Prediction Rule Research Articles

Reducing prescribing of antibiotics for acute respiratory infections using a frontline nurse-led EHR-Integrated clinical decision support tool: protocol for a stepped wedge randomized control trial

BackgroundOverprescribing of antibiotics for acute respiratory infections (ARIs) remains a major issue in outpatient settings. Use of clinical prediction rules (CPRs) can reduce inappropriate antibiotic prescribing but they remain underutilized by physicians and advanced practice providers. A registered nurse (RN)-led model of an electronic health record-integrated CPR (iCPR) for low-acuity ARIs may be an effective alternative to address the barriers to a physician-driven model.MethodsFollowing qualitative usability testing, we will conduct a stepped-wedge practice-level cluster randomized controlled trial (RCT) examining the effect of iCPR-guided RN care for low acuity patients with ARI. The primary hypothesis to be tested is: Implementation of RN-led iCPR tools will reduce antibiotic prescribing across diverse primary care settings. Specifically, this study aims to: (1) determine the impact of iCPRs on rapid strep test and chest x-ray ordering and antibiotic prescribing rates when used by RNs; (2) examine resource use patterns and cost-effectiveness of RN visits across diverse clinical settings; (3) determine the impact of iCPR-guided care on patient satisfaction; and (4) ascertain the effect of the intervention on RN and physician burnout.DiscussionThis study represents an innovative approach to using an iCPR model led by RNs and specifically designed to address inappropriate antibiotic prescribing. This study has the potential to provide guidance on the effectiveness of delegating care of low-acuity patients with ARIs to RNs to increase use of iCPRs and reduce antibiotic overprescribing for ARIs in outpatient settings.Trial registrationClinicalTrials.gov Identifier: NCT04255303, Registered February 5 2020, https://clinicaltrials.gov/ct2/show/NCT04255303.

Enriching Recommendation Models with Logic Conditions

This paper proposes RecLogic, a framework for improving the accuracy of machine learning (ML) models for recommendation. It aims to enhance existing ML models with logic conditions to reduce false positives and false negatives, without training a new model. Underlying RecLogic are (a) a class of prediction rules on graphs, denoted by TIEs, (b) a new approach to learning TIEs, and (c) a new paradigm for recommendation with TIEs. TIEs may embed ML recommendation models as predicates; as opposed to prior graph rules, it is tractable to decide whether a graph satisfies a set of TIEs. To enrich ML models, RecLogic iteratively trains a generator with feedback from each round, to learn TIEs with a probabilistic bound. RecLogic also provides a PTIME parallel algorithm for making recommendations with the learned TIEs. Using real-life data, we empirically verify that RecLogic improves the accuracy of ML predictions by 22.89% on average in an area where the prediction strength is neither sufficiently large nor sufficiently small, up to 33.10%.

Guidelines for neuroprognostication in adults with traumatic spinal cord injury

BackgroundTraumatic spinal cord injury (tSCI) impacts patients and their families acutely and often for the long term. The ability of clinicians to share prognostic information about mortality and functional outcomes allows patients and their surrogates to engage in decision-making and plan for the future. These guidelines provide recommendations on the reliability of acute-phase clinical predictors to inform neuroprognostication and guide clinicians in counseling adult patients with tSCI or their surrogates.MethodsA narrative systematic review was completed using Grading of Recommendations Assessment, Development, and Evaluation methodology. Candidate predictors, including clinical variables and prediction models, were selected based on clinical relevance and presence of an appropriate body of evidence. The Population/Intervention/Comparator/Outcome/Timing/Setting question was framed as “When counseling patients or surrogates of critically ill patients with traumatic spinal cord injury, should < predictor, with time of assessment if appropriate > be considered a reliable predictor of < outcome, with time frame of assessment >?” Additional full-text screening criteria were used to exclude small and lower quality studies. Following construction of an evidence profile and summary of findings, recommendations were based on four Grading of Recommendations Assessment, Development, and Evaluation criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. Good practice recommendations addressed essential principles of neuroprognostication that could not be framed in the Population/Intervention/Comparator/Outcome/Timing/Setting format. Throughout the guideline development process, an individual living with tSCI provided perspective on patient-centered priorities.ResultsSix candidate clinical variables and one prediction model were selected. Out of 11,132 articles screened, 369 met inclusion criteria for full-text review and 35 articles met eligibility criteria to guide recommendations. We recommend pathologic findings on magnetic resonance imaging, neurological level of injury, and severity of injury as moderately reliable predictors of American Spinal Cord Injury Impairment Scale improvement and the Dutch Clinical Prediction Rule as a moderately reliable prediction model of independent ambulation at 1 year after injury. No other reliable or moderately reliable predictors of mortality or functional outcome were identified. Good practice recommendations include considering the complete clinical condition as opposed to a single variable and communicating the challenges of likely functional deficits as well as potential for improvement and for long-term quality of life with SCI-related deficits to patients and surrogates.ConclusionsThese guidelines provide recommendations about the reliability of acute-phase predictors of mortality, functional outcome, American Spinal Injury Association Impairment Scale grade conversion, and recovery of independent ambulation for consideration when counseling patients with tSCI or their surrogates and suggest broad principles of neuroprognostication in this context.

