A decision support system to follow up and diagnose primary headache patients using semantically enriched data

Abstract Background Headache disorders are an important health burden, having a large health-economic impact worldwide. Current treatment & follow-up processes are often archaic, creating opportunities for computer-aided and decision support systems to increase their efficiency. Existing systems are mostly completely data-driven, and the underlying models are a black-box, deteriorating interpretability and transparency, which are key factors in order to be deployed in a clinical setting. Methods In this paper, a decision support system is proposed, composed of three components: (i) a cross-platform mobile application to capture the required data from patients to formulate a diagnosis, (ii) an automated diagnosis support module that generates an interpretable decision tree, based on data semantically annotated with expert knowledge, in order to support physicians in formulating the correct diagnosis and (iii) a web application such that the physician can efficiently interpret captured data and learned insights by means of visualizations. Results We show that decision tree induction techniques achieve competitive accuracy rates, compared to other black- and white-box techniques, on a publicly available dataset, referred to as migbase. Migbase contains aggregated information of headache attacks from 849 patients. Each sample is labeled with one of three possible primary headache disorders. We demonstrate that we are able to reduce the classification error, statistically significant (ρ≤0.05), with more than 10% by balancing the dataset using prior expert knowledge. Furthermore, we achieve high accuracy rates by using features extracted using the Weisfeiler-Lehman kernel, which is completely unsupervised. This makes it an ideal approach to solve a potential cold start problem. Conclusion Decision trees are the perfect candidate for the automated diagnosis support module. They achieve predictive performances competitive to other techniques on the migbase dataset and are, foremost, completely interpretable. Moreover, the incorporation of prior knowledge increases both predictive performance as well as transparency of the resulting predictive model on the studied dataset

Time-to-birth prediction models and the influence of expert opinions

Preterm birth is the leading cause of death among children under five years old. The pathophysiology and etiology of preterm labor are not yet fully understood. This causes a large number of unnecessary hospitalizations due to high--sensitivity clinical policies, which has a significant psychological and economic impact. In this study, we present a predictive model, based on a new dataset containing information of 1,243 admissions, that predicts whether a patient will give birth within a given time after admission. Such a model could provide support in the clinical decision-making process. Predictions for birth within 48 h or 7 days after admission yield an Area Under the Curve of the Receiver Operating Characteristic (AUC) of 0.72 for both tasks. Furthermore, we show that by incorporating predictions made by experts at admission, which introduces a potential bias, the prediction effectiveness increases to an AUC score of 0.83 and 0.81 for these respective tasks

Calculation of a Primary Immunodeficiency “Risk Vital Sign” via Population-Wide Analysis of Claims Data to Aid in Clinical Decision Support

Background: Early diagnosis of primary immunodeficiency disease leads to reductions in illness and decreased healthcare costs. Analysis of electronic health record data may allow for identification of persons at risk of host-defense impairments from within the general population. Our hypothesis was that coded infection history would inform individual risk of disease and ultimately lead to diagnosis.Methods: In this study we assessed individual risk for primary immunodeficiency by analyzing diagnostic codes and pharmacy records from members (n = 185,892) of a large pediatric health network. Relevant infection-associated diagnostic codes were weighted and enumerated for individual members allowing for risk score calculations (“Risk Vital Sign”). At-risk individuals underwent further assessment by chart review and re-analysis of diagnostic codes 12 months later.Results: Of the original cohort, 2188 (1.2%) individuals were identified as medium-high-risk for having a primary immunodeficiency. This group included 41 subjects who were ultimately diagnosed with primary immunodeficiency. An additional 57 medium-high risk patients had coded diagnoses worthy of referral.Conclusions: Population-wide informatics approaches can facilitate disease detection and improve outcomes. Early identification of the 98 patients with confirmed or suspected primary immunodeficiency described here could represent an annual cost savings of up to $7.7 million US Dollars

Utilização de um sistema de apoio à decisão clínica na reabilitação com implantes dentários e análise dos resultados clínicos : SAC Assessment Tool

