Autism Spectrum Disorder’s Severity Prediction Model Using Utterance Features for Automatic Diagnosis Support

AbstractAutism spectrum disorder (ASD) is common in adolescents with cerebral palsy (CP) and there is a lack of studies applying artificial intelligence to investigate this field and this population in particular. The aim of this study is to develop and test a predictive learning model to identify factors associated with ASD in adolescents with CP. This was a multicenter controlled cohort study of 102 adolescents with CP (61 males, 41 females; mean age ± SD [standard deviation] = 16.6 ± 1.2 years; range: 12–18 years). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, intellectual disability, motor skills, and eating and drinking abilities were collected between 2005 and 2015. Statistical analysis included Fisher's exact test and multiple logistic regressions to identify factors associated with ASD. A predictive learning model was implemented to identify factors associated with ASD. The guidelines of the “transparent reporting of a multivariable prediction model for individual prognosis or diagnosis” (TRIPOD) statement were followed. Type of spasticity (hemiplegia > diplegia > tri/quadriplegia; OR [odds ratio] = 1.76, SE [standard error] = 0.2785, p = 0.04), communication disorders (OR = 7.442, SE = 0.59, p < 0.001), intellectual disability (OR = 2.27, SE = 0.43, p = 0.05), feeding abilities (OR = 0.35, SE = 0.35, p = 0.002), and motor function (OR = 0.59, SE = 0.22, p = 0.01) were significantly associated with ASD. The best average prediction model score for accuracy, specificity, and sensitivity was 75%. Motor skills, feeding abilities, type of spasticity, intellectual disability, and communication disorders were associated with ASD. The prediction model was able to adequately identify adolescents at risk of ASD.

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Lymphocyte–monocyte–neutrophil index: a predictor of severity of coronavirus disease 2019 patients produced by sparse principal component analysis

Virology Journal ◽

10.1186/s12985-021-01561-9 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Yingjie Qi ◽

Jian-an Jia ◽

Huiming Li ◽

Nagen Wan ◽

Shuqin Zhang ◽

...

Keyword(s):

Principal Component Analysis ◽

Prediction Model ◽

Disease Severity ◽

Prediction Models ◽

Principal Component ◽

Component Analysis ◽

Sparse Principal Component Analysis ◽

Validation Data ◽

Severity Prediction ◽

Prediction Efficiency

Abstract Background It is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, thus more effective predictors should be developed. Methods Clinical indicators of COVID-19 patients from two independent cohorts (Training data: Hefei Cohort, 82 patients; Validation data: Nanchang Cohort, 169 patients) were retrospected. Sparse principal component analysis (SPCA) using Hefei Cohort was performed and prediction models were deduced. Prediction results were evaluated by receiver operator characteristic curve and decision curve analysis (DCA) in above two cohorts. Results SPCA using Hefei Cohort revealed that the first 13 principal components (PCs) account for 80.8% of the total variance of original data. The PC1 and PC12 were significantly associated with disease severity with odds ratio of 4.049 and 3.318, respectively. They were used to construct prediction model, named Model-A. In disease severity prediction, Model-A gave the best prediction efficiency with area under curve (AUC) of 0.867 and 0.835 in Hefei and Nanchang Cohort, respectively. Model-A’s simplified version, named as LMN index, gave comparable prediction efficiency as classical clinical markers with AUC of 0.837 and 0.800 in training and validation cohort, respectively. According to DCA, Model-A gave slightly better performance than others and LMN index showed similar performance as albumin or neutrophil-to-lymphocyte ratio. Conclusions Prediction models produced by SPCA showed robust disease severity prediction efficiency for COVID-19 patients and have the potential for clinical application.

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An exploration of the genetic epidemiology of non-suicidal self-harm and suicide attempt

BMC Psychiatry ◽

10.1186/s12888-021-03216-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Abigail Emma Russell ◽

Gibran Hemani ◽

Hannah J Jones ◽

Tamsin Ford ◽

David Gunnell ◽

...

