A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis

BACKGROUND: The aim of this study is to develop a simple and reliable hybrid decision support model by combining statistical analysis and decision tree algorithms to ensure high accuracy of early diagnosis in patients with suspected acute appendicitis and to identify useful decision rules. METHODS: We enrolled 326 patients who attended an emergency medical center complaining mainly of acute abdominal pain. Statistical analysis approaches were used as a feature selection process in the design of decision support models, including the Chi-square test, Fisher's exact test, the Mann-Whitney U-test (p < 0.01), and Wald forward logistic regression (entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively). The final decision support models were constructed using the C5.0 decision tree algorithm of Clementine 12.0 after pre-processing. RESULTS: Of 55 variables, two subsets were found to be indispensable for early diagnostic knowledge discovery in acute appendicitis. The two subsets were as follows: (1) lymphocytes, urine glucose, total bilirubin, total amylase, chloride, red blood cell, neutrophils, eosinophils, white blood cell, complaints, basophils, glucose, monocytes, activated partial thromboplastin time, urine ketone, and direct bilirubin in the univariate analysis-based model; and (2) neutrophils, complaints, total bilirubin, urine glucose, and lipase in the multivariate analysis-based model. The experimental results showed that the model with univariate analysis (80.2%, 82.4%, 78.3%, 76.8%, 83.5%, and 80.3%) outperformed models using multivariate analysis (71.6%, 69.3%, 73.7%, 69.7%, 73.3%, and 71.5% with entry and removal criteria of 0.01 and 0.05; 73.5%, 66.0%, 80.0%, 74.3%, 72.9%, and 73.0% with entry and removal criteria of 0.05 and 0.10) in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under ROC curve, during a 10-fold cross validation. A statistically significant difference was detected in the pairwise comparison of ROC curves (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1). The larger induced decision model was more effective for identifying acute appendicitis in patients with acute abdominal pain, whereas the smaller induced decision tree was less accurate with the test data. CONCLUSIONS: The decision model developed in this study can be applied as an aid in the initial decision making of clinicians to increase vigilance in cases of suspected acute appendicitis

Appendicitis risk prediction models in children presenting with right iliac fossa pain (RIFT study): a prospective, multicentre validation study

BACKGROUND: Acute appendicitis is the most common surgical emergency in children. Differentiation of acute appendicitis from conditions that do not require operative management can be challenging in children. This study aimed to identify the optimum risk prediction model to stratify acute appendicitis risk in children. METHODS: We did a rapid review to identify acute appendicitis risk prediction models. A prospective, multicentre cohort study was then done to evaluate performance of these models. Children (aged 5-15 years) presenting with acute right iliac fossa pain in the UK and Ireland were included. For each model, score cutoff thresholds were systematically varied to identify the best achievable specificity while maintaining a failure rate (ie, proportion of patients identified as low risk who had acute appendicitis) less than 5%. The normal appendicectomy rate was the proportion of resected appendixes found to be normal on histopathological examination. FINDINGS: 15 risk prediction models were identified that could be assessed. The cohort study enrolled 1827 children from 139 centres, of whom 630 (34·5%) underwent appendicectomy. The normal appendicectomy rate was 15·9% (100 of 630 patients). The Shera score was the best performing model, with an area under the curve of 0·84 (95% CI 0·82-0·86). Applying score cutoffs of 3 points or lower for children aged 5-10 years and girls aged 11-15 years, and 2 points or lower for boys aged 11-15 years, the failure rate was 3·3% (95% CI 2·0-5·2; 18 of 539 patients), specificity was 44·3% (95% CI 41·4-47·2; 521 of 1176), and positive predictive value was 41·4% (38·5-44·4; 463 of 1118). Positive predictive value for the Shera score with a cutoff of 6 points or lower (72·6%, 67·4-77·4) was similar to that of ultrasound scan (75·0%, 65·3-83·1). INTERPRETATION: The Shera score has the potential to identify a large group of children at low risk of acute appendicitis who could be considered for early discharge. Risk scoring does not identify children who should proceed directly to surgery. Medium-risk and high-risk children should undergo routine preoperative ultrasound imaging by operators trained to assess for acute appendicitis, and MRI or low-dose CT if uncertainty remains. FUNDING: None

From 'tree' based Bayesian networks to mutual information classifiers : deriving a singly connected network classifier using an information theory based technique