Exploration of Talent Cultivation Path for Art and Design Majors under Industry-Teaching Integration Mode Based on Big Data Analysis

Abstract This paper combines the three elements of industry-teaching fusion with the competency characteristics of art design professionals and puts forward a specific cultivation path for art design professionals under the industry-teaching fusion mode with respect to the cultivation value of industry-teaching fusion for art design professionals. The association rule algorithm is selected to set up art design professional courses, and the neural network algorithm is selected to establish the talent cultivation mechanism model of industry-teaching fusion under the background of big data. According to the association rules for talent training path effect prediction, and then use big data is used to analyze the demand characteristics of art design professional talent training under the fusion of industry and education based on the demand characteristics for targeted talent training. The average difference between the student’s scores and the actual scores is 17.38, the accuracy rate of the model is 87.15% in the case of the allowable error range of 10 points, and the accuracy rate of the model is 90.55% in the case of the allowable error range of 15 points. A series of interventions, such as promotion rate prediction and academic warning, can be implemented based on this.

Beliefs and practice patterns of spinal thrust manipulation for mechanical low back pain of physical therapists in the state of Minnesota

ABSTRACT Introduction The primary purpose of this study was to examine the perceptions and utilization of spinal thrust manipulation (STM) techniques of physical therapists who treat patients with low back pain (LBP) in the State of Minnesota. A secondary purpose was to investigate differences between physical therapists who perform STM and those who do not. Methods A cross-sectional design was utilized through the completion of an electronic survey. 74 respondents completed the survey. Descriptive measures were recorded as frequencies for categorical data or mean ± standard deviation for continuous data. For between-group comparisons, chi-square analyses were used for categorical items of nominal or ordinal data and t-tests were utilized for continuous data. The alpha level was set at p < 0.05. Result 60.2% of respondents reported using STM when treating patients with LBP. 69.9% of respondents utilize a classification system. 76.7% of individuals answered correctly regarding the Minnesota State practice act. Of those who use STM, 81.8% utilize a Clinical Prediction Rule. Respondents who use STM were more likely to have a specialist certification (chi-square = 6.471, p = 0.011) and to have completed continuing education courses on manual therapy (chi-square = 4.736, p = 0.030). Discussion/Conclusions Physical therapists who perform STM are more likely to have a better understanding of their state practice act, be board certified, and have completed continuing education in manual therapy.

Development of a machine learning model using 12-lead ECG to improve acute dianosis of pulmonary embolism

