In recent years there has been interest in the use of machine learning in suicide research in reaction to the failure of traditional statistical methods to produce clinically useful models of future ...suicide. The current review summarizes recent prediction studies in the suicide literature including those using machine learning approaches to understand what value these novel approaches add.
Studies using machine learning to predict suicide deaths report area under the curve that are only modestly greater than, and sensitivities that are equal to, those reported in studies using more conventional predictive methods. Positive predictive value remains around 1% among the cohort studies with a base rate that was not inflated by case-control methodology.
Machine learning or artificial intelligence may afford opportunities in mental health research and in the clinical care of suicidal patients. However, application of such techniques should be carefully considered to avoid repeating the mistakes of existing methodologies. Prediction studies using machine-learning methods have yet to make a major contribution to our understanding of the field and are unproven as clinically useful tools.
Impulsivity is considered a possible phenotype underlying the expression of self-harm and suicidal behaviors. Yet impulsivity is a not a unitary construct and there is evidence that different facets ...of impulsivity follow different neurodevelopmental trajectories and that some facets may be more strongly associated with such behaviors than others. Moreover, it is unclear whether impulsivity is a useful predictor of self-harm or suicidal behavior in young people, a population already considered to display heightened impulsive behavior.
A systematic review and meta-analysis of studies published in Medline, PubMed, PsychInfo or Embase between 1970 and 2017 that used a neurocognitive measure to assess the independent variable of impulsivity and the dependent variable of self-harm and/or suicidal behavior among young people (mean age < 30 years old).
6183 titles were identified, 141 full texts were reviewed, and 18 studies were included, with 902 young people with a self-harm or suicidal behavior and 1591 controls without a history of these behaviors. Deficits in inhibitory control (13 studies, SMD 0.21, p-value = 0.002, 95% confidence interval (CI) (0.08–0.34), prediction interval (PI) = 0.06–0.35) and impulsive decision-making (14 studies, SMD 0.17, p-value = 0.008, 95% CI (0.045–0.3), PI = 0.03–0.31) were associated with self-harm or suicidal behavior. There were no significant differences between measures of different facets of impulsivity (ie. delay discounting, risky decision-making, cognitive or response inhibition) and self-harm or suicidal behavior.
Multiple facets of impulsivity are associated with suicidal behavior in young people. Future suicide research should be designed to capture impulsive states and investigate the impact on different subtypes of impulsivity.
The present study aimed to explore malnutrition risk, handgrip strength and quality of life (QOL) in cancer survivors.
In total, 232 individuals completed a demographic questionnaire, ...Patient-Generated Subjective Global Assessment Short Form and the European Organization for Research and Treatment of Cancer QOL Questionnaire (EORTC QLQ-C30). Handgrip strength was determined using a spring-loaded handgrip dynamometer and anthropometric measurements were taken by an oncology nurse. Frequencies and distribution data, analysis of variance and chi-squared tests were then conducted.
The majority of the cohort were female (n = 141; 60.8%) had breast cancer (n = 62; 26.7%) and the mean ± SD body mass index (BMI) was 26.6 ± 6.2 kg m
. Less than a one-third reported seeing a dietitian (n = 68; 29.3%). Over one-third reported recent weight loss (n = 88; 37.3%). Some 40.9% (n = 95) were at moderate to high risk of malnutrition, with women more likely than men to experience this (p = 0.01). Mean ± SD handgrip strength was 25 ± 15 kg and this differed significantly by gender (p = 0.00), cancer type (p = 0.01) and BMI classification (p = 0.01). One-fifth of individuals were classified as having dynapenia (n = 48; 21.1%). Median (interquartile range) QOL score was 66.7 (33.3). The proportion of individuals meeting the threshold for clinical importance for QOL subscales ranged from 12.5% (constipation) to 42.7% (physical functioning). Females were more likely than males to meet the threshold for physical functioning (p = 0.00), fatigue (p = 0.02) and pain (p = 0.01).
Females are more likely than males to be at moderate-high risk of malnutrition and meet the threshold for clinical significance for several QOL subscales.
Purpose
Machine learning (ML) has shown promise in modelling future self-harm but is yet to be applied to key questions facing clinical services. In a cohort of young people accessing primary mental ...health care, this study aimed to establish (1) the performance of models predicting deliberate self-harm (DSH) compared to suicide attempt (SA), (2) the performance of models predicting new-onset or repeat behaviour, and (3) the relative importance of factors predicting these outcomes.
Methods
802 young people aged 12–25 years attending primary mental health services had detailed social and clinical assessments at baseline and 509 completed 12-month follow-up. Four ML algorithms, as well as logistic regression, were applied to build four distinct models.
Results
The mean performance of models predicting SA (AUC: 0.82) performed better than the models predicting DSH (AUC: 0.72), with mean positive predictive values (PPV) approximately twice that of the prevalence (SA prevalence 14%, PPV: 0.32, DSH prevalence 22%, PPV: 0.40). All ML models outperformed standard logistic regression. The most frequently selected variable in both models was a history of DSH via cutting.
Conclusion
History of DSH and clinical symptoms of common mental disorders, rather than social and demographic factors, were the most important variables in modelling future behaviour. The performance of models predicting outcomes in key sub-cohorts, those with new-onset or repetition of DSH or SA during follow-up, was poor. These findings may indicate that the performance of models of future DSH or SA may depend on knowledge of the individual’s recent history of either behaviour.