For reasoning under uncertainty the Bayesian network has become the representation of choice. However, except where models are considered 'simple' the task of construction and inference are provably NP-hard. For modelling larger 'real' world problems this computational complexity has been addressed by methods that approximate the model. The Naive Bayes classifier, which has strong assumptions of independence among features, is a common approach, whilst the class of trees is another less extreme example. In this thesis we propose the use of an information theory based technique as a mechanism for inference in Singly Connected Networks. We call this a Mutual Information Measure classifier, as it corresponds to the restricted class of trees built from mutual information. We show that the new approach provides for both an efficient and localised method of classification, with performance accuracies comparable with the less restricted general Bayesian networks. To improve the performance of the classifier, we additionally investigate the possibility of expanding the class Markov blanket by use of a Wrapper approach and further show that the performance can be improved by focusing on the class Markov blanket and that the improvement is not at the expense of increased complexity. Finally, the two methods are applied to the task of diagnosing the 'real' world medical domain, Acute Abdominal Pain. Known to be both a different and challenging domain to classify, the objective was to investigate the optiniality claims, in respect of the Naive Bayes classifier, that some researchers have argued, for classifying in this domain. Despite some loss of representation capabilities we show that the Mutual Information Measure classifier can be effectively applied to the domain and also provides a recognisable qualitative structure without violating 'real' world assertions. In respect of its 'selective' variant we further show that the improvement achieves a comparable predictive accuracy to the Naive Bayes classifier and that the Naive Bayes classifier's 'overall' performance is largely due the contribution of the majority group Non-Specific Abdominal Pain, a group of exclusion

Estudio de un nuevo algoritmo de diagnóstico de dolor en fosa ilíaca derecha en el servicio de urgencias y validación de algoritmos clásicos de diagnóstico de la apendicitis aguda