Keyword(s):

Suicide Attempt ◽

Prediction Model ◽

Genetic Epidemiology ◽

Complex Traits ◽

Heritability Estimate ◽

Autism Spectrum ◽

Small Sample ◽

Autism Spectrum Conditions ◽

Genetic Overlap ◽

Self Harm

AbstractBackgroundEmpirical evidence supporting the distinction between suicide attempt (SA) and non-suicidal self-harm (NSSH) is lacking. Although NSSH is a risk factor for SA, we do not currently know whether these behaviours lie on a continuum of severity, or whether they are discrete outcomes with different aetiologies. We conducted this exploratory genetic epidemiology study to investigate this issue further.MethodsWe explored the extent of genetic overlap between NSSH and SA in a large, richly-phenotyped cohort (the Avon Longitudinal Study of Parents and Children;N = 4959), utilising individual-level genetic and phenotypic data to conduct analyses of genome-wide complex traits and polygenic risk scores (PRS).ResultsThe single nucleotide polymorphism heritability of NSSH was estimated to be 13% (SE 0.07) and that of SA to be 0% (SE 0.07). Of the traits investigated, NSSH was most strongly correlated with higher IQ (rG = 0.31, SE = 0.22), there was little evidence of high genetic correlation between NSSH and SA (rG = − 0.1, SE = 0.54), likely due to the low heritability estimate for SA. The PRS for depression differentiated between those with NSSH and SA in multinomial regression. The optimal PRS prediction model for SA (NagelkerkeR20.022,p < 0.001) included ADHD, depression, income, anorexia and neuroticism and explained more variance than the optimal prediction model for NSSH (Nagelkerke R20.010,p < 0.001) which included ADHD, alcohol consumption, autism spectrum conditions, depression, IQ, neuroticism and suicide attempt.ConclusionsOur findings suggest that SA does not have a large genetic component, and that although NSSH and SA are not discrete outcomes there appears to be little genetic overlap between the two. The relatively small sample size and resulting low heritability estimate for SA was a limitation of the study. Combined with low heritability estimates, this implies that family or population structures in SA GWASs may contribute to signals detected.

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Autism Spectrum Disorder’s Severity Prediction System Using Utterance Features

Transactions of the Japanese Society for Artificial Intelligence ◽

10.1527/tjsai.b-j45 ◽

2020 ◽

Vol 35 (3) ◽

pp. B-J45_1-11

Author(s):

Masahito Sakishita ◽

Chihiro Ogawa ◽

Kenji J. Tsuchiya ◽

Toshiki Iwabuchi ◽

Taishiro Kishimoto ◽

...

Keyword(s):

Autism Spectrum ◽

Prediction System ◽

Severity Prediction

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Derivation of a simple postoperative delirium incidence and severity prediction model

10.1101/426148 ◽

2018 ◽

Author(s):

Lindroth H. ◽

Bratzke L. ◽

Twadell S. ◽

Rowley P. ◽

Kildow J. ◽

...

Keyword(s):

Prediction Model ◽

Postoperative Delirium ◽

Rating Scale ◽

Factor Model ◽

Confusion Assessment Method ◽

Assessment Method ◽

Improvement Program ◽

Risk Of Death ◽

Framingham Risk ◽

Severity Prediction

SummaryBackgroundDelirium is an important postoperative complication, yet a simple and effective delirium prediction model remains elusive. We hypothesized that the combination of the National Surgical Quality Improvement Program (NSQIP) risk calculator for serious complications (NSQIP-SC) or risk of death (NSQIP-D), and cognitive tests of executive function (Trail Making Test A and B [TMTA, TMTB]), could provide a parsimonious model to predict postoperative delirium incidence or severity.MethodsData were collected from 100 adults (≥65yo) undergoing major non-cardiac surgery. In addition to NSQIP-SC, NSQIP-D, TMTA and TMTB, we collected participant age, sex, ASA score, tobacco use, type of surgery, depression, Framingham risk score, and preoperative blood pressure. Delirium was diagnosed with the Confusion Assessment Method (CAM), and the Delirium Rating Scale-R-98 (DRS) was used to assess symptom severity. LASSO and Best Subsets logistic and linear regression were employed in line with TRIPOD guidelines.ResultsThree participants were excluded due to intraoperative deaths (2) and alcohol withdrawal (1). Ninety-seven participants with a mean age of 71.68±4.55, 55% male (31/97 CAM+, 32%) and a mean Peak DRS of 21.5±6.40 were analyzed. Of the variables included, only NSQIP-SC and TMTB were identified to be predictors of postoperative delirium incidence (p<0.001, AUROC 0.81, 95% CI: 0.72, 0.90) and severity (p<0.001, Adj. R2: 0.30).ConclusionsIn this cohort, preoperative NSQIP-SC and TMTB were identified as predictors of postoperative delirium incidence and severity. Future studies should verify whether this two-factor model could be used for accurate delirium prediction.

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