Abstract Introduction Pulmonary embolism (PE) is a life-threatening condition. Given the lack of specificity in symptoms and clinical decision rules, diagnostic uncertainty in PE remains high and in most of the cases requires confirmation by computed tomography pulmonary angiogram (CTPA). This could be critical to decide fibrinolysis indication in hemodynamic unstable patients (pts) with PE suspicion in out-of-hospital setting or if CTPA is not immediately available. The implementation of artificial intelligence (AI) in medical diagnosis has attracted major attention last years. Electrocardiography (ECG) signals and patterns can be detected by AI networks with precision. The purpose of this study was to develop an AI model for predicting PE using 12-lead ECG. Methods We extracted 1014 ECGs of pts admitted to emergency department who underwent CTPA due to PE suspicion: 911 ECGs were used for development of the AI model (derivation cohort) and 103 ECGs were used for testing the PE prediction model (validation cohort). An AI algorithm based on an ensemble neural network was developed using 12-lead ECG signal. The first endpoint was the diagnosis of PE. To evaluate the performance of the AI model, we compare the performance of AI model against the recommend clinical prediction rules for PE based on clinical probability and D-dimer measurement (Wells and Geneva criteria combined with fixed and age-adjusted D-dimer cut-off, YEARS and PEGeD algorithms). Results On validation cohort, AI model achieves greater specificity to detect PE than the commonly used clinical prediction rules (p&amp;lt;0.001) -Table 1. The AI model shown a specificity of 100%, which is particularly relevant in the context of fibrinolysis decision. Although the sensitivity of the AI model is lower (p=0.001), the biggest gain of this model is to provide security to the physician to stablish a definite diagnosis. Globally, the AI model performed significantly better than all the other models (AUC 0.75, p&amp;lt;0.001), which had nearly no discriminative power. The incidence of typical PE ECG features (S1Q3T3, right bundle branch block and V1-V3 T wave inversion) was similar in pts with and without PE, meaning that AI model provide information beyond these findings and can improve PE prediction. ECGs were included regardless of cardiac rhythm (including pacing), so these findings can be generalized to all pts. Model performance is similar across gender. Conclusion In this study we developed and validated a deep learning-based AI algorithm for PE detection using a 12-lead ECG which demonstrated high performance.

The framing of time-dependent machine learning models improves risk estimation among young individuals with acute coronary syndromes

Abstract Introduction Acute coronary syndrome (ACS) is a common cause of death in individuals older than 55 years. Although younger individuals are less frequently seen with ACS, this clinical event shows high recurrence rates and triggers considerable economic burden. Young individuals with ACS (yACS) are usually underrepresented and show idiosyncratic epidemiologic features compared to older subjects. These differences may justify why available risk prediction models usually penalize yACS with higher false positive rates compared to older subjects. We hypothesized that exploring temporal framing structures such as prediction time, observation windows and subgroup-specific prediction, could improve time-dependent prediction metrics. More specifically, we evaluated potential improvements in risk prediction quality among yACS by (i) developing models specifically in yACS versus a global cohort; (ii) splitting predictive rules into short- and long-term prediction windows [STWm and LTWm] versus a global follow-up model (GFm). Methods We included 6341 consecutive subjects with ACS (2242 with yACS) admitted into public hospitals in a large city in Brazil who undergone coronarography up to 48h after hospital admission from 2011 to 2020. The observation window in STWm and GFm included the first 48h upon hospital admission, and LTWm included all in-hospital information. yACS cohort was divided into train/validation-set (70%, n=1569) and test-set (30%, n=673); global cohort was divided in training/validation-set (70%, n=4439) and test-set including the 673 yACS subjects. Models were repeated over five cross-validation folds and then assessed in the test-set. C-statistics was the evaluation metric for STWm and time-dependent concordance (Ctd-index) for LTWm. STWm evaluated the occurrence of in-hospital cardiovascular deaths and recurrent ACS (MACE) and LTWm estimated events occurring post-discharge from index ACS hospitalization considering time-to-event with competing risks (MACE versus non-cardiovascular deaths). Results Among yACS and older subjects, respectively, in-hospital MACE occurred in 180 and 493 individuals, post-discharge MACE in 454 and 881; and post-discharge non-cardiovascular death in 47 and 285 after 6.67 years (95%CI 5.59-7.24) of follow-up. The best strategy was to design models specifically in yACS individuals combining STWm and LTWm. Among yACS subjects, STWm and LTWm yielded a C-statistics [0.921 (95%CI 0.889-0.953)] and Ctd-index [0.722 (95%CI 0.678-0.760)], respectively, while the best Ctd-index in GFm was 0.681 (95%CI 0.654-0.703). There was very low concordance among top predictors of MACE for prACS versus global cohort, as well as for STWm versus LTWm. Conclusions Predictive accuracy for adverse clinical events was optimized by using specific rules for yACS and splitting short-term and long-term prediction windows, leading to the detection of 80% of events, compared to 69% by using a rule designed for the global cohort.

