Prescribing errors are a source of preventable harm in healthcare, which may be mitigated using Electronic Prescribing (EP) systems. Anyone who routinely prescribes medication could benefit from digitally assisted automated checks to identify whether a prescription should potentially not be allowed (e.g. drug allergy). National Health Service (NHS) Trusts have implemented a range of EP systems; however, their performance has not hitherto been evaluated. We developed the web-based Electronic Prescribing Risk and Safety Evaluation (ePRaSE) tool, which comprises a bank of prescribing scenarios to evaluate the performance of EP systems. We solicited ePRaSE testing: 68 pharmacists from across 45 English NHS Trusts, utilising 13 different EP systems volunteered for the study. We found considerable variability in mitigation performance (systems correctly identifying risk of error when prescribing) across both NHS Trusts and EP systems. Moreover, we found that mitigation performance varied considerably across NHS Trusts using the same EP system, strongly suggesting there are opportunities to optimise performance within systems. The ePRaSE tool is effective in identifying variability in risk management between NHS Trusts and EP systems. Wider use of this tool may facilitate improvements in EP system configurations, thus minimising potential harm from prescribing errors.

BACKGROUND:Children born preterm face elevated risks of neurodevelopmental impairments across domains. Prior studies have relied on expert-imposed typologies within single domains. This study applies statistical learning to a national database to identify transdomain clusters and their maternal and neonatal predictors. METHODS:Latent class analysis (LCA) was used to derive transdomain clusters from parent-reported visual, auditory, neuromotor, and communication impairments in preterm-born children at two years corrected age using the UK National Neonatal Research Database data (N = 27,261). Replication was conducted in an independent sample from Wales (N = 975). Clusters were clinically validated using cerebral palsy diagnosis, Bayley Scales of Infant and Toddler Development (3rd edition), and global neurodevelopmental delay. Random forest identified cluster-specific and shared predictors. FINDINGS:Four homogeneous clusters were derived (silhouette score = 0.71) and replicated in Wales with high balanced accuracy (93%): (1) typically developing (84.8%), (2) communication impairments (8.4%), (3) neuro-motor impairments (4.1%), and (4) multiple neuro-morbidity (2.7%). Clusters had high clinical validity and were distinguishable by shared and cluster-specific predictors. Neonatal brain injuries were most predictive of neuro-motor and multiple neuro-morbidity clusters. Birthweight, gestational age, socio-economic deprivation, and sex were stronger predictors of the communication cluster than preterm co-morbidities. INTERPRETATION:This study provides first evidence of the transdomain nature of neurodevelopmental impairments after preterm birth using LCA. The finding that socio-demographic and perinatal factors rather than co-morbidities increase the risk of communication impairment highlights the importance of environmental modification alongside clinical interventions. Applying data-driven approaches to routinely collected data may offer a cost-effective way to stratify at-risk children and inform targeted support strategies. FUNDING:UKRI Medical Research Council.

Inferring longitudinal Physical Activity (PA) levels and sleep timings from wrist-worn sensors may facilitate personalized insights into day-to-day profile assessments and can be used to monitor a range of physical- and mental-health outcomes, including towards symptom monitoring and rehabilitation. We used the publicly available CAPTURE-24 dataset, comprising 148 participants with ~24-hour concurrent three-dimensional wrist-worn accelerometer data and minute-by-minute labels used as ground truth: sleep, sedentary, light, moderate-vigorous PA. First, we down-sampled the raw accelerometry data to 10 Hz to ensure the generalizability of our methodology across longitudinal studies which typically use similarly low sample rates for actigraphy. Subsequently, we computed four complementary acceleration summary measures and 10 additional smoothened outputs for each acceleration summary measure to derive 44 features characterizing minute-by-minute PA. These features were presented into different classifiers casted as a 4-class classification problem. We trained the model using the first 98 participants and assessed model performance and generalization on the remaining 50 participants. Using a random forest classifier, we demonstrated accurately estimating PA levels and sleep with 87% overall accuracy (F1-score=0.80) including 98.6% correct sleep detection. These findings processing the down-sampled actigraphy data to 10 Hz match or exceed state-of-art results recently reported in the literature achieved using considerably more sophisticated and time-consuming methods (including deep learning) which required actigraphy data sampled at 100 Hz. Collectively, these findings support the deployment of longitudinal, large-scale actigraphy data with sample rates as low as 10 Hz, towards accurately estimating personalized day-to-day PA and sleep profiles in healthcare community studies.

