The Development of a Clinical Decision Support System for the Management of Pediatric Food Allergy

The role of pediatricians in the diagnosis and management of IgE-mediated food allergy: a review

Importance

Food allergy can often cause a significant burden on patients, families, and healthcare systems. The complexity of food allergy management requires a multidisciplinary approach involving different types of healthcare providers, including allergists, dieticians, psychologists, nurses, family practitioners and, of particular relevance for this article, pediatric primary caretakers. Pediatricians may be the first-line healthcare providers for food allergy: strategies for management and guideline adherence have been highlighted.

Observations

This review article summarizes the up-to-date recommendations on the role of pediatricians in the diagnosis, management, and prevention of IgE-mediated food allergy. Early introduction of allergenic foods like peanut is known to be of importance to reduce the development of peanut allergy in infants, and pediatricians are essential for educating and supporting parents in this decision. In scenarios of limited allergist availability, as is often the case among rural, Medicaid and minority populations, pediatricians can assist in the evaluation and management of food allergy, and provide action plans, education and counselling for patients and families.

Conclusions and relevance

Pediatric primary caretakers play a key role in the diagnosis, management, and prevention of IgE-mediated food allergy. As more diagnostic tools and therapies in food allergy become available, the need for a multidisciplinary team is paramount to optimize patient care.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

BACKGROUND

 Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.

OBJECTIVES

 This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.

METHODS

 The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.

RESULTS

 Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.

CONCLUSION

 The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases

Clinical decision support systems (CDSS) aid health care professionals (HCP) in evaluating large sets of information and taking informed decisions during their clinical routine. CDSS are becoming particularly important in the perspective of precision medicine, when HCP need to consider growing amounts of data to create precise patient profiles for personalized diagnosis, treatment and outcome monitoring. In allergy care, several CDSS are being developed and investigated, mainly for respiratory allergic diseases. Although the proposed solutions address different stakeholders, the majority aims at facilitating evidence-based and shared decision-making, incorporating guidelines, and real-time clinical data. We offer here an overview on existing tools, new developments and novel concepts and discuss the potential of digital CDSS in improving prevention, diagnosis and monitoring of allergic diseases.

Research of data mining and web technology in university discipline construction decision support system based on MVC model

Purpose

Taking the discipline construction in colleges and universities as the application background, based on the research on data mining technology and decision support system technology, the data generated by university management information system are effectively utilized. The paper aims to discuss these issues.

Design/methodology/approach

Based on the Beijing Key Discipline Information Platform as the data source, the decision tree algorithm of data mining is studied. On the basis of decision tree C4.5, the Bayesian theory is applied to the post-pruning operation of the decision tree.

Findings

A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with browser/server (B/S) structure is completed and implemented.

Originality/value

A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with B/S structure is completed and implemented.

Changes in Emergency Department Concordance with Guidelines for the Management of Food-Induced Anaphylaxis: 1999-2001 versus 2013-2015

BACKGROUND

Awareness about food allergy and food-induced anaphylaxis (FIA) has increased dramatically over the past decade. It remains unclear, however, whether concordance with guidelines for FIA management has improved over time.

OBJECTIVE

Our objective was to describe changes in emergency department (ED) concordance with guidelines for FIA management.

METHODS

We analyzed data from 2 multicenter retrospective studies of patients with food-related acute allergic reactions seen in 1 of 17 EDs during 2 time periods: 1999 to 2001 and 2013 to 2015. Visits were identified similarly across years-for example, using International Classification of Diseases, Ninth Revision, Clinical Modification codes 693.1, 995.60, 995.61-995.69, 995.0, and 995.3. Anaphylaxis was defined as an acute allergic reaction with involvement of 2+ organ systems or hypotension. We compared concordance between time periods for 4 guideline recommendations: (1) treatment with epinephrine, (2) discharge prescription for an epinephrine autoinjector (EAI), (3) referral to an allergist/immunologist, and (4) instructions to avoid offending allergen.

RESULTS

We compared 290 patients with FIA during 1999 to 2001 and 459 during 2013 to 2015. Any treatment with epinephrine (pre-ED or in the ED) for patients with FIA increased over time (38% vs 56%; P < .001). Prescriptions for EAI at discharge (24% vs 54%; P < .001) and documentation for referral to an allergist/immunologist (14% vs 24%; P = .001) approximately doubled, whereas instructions to avoid the offending allergen did not change significantly (37% vs 43%; P = .08). Receipt of 3+ guideline recommendations remained low but almost quadrupled over the study interval (6% vs 23%; P < .001).

CONCLUSIONS

Over the nearly 15-year study interval, we observed clinically and statistically significant increases in ED concordance with epinephrine-related guidelines for FIA. Management gaps remain and interventions to standardize care still appear warranted.

Changes in emergency department concordance with guidelines for the management of stinging insect-induced anaphylaxis: 1999-2001 vs 2013-2015

BACKGROUND

Changes in emergency department (ED) concordance with guidelines for the management of stinging insect-induced anaphylaxis (SIIA) are not known.

OBJECTIVE

To describe temporal changes in ED concordance with guidelines for the management of SIIAs.

METHODS

We analyzed data from 2 multicenter retrospective studies of patients with stinging insect-related acute allergic reactions seen in 1 of 14 North American EDs during 2 periods: 1999 through 2001 and 2013 through 2015. Visits were identified similarly across studies (eg, using International Classification of Diseases, Ninth Revision, Clinical Modification codes 989.5, 995.0, and 995.3). Anaphylaxis was defined as an acute allergic reaction with involvement of at least 2 organ systems or hypotension. We compared concordance between periods with 4 guideline recommendations: (1) treatment with epinephrine, (2) discharge prescription for epinephrine auto-injector, (3) referral to an allergist/immunologist, and (4) instructions to avoid the offending allergen.