Prehospital characteristics that identify major trauma patients: A hybrid systematic review protocol

Background International evidence has demonstrated significant improvements both in the trauma care process and outcomes for patients through re-configuring care services from that which is fragmented to integrated trauma networks. A backbone of any trauma network is a trauma triage tool. This is necessary to support paramedic staff in identifying major trauma patients based on prehospital characteristics. However, there is no consensus on an optimal triage tool and with that, no consensus on the minimum criteria for prehospital identification of major trauma. Objective Examine the prehospital characteristics applied in the international literature to identify major trauma patients. Methods To ensure the systematic review is both as comprehensive and complete as possible, we will apply a hybrid overview of reviews approach in accordance with best practice guidelines. Searches will be conducted in Pubmed (Ovid MEDLINE), Embase, Cochrane Library of Systematic Reviews and Cochrane Central Register of Clinical Trials. We will search for papers that analyse prehospital characteristics applied in trauma triage tools that identify major trauma patients. These papers will be all systematic reviews in the area, not limited by year of publication, supplemented with an updated search of original papers from November 2019. Duplication screening of all articles will be conducted by two reviewers and a third reviewer to arbitrate disputes. Data will be extracted using a pre-defined data extraction form, and quality appraised by the Newcastle Ottawa Quality Assessment form. Conclusions An exhaustive search for both systematic reviews and original papers will identify the range of tools developed in the international literature and, importantly, the prehospital characteristics that have been applied to identify major trauma patients. The findings of this review will inform the development of a national clinical prediction rule for triage of major trauma patients.

Retracted: An Approach to Acquire the Constraints Using Panel Big Data Hybrid Association Rule and Discretization Process for Breast Cancer Prediction.

[This retracts the article DOI: 10.1155/2021/3870147.].

4 Extending the 5P Clinical Decision Rule Predicting Concussion Recovery Using an Evidence-Based Assessment Model

Objective:Construction of predictive algorithms of concussion symptom recovery at 4 and 12 weeks post-injury using an evidence-based assessment (EBA) model to guide clinical decision-making, extending the 2016 5P decision rule.Participants and Methods:Children and adolescents, ages 8-18 (n=1,551; mean age=12.78; 62% male), followed over 12 weeks in the prospective multicenter cohort study (Predicting Persistent Post-Concussive Problems in Pediatrics, 5P; Zemek et al., 2016). The age-specific PostConcussion Symptom Inventory (PCSI) (8-12, 17 items; 1318 years, 20 items) was completed at six timepoints from the ED and at 1, 2, 4, 8, and 12-weeks post-injury. Logistic regression analysis was applied to the set of key variables including the PCSI Total Retrospective-Adjusted PostInjury Difference (RAPID) scores, patient demographics and pre-injury history, and injury characteristics to predict participant recovery status (Recovered, Not Recovered) at the 4- and 12-week endpoints. The resulting recovery-predictive equations identified the significant sets of variables with symptom scores at four successive post-injury timepoints (ED, 1, 2, 4 weeks). Logistic Regression Threshold values were established at the 90th CI against which individual patient data was applied to determine recovery status. Participants with sub-threshold sums were deemed recovered at the target endpoint (4- or 12-weeks post-injury).Results:A total of 19 predictive equations were generated for the two age groups across the recovery timeline. Four sets of equations were developed to predict symptom recovery status at 4-weeks post-injury for the two age groups (8-12 AUC=0.679-0.884; 13-18 AUC=0.752-0.909). Prediction of symptom recovery status at 12-weeks post-injury yielded six equations for the 8-12 age group (AUC=0.723-0.825), and five equations for the 13-18 age group (AUC=0.724-0.887). Total PCSI RAPID score was identified as a significant variable in each of these 19 equations. Participant sex was identified as significant in 18 of the 19 constructed equations. Other variables that were identified as significant at varying timepoints included age, pre-injury history of learning disability and migraines, and an early post-injury sign in the ED (answering questions more slowly than usual). Examples of the equations include: Week 1 predicting symptom recovery status at 4-weeks: 8-12 yr group-(Sex*.802)+(week 1 Total RAPID Score*.142)+(Age2* .053)+(-3.851) with AUC=0.808; 13-18 yr group-(Sex*.980)+(Week 1 Total RAPID Score*.071)+(-3.261) with AUC=0.861.Conclusions:Clinicians’ management of the concussion recovery of children and adolescents can benefit from EBA guidance. The 5P dataset (Zemek et al., 2016) provides an important window into “typical” and “atypical” recovery trajectories, establishing an initial predictive decision rule for a 4-week recovery endpoint, at the ED timepoint only, reporting AUC=0.69. The current study extends the prediction modeling using successive post-injury timepoints reflecting a typical management timeline. Symptom reports from both 1- and 2-weeks post injury with patient demographics/ history predicted symptom recovery status at 4- and 12-weeks post-injury, significantly improve predictive accuracy over the ED timepoint alone. These predictive equations, when applied to the individual patient, can serve to assist the clinician’s understanding of the patients’ recovery trajectory, i.e., on track for a typical or atypical recovery, further informing the intervention strategy.