Primary care consultations provide an opportunity for patients and clinicians to assess asthma attack risk. Using a data-driven risk prediction tool with routinely collected health records may be an efficient way to aid promotion of effective self-management, and support clinical decision making. Longitudinal Scottish primary care data for 21,250 asthma patients were used to predict the risk of asthma attacks in the following year. A selection of machine learning algorithms (i.e., Naïve Bayes Classifier, Logistic Regression, Random Forests, and Extreme Gradient Boosting), hyperparameters, training data enrichment methods were explored, and validated in a random unseen data partition. Our final Logistic Regression model achieved the best performance when no training data enrichment was applied. Around 1 in 3 (36.2%) predicted high-risk patients had an attack within one year of consultation, compared to approximately 1 in 16 in the predicted low-risk group (6.7%). The model was well calibrated, with a calibration slope of 1.02 and an intercept of 0.004, and the Area under the Curve was 0.75. This model has the potential to increase the efficiency of routine asthma care by creating new personalized care pathways mapped to predicted risk of asthma attacks, such as priority ranking patients for scheduled consultations and interventions. Furthermore, it could be used to educate patients about their individual risk and risk factors, and promote healthier lifestyle changes, use of self-management plans, and early emergency care seeking following rapid symptom deterioration.

Introduction The role of female sex hormones and their influence on asthma’s development and natural history remain uncertain. Our study aims to enhance understanding of exogenous sex hormones’ role in asthma development and manifestation, considering phenotypic heterogeneity and focusing on metabolic syndrome-linked asthma that has shown increased severity in females.Methods and analysis A cohort study using primary care data from the Clinical Practice Research Datalink (CPRD) databases linked with additional data sources (Hospital Episode Statistics, ethnicity and deprivation) will include individuals aged 16–70 years, spanning 1 January 2005 to 31 December 2019. We will use appropriate statistical learning methods depending on the outcome: extended Cox regression for late-onset asthma; Poisson or negative binomial regression for asthma exacerbations; binary logistic regression for asthma control; and ordered logistic regression for asthma severity. Asthma exacerbation will be defined based on the American Thoracic Society/European Respiratory Society Task Force definition as the presence of either one of an asthma-related accident and emergency department visit, an asthma-related (unscheduled) hospital admission or an acute course of oral corticosteroids (OCS) with evidence of asthma-related medical event and/or review within 2 weeks of OCS prescription. Poor asthma control in any given month will be defined by the occurrence of an exacerbation episode or use of short-acting beta agonist. Asthma severity will be defined based on the British Thoracic Society asthma severity steps. Asthma phenotypes will be identified using k-means clustering. Analyses will be undertaken using both GOLD and Aurum to ensure coverage across UK nations.Ethics and dissemination CPRD has received ethics approval from the Health Research Authority (East Midlands—Derby, REC reference number 21/EM/065) to support research using anonymised data. Approval to conduct this study was obtained through CPRD’s Research Data Governance process. The results will be disseminated through academic publications and conference presentations, contributing to the understanding and practice of asthma management, particularly in the context of the impacts of exogenous sex steroid hormones.

Preterm birth and socioeconomic status (SES) are associated with brain development in early life, but the contribution of each over time is uncertain. We examined the effects of gestational age (GA) and SES on white matter microstructure in the neonatal period and at five years. Participants included preterm and term children. Diffusion MRI was collected at term-equivalent age (n = 153 preterm, n = 90 term [127/243 female]) and from a subset at five years (n = 26 preterm, n = 32 term [22/58 female]). We assessed linear associations of GA, SES (Scottish Index of Multiple Deprivation [SIMD] and maternal education), and GA×SES interactions on fractional anisotropy (FA) using tract-based spatial statistics. We compared the proportion of voxels with significant associations between timepoints. In preterm neonates, higher GA and higher maternal education, but not SIMD, were associated with higher FA (p corrected for family-wise error rate, pFWER < 0.05). GA-FA associations depended on maternal education and SIMD (β =|0.001-0.005|, p < 0.001). At five years, the strength and direction of GA-FA associations depended on SIMD (β =|0.013-0.028|, p < 0.001), but not maternal education. In term infants, lower SES was associated with higher FA at the neonatal timepoint only (pFWER < 0.05). Preterm birth and SES both shape brain development at birth and continue to do so at five years. The SES measure most strongly associated with FA in preterm infants switches from a family-level (i.e. maternal education) to neighborhood-level (i.e. SIMD) measure between birth and five years, which suggests strategies to mitigate adverse effects of social inequalities on development may require adaptation as children grow.

Endometriosis is a common, chronic condition associated with debilitating pain, fatigue, and heterogeneous symptom presentation. In this exploratory study, 68 participants with confirmed endometriosis were monitored for up to three 4-6-week smartwatch cycles. We collected daily self-reports of pain and fatigue as well as retrospective questionnaires assessing quality of life, and we extracted daily measures of physical activity (PA), sleep, and diurnal rhythms from wrist-worn actigraphy data. We found that daily PA was strongly negatively correlated with self-reported fatigue (repeated measures correlations R < - 0.3 ) and that participants with more severe or variable symptom trajectories displayed lower levels of PA, greater sleep disturbance, and more disrupted sleep and activity rhythms (Spearman's |R| > 0.3 ). Lastly, we found evidence of sleep and PA changes following surgery for endometriosis that reflected change in self-reported symptoms. Collectively, our findings suggest that passive data collection using wrist-worn wearables in endometriosis could facilitate individualized objective insights into symptom trajectories.