RESULTS

We compared 182 patients with SIIA during 1999 to 2001 with 204 during 2013 to 2015. Any treatment with epinephrine (before arrival to the ED or in the ED) increased over time (30% vs 49%; P < .001). Prescriptions for epinephrine auto-injector at discharge increased significantly (34% vs 57%; P < .001), whereas documentation of referral to an allergist/immunologist decreased (28% vs 12%; P = .002), and instructions to avoid the offending allergen did not change (23% vs 24%; P = .94). Receipt of at least 3 guideline recommendations increased over time; however, the comparison was not statistically significant (10% vs 16%; P = .15).

CONCLUSION

During the nearly 15-year study interval, we observed increased ED concordance with epinephrine-related guideline recommendations for the management of SIIA. Reasons for the decrease in allergy/immunology referrals merit further study.

How the Smartphone Is Changing Allergy Diagnostics

PURPOSE OF REVIEW

Evidence-based clinical diagnosis of allergic disorders is increasingly challenging. Clinical decision support systems implemented in mobile applications (apps) are being developed to assist clinicians in diagnostic decisions at the point of care. We reviewed apps for allergic diseases general diagnosis, diagnostic refinement and diagnostic personalisation. Apps designed for specific medical devices are not addressed.

RECENT FINDINGS

Apps with potential usefulness in the initial diagnosis and diagnostic refinement of respiratory, food, skin and drug allergies are described. Apps to support diagnostic personalisation are not yet available. There is an urgent need to increase the scientific evidence on the real usefulness of these apps, as well as to develop new scientifically grounded apps designed and validated to support all allergic diseases and diagnostic levels. Apps have the potential to change the diagnosis of allergic diseases becoming part of the routine diagnostics toolset, but its usefulness needs to be established.

Design of a Clinical Decision Support System for Fracture Prediction Using Imbalanced Dataset

More than 1 billion people suffer from chronic respiratory diseases worldwide, accounting for more than 4 million deaths annually. Inhaled corticosteroid is a popular medication for treating chronic respiratory diseases. Its side effects include decreased bone mineral density and osteoporosis. The aims of this study are to investigate the association of inhaled corticosteroids and fracture and to design a clinical support system for fracture prediction. The data of patients aged 20 years and older, who had visited healthcare centers and been prescribed with inhaled corticosteroids within 2002-2010, were retrieved from the National Health Insurance Research Database (NHIRD). After excluding patients diagnosed with hip fracture or vertebrate fractures before using inhaled corticosteroid, a total of 11645 patients receiving inhaled corticosteroid therapy were included for this study. Among them, 1134 (9.7%) were diagnosed with hip fracture or vertebrate fracture. The statistical results showed that demographic information, chronic respiratory diseases and comorbidities, and corticosteroid-related variables (cumulative dose, mean exposed daily dose, follow-up duration, and exposed duration) were significantly different between fracture and nonfracture patients. The clinical decision support systems (CDSSs) were designed with integrated genetic algorithm (GA) and support vector machine (SVM) by training and validating the models with balanced training sets obtained by random and cluster-based undersampling methods and testing with the imbalanced NHIRD dataset. Two different objective functions were adopted for obtaining optimal models with best predictive performance. The predictive performance of the CDSSs exhibits a sensitivity of 69.84-77.00% and an AUC of 0.7495-0.7590. It was concluded that long-term use of inhaled corticosteroids may induce osteoporosis and exhibit higher incidence of hip or vertebrate fractures. The accumulated dose of ICS and OCS therapies should be continuously monitored, especially for patients with older age and women after menopause, to prevent from exceeding the maximum dosage.

Development of an allergy management support system in primary care

Background

Management of allergic patients in the population is becoming more difficult because of increases in both complexity and prevalence. Although general practitioners (GPs) are expected to play an important role in the care of allergic patients, they often feel ill-equipped for this task. Therefore, the aim of this study was to develop an allergy management support system (AMSS) for primary care.

Methods

Through literature review, interviewing and testing in secondary and primary care patients, an allergy history questionnaire was constructed by allergists, dermatologists, GPs and researchers based on primary care and specialists’ allergy guidelines and their clinical knowledge. Patterns of AMSS questionnaire responses and specific immunoglobulin E (sIgE)-test outcomes were used to identify diagnostic categories and develop corresponding management recommendations. Validity of the AMSS was investigated by comparing specialist (gold standard) and AMSS diagnostic categories.

Results

The two-page patient-completed AMSS questionnaire consists of 12 (mainly) multiple choice questions on symptoms, triggers, severity and medication. Based on the AMSS questionnaires and sIgE-test outcome of 118 patients, approximately 150 diagnostic categories of allergic rhinitis, asthma, atopic dermatitis, anaphylaxis, food allergy, hymenoptera allergy and other allergies were identified, and the corresponding management recommendations were formulated. The agreement between the allergy specialists’ assessments and the AMSS was 69.2% (CI 67.2–71.2).

Conclusion

Using a systematic approach, it was possible to develop an AMSS that allows for the formulation of diagnostic and management recommendations for GPs managing allergic patients. The AMSS thus holds promise for the improvement of the quality of primary care for this increasing group of patients.