong indicator of SCM. The development of machine-learning expert systems using two or more milk variables (such as lactose concentration and EC) may produce a predictive pattern for early SCM detection.

Cryptanalysis and improvement of a biometrics-based authentication and key agreement scheme for multi-server environments.

According to advancements in the wireless technologies, study of biometrics-based multi-server authenticated key agreement schemes has acquired a lot of momentum. Recently, Wang et al. presented a three-factor authentication protocol with key agreement and claimed that their scheme was resistant to several prominent attacks. Unfortunately, this paper indicates that their protocol is still vulnerable to the user impersonation attack, privileged insider attack and server spoofing attack. Furthermore, their protocol cannot provide the perfect forward secrecy. As a remedy of these aforementioned problems, we propose a biometrics-based authentication and key agreement scheme for multi-server environments. Compared with various related schemes, our protocol achieves the stronger security and provides more functionality properties. Besides, the proposed protocol shows the satisfactory performances in respect of storage requirement, communication overhead and computational cost. Thus, our protocol is suitable for expert systems and other multi-server architectures. Consequently, the proposed protocol is more appropriate in the distributed networks.