Validation of a guideline-based decision support system for the diagnosis of primary headache disorders based on ICHD-3 beta

BACKGROUND: China may have the largest population of headache sufferers and therefore the most serious burden of disease worldwide. However, the rate of diagnosis for headache disorders is extremely low, possibly due to the relative complexity of headache subtypes and diagnostic criteria. The use of computerized clinical decision support systems (CDSS) seems to be a better choice to solve this problem. METHODS: We developed a headache CDSS based on ICHD-3 beta and validated it in a prospective study that included 543 headache patients from the International Headache Center at the Chinese PLA General hospital, Beijing, China. RESULTS: We found that the CDSS correctly recognized 159/160 (99.4%) of migraine without aura, 36/36 (100%) of migraine with aura, 20/21 (95.2%) of chronic migraine, and 37/59 (62.7%) of probable migraine. This system also correctly identified 157/180 (87.2%) of patients with tension-type headache (TTH), of which infrequent episodic TTH was diagnosed in 12/13 (92.3%), frequent episodic TTH was diagnosed in 99/101 (98.0%), chronic TTH in 18/20 (90.0%), and probable TTH in 28/46 (60.9%). The correct diagnostic rates of cluster headache and new daily persistent headache (NDPH) were 90.0% and 100%, respectively. In addition, the system recognized 32/32 (100%) of patients with medication overuse headache. CONCLUSIONS: With high diagnostic accuracy for most of the primary and some types of secondary headaches, this system can be expected to help general practitioners at primary hospitals improve diagnostic accuracy and thereby reduce the burden of headache in China

Associations between language development and skin conductance responses to faces and eye gaze in children with autism spectrum disorder

Attention to social stimuli is associated with language development, and arousal is associated with the increased viewing of stimuli. We investigated whether skin conductance responses (SCRs) are associated with language development in ASD: a population that shows abnormalities in both attention to others and language development. A sample of 32 children with ASD (7 y – 15 y; M =9 y) was divided into two groups, based on language onset histories. A typically developing comparison group consisted of 18 age and IQ matched children. SCRs were taken as the participants viewed faces. SCRs differentiated the ASD group based on language onset and were associated with abnormal attention to gaze in infancy and subsequent language development

Investigating eye movement patterns, language, and social ability in children with autism spectrum disorder

Although all intellectually high-functioning children with autism spectrum disorder (ASD) display core social and communication deficits, some develop language within a normative timescale and others experience significant delays and subsequent language impairment. Early attention to social stimuli plays an important role in the emergence of language, and reduced attention to faces has been documented in infants later diagnosed with ASD. We investigated the extent to which patterns of attention to social stimuli would differentiate early and late language onset groups. Children with ASD (mean age = 10 years) differing on language onset timing (late/normal) and a typically developing comparison group completed a task in which visual attention to interacting and noninteracting human figures was mapped using eye tracking. Correlations on visual attention data and results from tests measuring current social and language ability were conducted. Patterns of visual attention did not distinguish typically developing children and ASD children with normal language onset. Children with ASD and late language onset showed significantly reduced attention to salient social stimuli. Associations between current language ability and social attention were observed. Delay in language onset is associated with current language skills as well as with specific eye-tracking patterns

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child's outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fetal heart ultrasound images. This study reviews nondeep and deep learning approaches for assessing the fetal heart using standard four-chamber ultrasound images. The state-of-the-art techniques in the field are described and discussed. The compendium demonstrates the capability of automatic assessment of the fetal heart using AI technology. This work can serve as a resource for research in the field

Core skills appraisal project : assessing and preparing adults to return to learning

With the introduction in 2003 of the Scottish Social Services Council's (SSSC) requirement to register, approximately 3000 residential child care (RCC) staff were faced with the prospect of returning to formal education. While residential child care workers welcomed the opportunity to improve the status of their field within the social services sector, many people had joined the sector at a time when no qualifications were required

Screening for Depression in Adults: A Summary of the Evidence for the U.S. Preventive Services Task Force

See related article on pp 760-764

Using machine learning to resolve the neural basis of alcohol dependence

Alcohol dependence is a psychiatric disorder with a lifetime prevalence of over 10% and a leading cause of morbidity and premature death. A better understanding of the neural mechanisms underlying alcohol dependence to improve prevention, diagnosis and treatment is thus of great societal interest. Recent advancements in the analysis of neuroimaging data based on machine learning have opened new paths to a better quantitative understanding of the disorder. The present habilitation reviews both works with a focus on improving machine learning methodology and empirical works in which machine learning was applied to investigate the neural basis of alcohol dependence. The methodological works advanced several aspects of machine learning in neuroimaging. In particular, they introduced i) a novel classifier (weighted robust distance – WeiRD), which operates parameter-free, computationally efficient and enables a transparent inspection of feature importances, ii) a method to preprocess neuroimaging data based on multivariate noise normalization, which yielded a substantial improvement in classification performance compared to previous the state-of-the-art, and iii) a novel method to reintroduce meaningful graded information into discretized classification accuracies by utilizing classifier decision values. Drawing on a large neuroimaging dataset of alcohol-dependent patients and controls from the LeAD-study (www.lead-studie.de; clinical trial number: NCT01679145), machine learning methods were applied in empirical works to investigate structural and functional alterations in alcohol dependence. Structural damage associated with alcohol dependence were investigated from two conceptually different angles. A first study was aimed at providing the first quantitative evidence for a long-standing hypothesis about the damaging effects of alcohol – the premature aging hypothesis. To this end, a machine learning model was trained on the relationship between grey-matter pattern information and chronological age in a healthy control group and then applied to the sample of alcohol-dependent patients. The predicted ‘brain age’ of patients was found to be was several years higher than their chronological age, thus not only providing quantitative evidence for brain aging in alcohol dependence, but also showing that these aging effects are indeed substantial in relation to the human lifespan. The second study used machine learning to quantify the predictive accuracy of grey-matter pattern information for the diagnosis and a severity measure (lifetime consumption) of alcohol dependence. On average, machine learning models correctly predicted the diagnosis in three of four subjects and accurately estimated the amount of lifetime alcohol consumption. Closer inspection of the prediction model indicated an important role of dorsal anterior cingulate cortex. Comparison with an experienced radiologist, who, like the classifier, was provided with the structural brain scans of the subjects, demonstrated superior performance of computer-based classification and in addition a more effective consideration of demographic information (age and gender). Finally, a third study used functional magnetic resonance imaging to investigate a specific hypothesis about reduced goal-directed learning in alcohol dependence as well as its relation to relapse after detoxification. Computational modelling in combination with machine learning revealed that the interaction of model-based learning and high alcohol expectancies was predictive of diagnosis (patients versus controls) and treatment outcome (abstainers versus relapsers). This finding was paralleled by a signature of model-based learning in medial prefrontal cortex, which was reduced in patients relative to controls and in relapsers relative to abstainers. In sum, the works presented in this habilitation advance machine learning methods for neuroimaging and show that these methods yield novel insights into the neural basis of alcohol dependence. An emerging theme across the three empirical studies on alcohol dependence is the disturbance of executive frontal brain structure and function, supporting a top-down rather than bottom-up view for the aetiology of alcohol dependence