Incremental Rule-based Learning

In a system which learns to predict the value of an output variable given one or more input variables by looking at a set of examples, a rule-based knowledge representation provides not only a natural method of constructing a classifier, but also a human-readable explanation of what has been learned. Consider a rule of the form if y then x where y is a conjunction of values of input variables and x is a value of the output variable. The number of input variables in y is called the order of the rule. In previous work, a measure of the information content or "value" of such a rule has been developed (the J-measure. It has been shown in [3] that a classifier can be built from the rules obtained by a constrained search of all possible rules which performs comparably with other classifiers

22q13.32 Deletion and Duplication and Inversion in the Same Family: A Rare Occurrence

Chromosome 22q13.3 deletion syndrome is a well-recognized cause of global developmental delay, while duplication of the same chromosome is a rare occurrence. The presence of both abnormalities in the same family has never been reported, to our knowledge. We report a rare occurrence of 22q13.3 duplication and 22q13.3 deletion in siblings, as a consequence of a mother's inversion on her 22nd chromosome (p13;q13.32). A 6 year old male was noted in infancy to have mild global developmental delay without dysmorphic features. His genetic testing revealed he had 22q13.3 duplication to the terminus. His 4 year old brother was noted in early infancy to have severe global developmental delay and dysmorphic features related to 22q13.3 deletion to the terminus. Their mother had a long inversion on her 22nd chromosome. Genetic tests for their father and eldest brother were unremarkable. The mother's inversion may rearrange to form 22q duplication or deletion when passed on to children. The chance of a child born with a chromosome imbalance is as high as 50%

Incremental Rule-based Learning

In a system which learns to predict the value of an output variable given one or more input variables by looking at a set of examples, a rule-based knowledge representation provides not only a natural method of constructing a classifier, but also a human-readable explanation of what has been learned. Consider a rule of the form if y then x where y is a conjunction of values of input variables and x is a value of the output variable. The number of input variables in y is called the order of the rule. In previous work, a measure of the information content or "value" of such a rule has been developed (the J-measure. It has been shown in [3] that a classifier can be built from the rules obtained by a constrained search of all possible rules which performs comparably with other classifiers

The utility of diagnostic selective nerve root blocks in the management of patients with lumbar radiculopathy: a systematic review.

OBJECTIVE: Lumbar radiculopathy (LR) often manifests as pain in the lower back radiating into one leg (sciatica). Unsuccessful back surgery is associated with significant healthcare costs and risks to patients. This review aims to examine the diagnostic accuracy of selective nerve root blocks (SNRBs) to identify patients most likely to benefit from lumbar decompression surgery. DESIGN: Systematic review of diagnostic test accuracy studies. ELIGIBILITY CRITERIA: Primary research articles using a patient population with low back pain and symptoms in the leg, SNRB administered under radiological guidance as index test, and any reported reference standard for the diagnosis of LR. INFORMATION SOURCES: MEDLINE (Ovid), MEDLINE In-Process & Other Non-Indexed Citations, EMBASE, Science Citation Index, Biosis, LILACS, Dissertation abstracts and National Technical Information Service from inception to 2018. METHODS: Risk of bias and applicability was assessed using the QUADAS-2 tool. We performed random-effects logistic regression to meta-analyse studies grouped by reference standard. RESULTS: 6 studies (341 patients) were included in this review. All studies were judged at high risk of bias. There was substantial heterogeneity across studies in sensitivity (range 57%-100%) and specificity (10%-86%) estimates. Four studies were diagnostic cohort studies that used either intraoperative findings during surgery (pooled sensitivity: 93.5% [95% CI 84.0 to 97.6]; specificity: 50.0% [16.8 to 83.2]) or 'outcome following surgery' as the reference standard (pooled sensitivity: 90.9% [83.1 to 95.3]; specificity 22.0% [7.4 to 49.9]). Two studies had a within-patient case-control study design, but results were not pooled because different types of control injections were used. CONCLUSIONS: We found limited evidence which was of low methodological quality indicating that the diagnostic accuracy of SNRB is uncertain and that specificity in particular may be low. SNRB is a safe test with a low risk of clinically significant complications, but it remains unclear whether the additional diagnostic information it provides justifies the cost of the test.National Institute for Health Research Health Technology Assessment programme grant (project number 09/111/01)