Defining Replicability of Prediction Rules

Defining Replicability of Prediction Rules

Development of Fibro-PeN, a clinical prediction model for moderate-to-severe fibrosis in children with nonalcoholic fatty liver disease.

Liver fibrosis is common in children with NAFLD and is an important determinant of outcomes. High-performing noninvasive models to assess fibrosis in children are needed. The objectives of this study were to evaluate the performance of existing pediatric and adult fibrosis prediction models and to develop a clinical prediction rule for identifying moderate-to-severe fibrosis in children with NAFLD. We enrolled children with biopsy-proven NAFLD in the Nonalcoholic Steatohepatitis Clinical Research Network within 90 days of liver biopsy. We staged liver fibrosis in consensus using the Nonalcoholic Steatohepatitis Clinical Research Network scoring system. We evaluated existing pediatric and adult models for fibrosis and developed a new pediatric model using the least absolute shrinkage and selection operator with linear and spline terms for discriminating moderate-to-severe fibrosis from none or mild fibrosis. The model was internally validated with 10-fold cross-validation. We evaluated 1055 children with NAFLD, of whom 26% had moderate-to-severe fibrosis. Existing models performed poorly in classifying fibrosis in children, with area under the receiver operator curves (AUC) ranging from 0.57 to 0.64. In contrast, our new model, fibrosis in pediatric NAFLD was derived from fourteen common clinical variables and had an AUC of 0.79 (95% CI: 0.77-0.81) with 72% sensitivity and 76% specificity for identifying moderate-to-severe fibrosis. Existing fibrosis prediction models have limited clinical utility in children with NAFLD. Fibrosis in pediatric NAFLD offers improved performance characteristics for risk stratification by identifying moderate-to-severe fibrosis in children with NAFLD.

A study on predictive analysis and rules for international financial risk considering debt crisis

Abstract This paper first establishes a system of international financial risk early warning indicators, constructs a VAR forecasting model for international financial risk, and uses the ADF test to identify the best amount of lags for the model before doing a smoothness test on the variables. The lagged values are used as independent variables to establish equations to describe the dynamic relationship between multiple variables. Then the model is tested for cointegration and Grange causality. The VAR model is then used to estimate the financial risk profile from the standpoint of a global economy and to examine the extent to which the debt crisis has affected international financial risk. The results show that at the 5% significant level, national debt expansion is the cause of asset price volatility, macroeconomic volatility, and debt risk. The forecasted inflation rate for 2023-2025 is 4.18, output gap is 4.94, and the policy interest rate is 4.18 for emerging Asian countries and regions.

Derivation of clinical predictive factors (CHIEF) for first recurrent Clostridioides difficile infection

Current models for predicting Clostridioides difficile infection (CDI) recurrence rates have a limited capacity to account for important risk factors. This study developed a clinical prediction rule for CDI recurrence. This retrospective cohort study evaluated 209 patients with CDI at a university hospital in Japan. Logistic regression and receiver operating characteristic curve analyses were performed to identify potential predictors (age, sex, underlying diseases, antibiotic use, acid suppressants, immunosuppressants, CDI history) of CDI recurrence. Forty-five patients developed recurrent CDI. Univariate analyses identified several significant recurrence predictors (enteral feeding, inflammatory bowel diseases [IBD], community-onset CDI, severe CDI). Enteral feeding (odds ratio: 3.87, 95% confidence interval: 1.75-8.56) and IBD (odds ratio: 7.08, 95% confidence interval: 1.28-39.06) were significant factors in the multivariate analysis. The CHIEF predictive scoring system was developed using 5 relevant variables (carbapenem use, hematologic malignancy, IBD, enteral feeding, fluoroquinolone use); the area under the receiver operating characteristic curve for the CHIEF score was 0.70. The CHIEF score incorporates useful, clinically available factors and could help identify patients at risk of recurrent CDI. These findings contribute to the understanding of risk factors associated with CDI recurrence and provide support for the development of prevention strategies.