Introducció: Actualment els clínics dels serveis d’urgències no disposem d’un model de diagnòstic de dolor de la fosa ilíaca dreta (FID). Objectiu: Construcció d’un model senzill basat amb arbres de classificació (CHAID) i model de xarxa neuronal artificial (XNA) que combini els models clàssics, marcadors d’inflamació, característiques del pacient i clínica del dolor en FID a Urgències. Metodologia: Estudi prospectiu observacional on s’inclogueren pacients majors de 14 anys que van acudir al servei d’urgències de l’hospital Universitari Arnau de Vilanova de Lleida per dolor a la FID, de més de 6h d’evolució. Durant el temps d’estudi a tots se’ls hi van recollir les principals característiques demogràfiques, es van determinar els nivells de leucocits , proteica C reactiva (PCR) i les variables referents a la clínica i exploració física que formen els models clàssics de diagnòstic de d’apendicitis aguda (AA). Construcció d’un model multivariable multinominal amb metodologia CART (Clasification and Regression Trees, selecció automàtica amb jerarquia de variables, punts de tall de variables continues i sistema de validació creuada). Valoració mitjançant anàlisis ROC ( AUC (CI 95%)). Resultats: Es van recollir 252 casos, 53% eren homes. Edat mitjana 33.3-16 anys. Diagnòstics finals en 4 grups:1 - Dolor simple en FID (dFID) 45%, 2 - Apendicitis aguda (AA) 37%, 3 - Dolor abdominal sense procés infamatori (DACPI) 12%, 4 - Dolor abdominal amb procés inflamatori (DACPI) 6%. Rendiment dels models senzills: Alvarado score (ALS) amb 0.82(0.76-0.87) i PCR amb 0.83(0.77-0.88), Fenyö-Linberg score (FLS) amb 0.88(0.84-0.92). Model XNA determina 4 grups de diagnòstics amb probabilitat: dFID de 0,92(0,88-0,96), AA de 0.95(0.91-0.98), DASPI de 0.92(0.84-0.99) i DACPI de 0,84(0,70-0,99). El CHAID selecciona les variables ALS, PCR, gènere, hores d’evolució de la clínica i dolor amb la tos. El CHAID determina 10 grups de pacients (regles de decisió): 3 amb probabilitat de DFID (71,1-84,4-87%), 5 amb probabilitat e AA (52-52,6-72,7-72,7-94,1%) i 1 amb probabilitat de DASPI 60% i 1 sense probabilitat individual superior al 50%. L’AC aconsegueix un rendiment per a DFID de 0.89(0.85-0.93), per a AA de 0.93(0.90-0.96),, per a DASPI de 0.86 (0.81-0.92) i per a DACPI de 0.82(0.73-0.90). Conclusions: Per separat, el rendiment diagnòstic dels scores clàssics o de la PCR és insuficient per estratificar la probabilitat diagnosticada en pacients amb dolor en FID. La metodologia basada en CHAID ofereix una eina senzilla per establir a urgències grups de pacients amb diferent ris diagnòstic. El model XNA aconsegueix classificar als pacients però te una interpretació nul·la de la red obtinguda. El CHAID troba grups amb una altra probabilitat de AA i de dFID. Els pacients amb dubtes de diagnòstic es beneficiaran de més probes diagnòstiques i/o període en observació.Introducción: Actualmente los clínicos de los servicios de urgencias no disponemos de un modelo de diagnóstico de dolor en fosa ilíaca derecha (FID). Objetivo: Elaboración de un modelo de diagnóstico sencillo basado en árboles de clasificación (CHAID) y en un modelo de red neuronal artificial (RNA) que combine los modelos clásicos, los marcadores de inflamación sistémica y las características del paciente que presenta clínica de dolor en FID en Urgencias. Metodología: Estudio prospectivo observacional en el que se incluyeron pacientes mayores de 14 años que acudieron a servicio de urgencias del Hospital Universitario Arnau de Vilanova de Lleida por dolor en FID de más de 6 h. de evolución. Durante el tiempo de duración del estudio se recogieron una serie de parámetros a todos los pacientes entre los cuáles destacan sus características demográficas, nivel de leucocitos y proteína C reactiva (PCR) en suero junto a variables clínicas y de exploración física que determinan los modelos clásicos de diagnóstico de apendicitis aguda (AA). Se construyó un modelo multivariable multinomial con metodología CART (Clasification and Regression Trees, selección automática con jerarquía de variables , puntos de corte de variables continuas y sistema de validación cruzada). Valoración mediante análisis ROC -AUC(CI 95%)-. Resultados: Se obtuvo una N= 252 casos. La distribución por sexo fue 53% hombres y 47% mujeres. Edad media 33.3±16 años. Los diagnósticos finales obtenidos se clasificaron en 4 grupos con la siguiente distribución: 1- Dolor simple en FID (dFID) 45%, 2- Apendicitis aguda (AA) 37%, 3- Dolor abdominal sin proceso inflamatorio (DASPI) 12%, 4- Dolor abdominal con proceso inflamatorio (DACPI) 6%. Rendimiento de los modelos sencillos: Alvarado score (ALS) con 0.82(0.76-0.87) y PCR con 0.83(0.77-0.88), Fenyö-Linberg score (FLS) con 0,88(0,84- 0,92). Modelo RNA grupos diagnósticos con la siguiente probabilidad: dFID de 0,92(0,88-0,96), AA de 0,95(0,91-0,98), DASPI de 0,92(0,84-0,99) y DACPI de 0,84(0,70-0,99. El CHAID selecciona las variable ALS, PCR, género, horas de evolución de la clínica y dolor con la tos, determinando 10 grupos de pacientes (reglas de decisión): 3 con probabilidad de dFID (71,1-84,4-87%), 5 con probabilidad de AA (52-52,6-72,7-72,7-94,1 %) y 1 con probabilidad de DASPI 60% y 1 sin probabilidad individual superior al 50 %. El AC consigue un rendimiento para dFID de 0.89(0.85-0.93), para AA de 0.93(0.90-0.96), para DASPI de 0.86(0.81-0.92) y para DACPI de 0.82(0.73-0.90). Conclusiones: Por separado, el rendimiento diagnóstico de los scores clásicos o de la PCR es insuficiente para estratificar la probabilidad diagnóstica en pacientes con dolor en FID. La metodología basada en CHAID ofrece una herramienta sencilla para establecer en Urgencias grupos de pacientes con distinto riesgo diagnóstico. El modelo RNA consigue clasificar a los pacientes pero tiene nula interpretación de la red obtenida. El CHAID encuentra grupos con alta probabilidad de AA y de dFID. Los pacientes con dudas diagnósticas se beneficiarán de más pruebas diagnósticas y/o período de observación.Introduction: Nowadays, the professionals of emergency departments do not have the diagnostic algorithm for right iliac fossa pain (RIF). Objectives: Construction of simple diagnostic algorithm for RIF pain based on Classification Tree and Artificial Neural Network (ANN) methods, which combines classical models for diagnosis acute appendicitis, inflammatory markers, patient characteristics and clinic RIF pain in Emergency Department. Methods: The prospective observational study, which includes patients, older then 14 years, with RIF pain who were admitted in Emergency Department of University Hospital Arnau de Villanova of Lleida. The signs, symptoms, laboratory values and pathology reports of each patient were collected and evaluated. The construction of multinomial multivariable model was done using CART methodology (Classification and Regression Trees, autonomic selection of hierarchy of variables, cutoff points of continuous variables and cross-validation) and Artificial Neural Network (ANN) method. Valuation was done using ROC analysis (AUC (95% CI)). Results: Out of total 252 patients, 53% were males. The age ranged 33.3±16 years. Final diagnosis we divided in 4 groups: 1- (NsP) Nonspecific RIF pain (45 %), 2 – (AA) Acute appendicitis (37%), 3 - (NID) other abdominal disease without inflammation (12%), 4 - (IBD) Inflammatory bowel disease (6.0 %). Efficiency of simple models: Alvarado score (ALS) 0,82(0,76- 0,87) and C-reactive protein (CRP) 0,83(0,77-0,88), Fenyö-Linberg score (FLS) 0,88(0,84-0,92). The CT selects the variables of ASS, CRP, sex and duration of the clinical symptoms determining 7 groups of patients (application of decision rules): 3 groups of probability of AA (59,3-62,5-90,5%), 2 with probability of NsP (68,9-82,6 %) and 2 without probability superior then 50%. The CT shows the efficiency for AA of 0,89 (0,85-0,93), NsP 0,84 (0,79-0,89), IBD of 0,84 (0,78-0,90) and for NID 0,66 (0,57-0,75). Conclusions: The classic score and CRP have insufficient diagnostic efficiency to stratify the diagnostic probability to patients with right iliac fossa pain. The methodology based on CHAID offer us the simple way to establish the groups of patients with different diagnostic in Emergency Department. The ANN method obtains to classify the patients but it has no interpretation. The decision tree technique finds high probability of the groups with AA and NsP. The patients with questionable diagnostic will benefit of another diagnostic proofs o longer observation period

