Using machine learning techniques to characterize sleep-deprived driving behavior.

OBJECTIVE: Sleep deprivation is known to affect driving behavior and may lead to serious car accidents similar to the effects from e.g., alcohol. In a previous study, we have demonstrated that the use of machine learning techniques allows adequate characterization of abnormal driving behavior after alprazolam and/or alcohol intake. In the present study, we extend this approach to sleep deprivation and test the model for characterization of new interventions. We aimed to classify abnormal driving behavior after sleep deprivation, and, by using a machine learning model, we tested if this model could also pick up abnormal driving behavior resulting from other interventions. METHODS: Data were collected during a previous study, in which 24 subjects were tested after being sleep-deprived and after a well-rested night. Features were calculated from several driving parameters, such as the lateral position, speed of the car, and steering speed. In the present study, we used a gradient boosting model to classify sleep deprivation. The model was validated using a 5-fold cross validation technique. Next, probability scores were used to identify the overlap of driving behavior after sleep deprivation and driving behavior affected by other interventions. In the current study alprazolam, alcohol, and placebo are used to test/validate the approach. RESULTS: The sleep deprivation model detected abnormal driving behavior in the simulator with an accuracy of 77 ± 9%. Abnormal driving behavior after alprazolam, and to a lesser extent also after alcohol intake, showed remarkably similar characteristics to sleep deprivation. The average probability score for alprazolam and alcohol measurements was 0.79, for alcohol 0.63, and for placebo only 0.27 and 0.30, matching the expected relative drowsiness. CONCLUSION: We developed a model detecting abnormal driving induced by sleep deprivation. The model shows the similarities in driving characteristics between sleep deprivation and other interventions, i.e., alcohol and alprazolam. Consequently, our model for sleep deprivation may serve as a next reference point for a driving test battery of newly developed drugs.

The use of machine learning improves the assessment of drug-induced driving behaviour.

RATIONALE: Car-driving performance is negatively affected by the intake of alcohol, tranquillizers, sedatives and sleep deprivation. Although several studies have shown that the standard deviation of the lateral position on the road (SDLP) is sensitive to drug-induced changes in simulated and real driving performance tests, this parameter alone might not fully assess and quantify deviant or unsafe driving. OBJECTIVE: Using machine learning we investigated if including multiple simulator-derived parameters, rather than the SDLP alone would provide a more accurate assessment of the effect of substances affecting driving performance. We specifically analysed the effects of alcohol and alprazolam. METHODS: The data used in the present study were collected during a previous study on driving effects of alcohol and alprazolam in 24 healthy subjects (12 M, 12 F, mean age 26 years, range 20-43 years). Various driving features, such as speed and steering variations, were quantified and the influence of administration of alcohol or alprazolam was assessed to assist in designing a predictive model for abnormal driving behaviour. RESULTS: Adding additional features besides the SDLP increased the model performance for prediction of drug-induced abnormal driving behaviour (from an accuracy of 65 %-83 % after alprazolam intake and from 50 % to 76 % after alcohol ingestion). Driving behaviour influenced by alcohol and alprazolam was characterised by different feature importance, indicating that the two interventions influenced driving behaviour in a different way. CONCLUSION: Machine learning using multiple driving features in addition to the state-of-the-art SDLP improves the assessment of drug-induced abnormal driving behaviour. The created models may facilitate quantitative description of abnormal driving behaviour in the development and application of psychopharmacological medicines. Our models require further validation using similar and unknown interventions.

Body mass index related electrocardiographic findings in healthy young individuals with a normal body mass index.

INTRODUCTION: An increased body mass index (BMI) (>25 kg/m2) is associated with a wide range of electrocardiographic changes. However, the association between electrocardiographic changes and BMI in healthy young individuals with a normal BMI (18.5-25 kg/m2) is unknown. The aim of this study was to evaluate the association between BMI and electrocardiographic parameters. METHODS: Data from 1,290 volunteers aged 18 to 30 years collected at our centre were analysed. Only subjects considered healthy by a physician after review of collected data with a normal BMI and in sinus rhythm were included in the analysis. Subjects with a normal BMI (18.5-25 kg/m2) were divided into BMI quartiles analysis and a backward multivariate regression analysis with a normal BMI as a continuous variable was performed. RESULTS: Mean age was 22.7 ± 3.0 years, mean BMI was 22.0, and 73.4% were male. There were significant differences between the BMI quartiles in terms of maximum P-wave duration, P-wave balance, total P-wave area in lead V1, PR-interval duration, and heart axis. In the multivariate model maximum P-wave duration (standardised coefficient (SC) = +0.112, P < 0.001), P-wave balance in lead V1 (SC = +0.072, P < 0.001), heart axis (SC = -0.164, P < 0.001), and Sokolow-Lyon voltage (SC = -0.097, P < 0.001) were independently associated with BMI. CONCLUSION: Increased BMI was related with discrete electrocardiographic alterations including an increased P-wave duration, increased P-wave balance, a leftward shift of the heart axis, and decreased Sokolow-Lyon voltage on a standard twelve lead electrocardiogram in healthy young individuals with a normal BMI.

A systematic literature review of the human skin microbiome as biomarker for dermatological drug development.

AIMS: To explore the potential of the skin microbiome as biomarker in six dermatological conditions: atopic dermatitis (AD), acne vulgaris (AV), psoriasis vulgaris (PV), hidradenitis suppurativa (HS), seborrhoeic dermatitis/pityriasis capitis (SD/PC) and ulcus cruris (UC). METHODS: A systematic literature review was conducted according to the PRISMA guidelines. Two investigators independently reviewed the included studies and ranked the suitability microbiome implementation for early phase clinical studies in an adapted GRADE method. RESULTS: In total, 841 papers were identified and after screening of titles and abstracts for eligibility we identified 42 manuscripts that could be included in the review. Eleven studies were included for AD, five for AV, 10 for PV, two for HS, four for SD and 10 for UC. For AD and AV, multiple studies report the relationship between the skin microbiome, disease severity and clinical response to treatment. This is currently lacking for the remaining conditions. CONCLUSION: For two indications - AD and AV - there is preliminary evidence to support implementation of the skin microbiome as biomarkers in early phase clinical trials. For PV, UC, SD and HS there is insufficient evidence from the literature. More microbiome-directed prospective studies studying the effect of current treatments on the microbiome with special attention for patient meta-data, sampling methods and analysis methods are needed to draw more substantial conclusions.