Computed Tomography Overuse in Pediatric Minor Head Trauma: Insights from a Single-Center Experience.

Minor head trauma is a common reason for emergency department visits in children, but many of these cases are not clinically significant. Despite established criteria for selecting patients who require computed tomography (CT), concerns about overuse of CT persist. This study aimed to determine the frequency of clinically important traumatic brain injury by retrospectively evaluating cranial CT scans in children categorized as very low risk for such injuries based on PECARN prediction rules. Cranial CT scans of 941 minor head trauma cases were assessed for the presence, type, and number of calvarial bone fractures. Concomitant bleeding and treatment approaches were also recorded. Among 881 patients (93.6%), cranial CT scans did not reveal any lesions apart from soft tissue edema. None of the cases had clinically important traumatic brain injury or required neurosurgical intervention. The study demonstrated that 93.6% of cranial CT scans for pediatric minor head trauma were negative, indicating a concerningly high rate of CT overuse. Although prediction rules exist, their application in clinical practice is not always optimal. Given the principle of "first, do no harm," proper patient selection is crucial to avoid unnecessary exposure to ionizing radiation.

Predictive Rule for Mortality of Inpatients With Escherichia coli Bacteremia: Chi-Square Automatic Interaction Detector Decision Tree Analysis Model.

A predictive rule for risk factors for mortality due to Escherichia coli (E. coli)bacteremia has not been defined, especially using the chi-squareautomatic interaction detector (CHAID) decision tree analysis. Here we aimed to create the predictive rule for risk factors for in-hospital mortality due to E. coli bacteremia. The outcome of this retrospective cross-sectional survey was death in the hospital due to E. coli bacteremia. Factors potentially predictive of death in the hospital due to E. coli bacteremia were analyzed using the CHAID decision tree analysis. A total of 420 patients (male:female=196:224; mean±standard deviation [SD] age, 75.81±13.13 years) were included in this study. 56 patients (13.3%) died in the hospital. The CHAID decision tree analysis revealed that patients with total protein level ≤5.10 g/dL (incidence, 46.2%), total protein level ≤5.90 g/dL with disturbance of consciousness (incidence, 39.4%), and total protein level >5.90 g/dL with hemoglobin level ≤11.10 g/dL and lactate dehydrogenase level ≥312.0 IU/L (incidence, 42.3%) were included in the high-risk group. Appropriate preventative therapy should be facilitated in patients with E. coliat a high risk of mortality.

Diagnostic clinical prediction rules for categorising low back pain: A systematic review.

Low back pain (LBP) is a common complex condition, where specific diagnoses are hard to identify. Diagnostic clinical prediction rules (CPRs) are known to improve clinical decision-making. A review of LBP diagnostic-CPRs by Haskins etal. (2015) identified six diagnostic-CPRs in derivation phases of development, with one tool ready for implementation. Recent progress on these tools is unknown. Therefore, this review aimed to investigate developments in LBP diagnostic-CPRs and evaluate their readiness for implementation. A systematic review was performed on five databases (Medline, Amed, Cochrane Library, PsycInfo, and CINAHL) combined with hand-searching and citation-tracking to identify eligible studies. Study and tool quality were appraised for risk of bias (Quality Assessment of Diagnostic Accuracy Studies-2), methodological quality (checklist using accepted CPR methodological standards), and CPR tool appraisal (GRade and ASsess Predictive). Of 5021 studies screened, 11 diagnostic-CPRs were identified. Of the six previously known, three have been externally validated but not yet undergone impact analysis. Five new tools have been identified since Haskin etal. (2015); all are still in derivation stages. The most validated diagnostic-CPRs include the Lumbar-Spinal-Stenosis-Self-Administered-Self-Reported-History-Questionnaire and Diagnosis-Support-Tool-to-Identify-Lumbar-Spinal-Stenosis, and the StEP-tool which differentiates radicular from axial-LBP. This updated review of LBP diagnostic CPRs found five new tools, all in the early stages of development. Three previously known tools have now been externally validated but should be used with caution until impact evaluation studies are undertaken. Future funding should focus on externally validating and assessing the impact of existing CPRs on clinical decision-making.