Introdução: a International Team for Implantology desenvolveu a ferramenta SAC Assessment Tool para categorizar o grau de risco e complexidade das reabilitações implanto-suportadas. Os objetivos definidos para esta investigação centraram-se: na análise da aplicação desta ferramenta nos casos clínicos efetuados; no estudo da relação entre cada variável e o resultado final da ferramenta; na análise dos resultados clínicos, particularmente taxa de sucesso/sobrevivência e complicações mecânicas e biológicas da reabilitação. Materiais e Métodos: estudo observacional, longitudinal retrospetivo. Foram analisadas todas as variáveis registadas pela SAC Assessment Tool e os resultados clínicos em pacientes com reabilitações protéticas implantosuportadas executadas numa Clínica Dentária Universitária. Os dados recolhidos foram alvo de análise estatística descritiva e inferencial. Resultados: foram analisados dados da avaliação cirúrgica de 78 pacientes (131 zonas edêntulas), sendo que 38 desses tinham também avaliação protética (58 zonas edêntulas). Tempo de follow-up cirúrgico: 8 meses. A maioria dos pacientes era do sexo feminino, 46-65 anos, saudáveis, com expectativas médias-altas, higiene oral suficiente, sem hábitos tabágicos e acesso para reabilitar adequado. Maioritariamente realizaram-se reabilitações unitárias, sem risco estético, com protocolo de carga convencional e retenção aparafusada. A maioria das avaliações cirúrgicas apresentaram grau de dificuldade simples e avançado, e as protéticas um grau simples. A maior parte das variáveis da avaliação cirúrgica e os resultados clínicos apresentaram uma relação estatisticamente significativa com o resultado final da SAC, o mesmo não se verificando na avaliação protética. Conclusões: a SAC Assessment Tool é uma ferramenta muito importante na avaliação das reabilitações implanto-suportadas pela categorização exaustiva que faz dos fatores de risco envolvidos. As variáveis que demonstraram uma relação significativa com o resultado final da SAC Assessment Tool demonstram a importância da sua inclusão nesta ferramenta. O resultado final da avaliação com esta ferramenta tem relação com o resultado clínico do tratamento.Introduction: the International Team for Implantology developed the SAC Assessment Tool to categorize the degree of risk and complexity of implantsupported rehabilitations. The objectives defined for this investigation focused on: the analysis of the application of this tool in the clinical cases performed; the study of the relationship between each variable and the tool’s final result; the analysis of clinical results, particularly success/survival rate and mechanical and biological complications of the rehabilitation. Materials and Methods: observational, longitudinal retrospective study. All variables recorded by the SAC Assessment Tool and clinical outcomes in patients with implant-supported prosthetic rehabilitations performed in a University Dental Clinic were analyzed. The data collected was subjected to descriptive and inferential statistical analysis. Results: data from the surgical evaluation of 78 patients (131 edentulous areas) was analyzed, 38 of these also had prosthetic evaluation (58 edentulous areas). Surgical follow-up time: 8 months. Most patients were female, 46-65 years old, healthy, with medium-high expectations, sufficient oral hygiene, no smoking habits, and adequate access to rehabilitation. Mostly single unit rehabilitations were performed, without esthetic risk, with conventional loading protocol and screw-retained retention. Most of the surgical evaluations presented a simple and advanced degree of difficulty, and the prosthetic ones a simple degree. Most of the variables in the surgical evaluation and clinical results showed a statistically significant relationship with the final outcome of the SAC Assessment Tool, while the same wasn’t true for the prosthetic evaluation. Conclusion: the SAC Assessment Tool is very important at assessing implantsupported rehabilitations because of its comprehensive categorization of the risk factors involved. The variables that showed a significant relationship with the final result of the SAC Assessment Tool demonstrate the importance of its inclusion in this tool. The final result of the assessment with this tool is related to the clinical outcome of the treatment