Rule-based neural networks for classification and probability estimation

In this paper we propose a network architecture that combines a rule-based approach with that of the neural network paradigm. Our primary motivation for this is to ensure that the knowledge embodied in the network is explicitly encoded in the form of understandable rules. This enables the network's decision to be understood, and provides an audit trail of how that decision was arrived at. We utilize an information theoretic approach to learning a model of the domain knowledge from examples. This model takes the form of a set of probabilistic conjunctive rules between discrete input evidence variables and output class variables. These rules are then mapped onto the weights and nodes of a feedforward neural network resulting in a directly specified architecture. The network acts as parallel Bayesian classifier, but more importantly, can also output posterior probability estimates of the class variables. Empirical tests on a number of data sets show that the rule-based classifier performs comparably with standard neural network classifiers, while possessing unique advantages in terms of knowledge representation and probability estimation

The spread of influenza A(H1N1)pdm09 in Victorian school children in 2009:iImplications for revised pandemic planning

Background Victoria was the first state in Australia to experience community transmission of influenza A(H1N1)pdm09. We undertook a descriptive epidemiological analysis of the first 1,000 notified cases to describe the epidemic associated with school children and explore implications for school closure and antiviral distribution policy in revised pandemic plans. Methods Records of the first 1,000 laboratory-confirmed cases of influenza A(H1N1)pdm09 notified to the Victorian Government Department of Health between 20 May and 5 June 2009 were extracted from the state’s notifiable infectious diseases database. Descriptive analyses were conducted on case demographics, symptoms, case treatment, prophylaxis of contacts and distribution of cases in schools. Results Two-thirds of the first 1,000 cases were school-aged (5–17 years) with cases in 203 schools, particularly along the north and western peripheries of the metropolitan area. Cases in one school accounted for nearly 8% of all cases but the school was not closed until nine days after symptom onset of the first identified case. Amongst all cases, cough (85%) was the most commonly reported symptom followed by fever (68%) although this was significantly higher in primary school children (76%). The risk of hospitalisation was 2%. The median time between illness onset and notification of laboratory confirmation was four days, with only 10% of cases notified within two days of onset and thus eligible for oseltamivir treatment. Nearly 6,000 contacts were followed up for prophylaxis. Conclusions With a generally mild clinical course and widespread transmission before its detection, limited and short-term school closures appeared to have minimal impact on influenza A(H1N1)pdm09 transmission. Antiviral treatment could rarely be delivered to cases within 48 hours of symptom onset. These scenarios and lessons learned from them need to be incorporated into revisions of pandemic plans

Independent Prognostic Significance of Monosomy 17 and Impact of Karyotype Complexity in Monosomal Karyotype/Complex Karyotype Acute Myeloid Leukemia: Results from Four ECOG-ACRIN Prospective Therapeutic Trials

The presence of a monosomal karyotype (MK+) and/or a complex karyotype (CK+) identifies subcategories of AML with poor prognosis. The prognostic significance of the most common monosomies (monosomy 5, monosomy 7, and monosomy 17) within MK+/CK+ AML is not well defined. We analyzed data from 1,592 AML patients age 17–93 years enrolled on ECOG-ACRIN therapeutic trials. The majority of MK+ patients (182/195; 93%) were MK+/CK+ with 87% (158/182) having ≥5 clonal abnormalities (CK≥ 5). MK+ patients with karyotype complexity ≤4 had a median overall survival (OS) of 0.4y compared to 1.0y for MK- with complexity ≤4 (p < 0.001), whereas no OS difference was seen in MK+ vs. MK- patients with CK≥ 5 (p = 0.82). Monosomy 5 (93%; 50/54) typically occurred within a highly complex karyotype and had no impact on OS (0.4y; p = 0.95). Monosomy 7 demonstrated no impact on OS in patients with CK≥ 5 (p = 0.39) or CK ≤ 4 (p = 0.44). Monosomy 17 appeared in 43% (68/158) of CK≥ 5 patients and demonstrated statistically significant worse OS (0.4y) compared to CK≥ 5 patients without monosomy 17 (0.5y; p = 0.012). Our data suggest that the prognostic impact of MK+ is limited to those with less complex karyotypes and that monosomy 17 may independently predict for worse survival in patients with AML