A systematic review of the frequency of features of the seven-point checklist in proven cutaneous melanoma: The importance of change.

Pigmented skin lesions in human adults can present with several different visible features that may indicate signs of malignancy, particularly melanoma. Patient and clinician awareness of these features can aid the early recognition and melanoma diagnosis improving patient outcomes. The seven-point checklist (7PCL) is a clinical prediction rule advocated by the National Institute for Health Care Excellence to aid the assessment of pigmented skin lesions in primary care to indicate referral for specialist opinion. Assess the current evidence to establish which features of the 7PC present more frequently, so public education and clinician assessment can be focused to maximise early diagnosis and minimise referrals of benign lesions. A systematic review of published evidence identified studies that assessed the seven features of the 7PCL in histologically proven melanomas. Two independent reviewers screened eligible studies and independently extracted data and assessed quality. 112 studies were screened, 20 were assessed in full, seven met the inclusion criteria. 1184 histologically diagnosed melanomas were assessed using the 7PCL. Four studies involved patients assessing 335 melanomas, and three involved clinicians who assessed 849 melanomas. The most common feature identified was a change in size of the lesion, and the least common was inflammation. The most frequently occurring features of melanoma involve shape, size and colour, however focussing on changes in features, rather than irregularity, is more likely to identify early melanoma and increase the accuracy of referrals.

Graphical posterior predictive classification: Bayesian model averaging with particle Gibbs

In this study, we present a multi-class graphical Bayesian predictive classifier that incorporates the uncertainty in the model selection into the standard Bayesian formalism. For each class, the dependence structure underlying the observed features is represented by a set of decomposable Gaussian graphical models. Emphasis is then placed on the Bayesian model averaging which takes full account of the class-specific model uncertainty by averaging over the posterior graph model probabilities. An explicit evaluation of the model probabilities is well known to be infeasible. To address this issue, we consider the particle Gibbs strategy of J. Olsson, T. Pavlenko, and F. L. Rios [Electron. J. Statist. 13 (2019), no. 2, 2865–2897] for posterior sampling from decomposable graphical models which utilizes the so-called Christmas tree algorithm of J. Olsson, T. Pavlenko, and F. L. Rios [Stat. Comput. 32 (2022), no. 5, Paper No. 80, 18] as proposal kernel. We also derive a strong hyper Markov law which we call the hyper normal Wishart law that allows to perform the resultant Bayesian calculations locally. The proposed predictive graphical classifier reveals superior performance compared to the ordinary Bayesian predictive rule that does not account for the model uncertainty, as well as to a number of out-of-the-box classifiers.

AdaBoost Algorithm-Based Channel Estimation: Enhanced Performance

A combination of a group of rules characterized by weakness and imprecision, to reach a prediction rule known for its high precision, forms the concept of boosting, a machine learning approach. From this concept, the Adaptive Boosting (AdaBoost) algorithm spun. It is the first of its kind, and remains in use, under study, and involved in practical applications in various fields to this day. This algorithm is involved in modulation techniques, as it works on improving the bit error rate (BER). Inputting a noisy signal received from a sender into AdaBoost yields the original signal after removing noise. This is done through the reconstruction of the constellation diagram of the modulation technique, by eliminating the noise filling the data’s signal space. The result is an enhancement of the BER of 8 quadrature amplitude modulation (8QAM) and 16 quadrature amplitude modulation (16QAM), through AdaBoost. The AdaBoost algorithm is then added to the channel estimation techniques like the Least Squares (LS), Least Mean Squares (LMS) and Recursive Least Squares (RLS) and is utilized to enhance the BER performance of different estimation techniques in a Rayleigh fading environment. The AdaBoost technique allows benefiting from its learning capabilities as a machine learning algorithm in overcoming the effect of noise and fading channel on the received signals.