The planned withdrawal of life-sustaining treatment is a common practice in the intensive care unit for patients where ongoing organ support is recognised to be futile. Predicting the time to asystole following withdrawal of life-sustaining treatment is crucial for setting expectations, resource utilisation and identifying patients suitable for organ donation after circulatory death. This systematic review evaluates the literature for variables associated with, and predictive models for, time to asystole in patients managed on intensive care units. We conducted a comprehensive structured search of the MEDLINE and Embase databases. Studies evaluating patients managed on adult intensive care units undergoing withdrawal of life-sustaining treatment with recorded time to asystole were included. Data extraction and PROBAST quality assessment were performed and a narrative summary of the literature was provided. Twenty-three studies (7387 patients) met the inclusion criteria. Variables associated with imminent asystole (<60 min) included: deteriorating oxygenation; absence of corneal reflexes; absence of a cough reflex; blood pressure; use of vasopressors; and use of comfort medications. We identified a total of 20 unique predictive models using a wide range of variables and techniques. Many of these models also underwent secondary validation in further studies or were adapted to develop new models. This review identifies variables associated with time to asystole following withdrawal of life-sustaining treatment and summarises existing predictive models. Although several predictive models have been developed, their generalisability and performance varied. Further research and validation are needed to improve the accuracy and widespread adoption of predictive models for patients managed in intensive care units who may be eligible to donate organs following their diagnosis of death by circulatory criteria.

IntroductionMeasuring cortisol during military training offers insights into physiological responses to stress. We attempted precisely timed, cortisol awakening response (CAR) and pre-sleep cortisol (PSC), and diurnal slope (peak morning minus evening cortisol), during a British Army exercise. We aimed to understand cortisol dynamics and evaluate the feasibility of CAR and PSC in this environment.MethodSetting:high-intensity, 10-day infantry exercise.Participants:regular infantry soldiers exercising (EX, n=25) or headquarters-based (HQ, n=6). Participants undertook PSC and WAKE and WAKE+30 min samples after 1–2 days, 5–6 days and 9–10 days. Wrist-worn GENEActiv accelerometers were used to assess sleep duration in EX only. Samples taken ±15 min from prespecified time points were deemed adherent. Validated questionnaires were used to measure resilience and perceived stress. Cortisol and cortisone were measured simultaneously by liquid chromatography tandem mass spectrometry.ResultsFrom adherent participants’ samples, CAR was positive and tended to decrease as the exercise progressed. From all available data, HQ demonstrated greater diurnal slope than EX (F=7.68, p=0.02), reflecting higher morning cortisol (F=4.72, p=0.038) and lower PSC (p=0.04). No differences were seen in cortisol:cortisone ratio. 26.1% of CAR samples were adherent, with moderately strong associations between adherence and stress (r=0.41, p=0.009) but no association between adherence and day of exercise (χ2=0.27, p=0.8), sleep duration (r=–0.112, p=0.43) or resilience (r=–0.79, p=0.75). Test–retest reliability ratings for CAR were Cronbach’s α of 0.48, –11.7 and 0.34 for the beginning, middle and end of the exercise, respectively.ConclusionsWe observed a reduction in morning cortisol and decreased diurnal slope during a high-intensity military exercise, compared with the HQ comparator cohort in whom diurnal slope was preserved. A carefully timed CAR was not feasible in this setting.

Hypoglycaemia is one of the most common complications in diabetes, which can be life threatening if not managed appropriately. So far, research on hypoglycaemia prediction has been scarce, focusing on small cohorts linked to specific geographical regions, thus limiting the generalizability of the findings. In this paper, we developed and validated different machine learning models for next-day hypoglycaemia prediction in type 2 diabetes. We used a large international cohort comprising 669 participants, who had been regular users (for over a couple of years) of a mobile app for diabetes self-management and used common portable commercial devices for measuring their blood glucose and blood pressure levels, collecting in total 96121 observations (from which we extracted a balanced dataset of 2998 observations). Random Forests (RF), Support Vector Machines, Adaptive Boosting and Feed-Forward Artificial Neural Networks were employed to train predictive models based on 10-day temporal sequences with blood glucose and blood pressure measurements towards estimating next day hypoglycaemic episodes. We used a leave-one-subject-out (LOSO) approach for model validation, and found that RF achieved the best accuracy (0.814) and F1-score (0.812) with sensitivity (0.805) and specificity (0.824) for next-day hypoglycaemia prediction. The results of this study provide an expedient and reliable app-based approach to accurately predict hypoglycaemia in day-to-day life, thereby facilitating patient and care provider awareness and potentially preventing other serious complications.