Electricity price prediction through statistical and machine learning techniques captures market trends and would be a useful tool for energy traders to observe price fluctuations and increase their ...profits over time. A Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX) identifies key energy-related factors that influence hourly electricity price through prediction modelling. We propose to use a transparent NARMAX model and analyse Irish Integrated Single Electricity Market (ISEM) data from May 2019 until April 2020 to determine which external factors have a significant impact on the electricity pricing. The experimental results indicate that historical electricity price, demand, and system generation are the most significant factors with historical electricity price being the most weighted factor and the largest Error Reduction Ratio (ERR). A NARMAX model generated using correlated lags was also considered to identify key energy-related lag factors that influence the electricity price. For justification, the significant lag factors are included as inputs in a Seasonal AutoRegressive Integrated Moving Average model with eXogenous input (SARIMAX) to determine if model performance improves with refinement. To conclude, using the NARMAX methodology with energy-related input factors helps to determine the significant factors and results in accurate predictions of electricity price.
•Superior general neurocognition predicts suicidal behaviour during care in young people with affective disorders after controlling for other risk factors.•A number of individual neurocognitive ...domains predicted suicidal behaviour, including greater cognitive flexibility, faster processing speed, better working memory, delayed verbal memory and visuospatial memory.•Cognitive flexibility, though greater in those who engaged in suicidal behaviour, was significantly impaired relative to population norms in both groups.•These findings suggest a distinct clinical and neurocognitive phenotype relative to adult populations with suicidal behaviour, which has implications for suicide prevention interventions•Psychological interventions that target suicidal cognitions and behaviour are less likely to be hampered by neurocognitive deficits in populations of young adults
Neurocognitive impairment is recognised as a risk factor for suicidal behaviour in adults. The current study aims to determine whether neurocognitive deficits also predict ongoing or emergent suicidal behaviour in young people with affective disorders.
Participants were aged 12-30 years and presented to early intervention youth mental health clinics between 2008 and 2018. In addition to clinical assessment a standardised neurocognitive assessment was conducted at baseline. Clinical data was extracted from subsequent visits using a standardised proforma.
Of the 635 participants who met inclusion criteria (mean age 19.6 years, 59% female, average follow up 476 days) 104 (16%) reported suicidal behaviour during care. In 5 of the 10 neurocognitive domains tested (cognitive flexibility, processing speed, working memory, verbal memory and visuospatial memory) those with suicidal behaviour during care were superior to clinical controls. Better general neurocognitive function remained a significant predictor (OR=1.94, 95% CI 1.29- 2.94) of suicidal behaviour in care after controlling for other risk factors.
The neurocognitive battery used was designed for use with affective and psychotic disorders and may not have detected some deficits more specific to suicidal behaviour.
Contrary to expectations, better neurocognitive functioning predicts suicidal behaviour during care in young people with affective disorders. While other populations with suicidal behaviour, such as adults with affective disorders or young people with psychotic disorders, tend to experience neurocognitive deficits which may limit their capacity to engage in some interventions, this does not appear to be the case for young people with affective disorders.
In 2020 the Centers for Disease Control provided the public with recommendations to slow the spread of COVID‐19 by wearing a mask in the community. In the current study, experimenters coached group ...home staff via telehealth to implement synchronous schedules of reinforcement to increase mask wearing for 5 adults with intellectual and developmental disabilities. Results showed the intervention effectively increased mask wearing for all participants for up to 30 min. Additionally, some participants for whom we assessed generalization of mask wearing demonstrated generalization to various community environments. Furthermore, procedural integrity data suggested staff could be coached via telehealth to implement the intervention, and staff surveys suggested the procedures and coaching were socially valid.
Technical indicators have been widely applied to the financial trading market, often combined with machine learning algorithms, to predict future stock market prices. The characteristics of energy ...market data are comparable to financial trading data; hence this research derives eight price prediction technical indicators for hourly electricity prices from the Irish Integrated Single Electricity Market. The proposed indicators consider the three key types of price indicators: trend, oscillator, and momentum. Building the technical indicators from raw electricity price data helps to capture market behaviours and find information to predict future profitable prices. The electricity price data for the proposed indicators were collected from February 2019 until March 2020. Three machine learning regression algorithms were trained with the technical indicators: Extreme Gradient Boosting, Gradient Boosting, and Random Forest. The results demonstrate that the price prediction models perform much better when trained using the proposed technical indicators when compared with baseline raw price data models.
•Key types of price technical indicators are trend, oscillator, and momentum.•Novel technical indicators derived for electricity price forecasting models.•Included as model inputs in three regression machine learning algorithms.•Model accuracy and performance greatly improves with technical indicator inputs.
In the current study, experimenters implemented synchronous schedules of reinforcement to increase mask wearing for up to 30 min for six children under the age of 5 years. Additionally, for a subset ...of children, we evaluated whether mask wearing would continue under baseline conditions in their classroom with staff during 30 min sessions (treatment extension), and later throughout the day (all‐day probes). Results showed the intervention increased mask wearing for all children for up to 30 min. Additionally, treatment‐extension sessions and all‐day probes, conducted with some children, showed mask wearing maintained in their classroom with staff.
PDF