Toward a Discourse Community for Telemedicine: A Domain Analytic View of Published Scholarship

In the past 20 years, the use of telemedicine has increased, with telemedicine programs increasingly being conducted through the Internet and ISDN technologies. The purpose of this dissertation is to examine the discourse community of telemedicine. This study examined the published literature on telemedicine as it pertains to quality of care, defined as correct diagnosis and treatment (Bynum and Irwin 2011). Content analysis and bibliometrics were conducted on the scholarly discourse, and the most prominent authors and journals were documented to paint and depict the epistemological map of the discourse community of telemedicine. A taxonomy based on grounded research of scholarly literature was developed and validated against other existing taxonomies. Telemedicine has been found to increase the quality and access of health care and decrease health care costs (Heinzelmann, Williams, Lugn and Kvedar 2005 and Wootton and Craig 1999). Patients in rural areas where there is no specialist or patients who find it difficult to get to a doctor’s office benefit from telemedicine. Little research thus far has examined scholarly journals in order to aggregate and analyze the prevalent issues in the discourse community of telemedicine. The purpose of this dissertation is to empiricallydocument the prominent topics and issues in telemedicine by examining the related published scholarly discourse of telemedicine during a snapshot in time. This study contributes to the field of telemedicine by offering a comprehensive taxonomy of the leading authors and journals in telemedicine, and informs clinicians, librarians and other stakeholders, including those who may want to implement telemedicine in their institution, about issues telemedicine

12 Chapters on Nuclear Medicine

The development of nuclear medicine as a medical specialty has resulted in the large-scale application of its effective imaging methods in everyday practice as a primary method of diagnosis. The introduction of positron-emitting tracers (PET) has represented another fundamental leap forward in the ability of nuclear medicine to exert a profound impact on patient management, while the ability to produce radioisotopes of different elements initiated a variety of tracer studies in biology and medicine, facilitating enhanced interactions of nuclear medicine specialists and specialists in other disciplines. At present, nuclear medicine is an essential part of diagnosis of many diseases, particularly in cardiologic, nephrologic and oncologic applications and it is well-established in its therapeutic approaches, notably in the treatment of thyroid cancers. Data from official sources of different countries confirm that more than 10-15 percent of expenditures on clinical imaging studies are spent on nuclear medicine procedures