Introduction Preterm birth (PTB) is strongly associated with encephalopathy of prematurity (EoP) and neurocognitive impairment. The biological axes linking PTB with atypical brain development are uncertain. We aim to elucidate the roles of neuroendocrine stress activation and immune dysregulation in linking PTB with EoP. Methods and analysis PRENCOG (PREterm birth as a determinant of Neurodevelopment and COGnition in children: mechanisms and causal evidence) is an exposure-based cohort study at the University of Edinburgh. Three hundred mother–infant dyads comprising 200 preterm births (gestational age, GA <32 weeks, exposed) and 100 term births (GA >37 weeks, non-exposed), will be recruited between January 2023 and December 2027. We will collect parental and infant medical, demographic, socioeconomic characteristics and biological data which include placental tissue, umbilical cord blood, maternal and infant hair, infant saliva, infant dried blood spots, faecal material, and structural and diffusion MRI. Infant biosamples will be collected between birth and 44 weeks GA. EoP will be characterised by MRI using morphometric similarity networks (MSNs), hierarchical complexity (HC) and magnetisation transfer saturation imaging (MTsat). We will conduct: first, multivariable regressions and statistical association assessments to test how PTB-associated risk factors (PTB-RFs) relate to MSNs, HC and or MTsat; second, structural equation modelling to investigate neuroendocrine stress activation and immune dysregulation as mediators of PTB-RFs on features of EoP. PTB-RF selection will be informed by the variables that predict real-world educational outcomes, ascertained by linking the UK National Neonatal Research Database with the National Pupil Database. Ethics and dissemination A favourable ethical opinion has been given by the South East Scotland Research Ethics Committee 02 (23/SS/0067) and NHS Lothian Research and Development (2023/0150). Results will be reported to the Medical Research Council, in scientific media, via stakeholder partners and on a website in accessible language ( https://www.ed.ac.uk/centre-reproductive-health/prencog ).

Longitudinal patient registries generate important evidence for advancing clinical care and the regulatory evaluation of health-care products. Most national registries rely on data collected as part of routine clinical encounters, an approach that does not capture real-world, patient-centred outcomes, such as physical activity, fatigue, ability to do daily tasks, and other indicators of quality of life. Digital health technologies that obtain such real-world data could greatly enhance patient registries but unresolved challenges have so far prevented their broad adoption. Based on our experience implementing digital health technologies in registries and observational studies, we propose potential solutions to three practical challenges we have repeatedly encountered: determining what to measure digitally, selecting the appropriate device, and ensuring representativeness and engagement over time. We describe the example of a hypothetical patient registry for valvular heart disease, a condition for which there is substantial variation in treatment selection and postintervention outcomes, and for which patient-centred outcome data are urgently needed to inform clinical care guidelines and health-service commissioning.

The Timed-Up and Go test is a simple yet effective test used to evaluate balance and mobility in conditions that affect movement, such as Parkinson's disease. This test can inform clinicians about the monitoring and progression of the disease by measuring the time taken to complete the test. We used a smartphone app to obtain the phone's inertial data and implemented an algorithm to automatically extract the time taken to complete the test. We considered data collected from six healthy participants performing tests at different speeds. The proposed method was further tested on twelve participants with Parkinson's disease based on a reference measurement in clinic. We show that, for both groups, we obtain good accuracy (RMSE = 3.42 and 1.95 s) and a strong positive correlation (r = 0.85 and 0.83) between estimated duration and ground truth. We highlight limitations in our approach when the test is performed at very low speed or without a clear pause between the test and the user interaction with the phone.

The quantification of emotional states is an important step to understanding wellbeing. Time series data from multiple modalities such as physiological and motion sensor data have proven to be integral for measuring and quantifying emotions. Monitoring emotional trajectories over long periods of time inherits some critical limitations in relation to the size of the training data. This shortcoming may hinder the development of reliable and accurate machine learning models. To address this problem, this article proposes a framework to tackle the limitation in performing emotional state recognition: (1) encoding time series data into coloured images; (2) leveraging pre-trained object recognition models to apply a Transfer Learning (TL) approach using the images from step 1; (3) utilising a 1D Convolutional Neural Network (CNN) to perform emotion classification from physiological data; (4) concatenating the pre-trained TL model with the 1D CNN. We demonstrate that model performance when inferring real-world wellbeing rated on a 5-point Likert scale can be enhanced using our framework, resulting in up to 98.5% accuracy, outperforming a conventional CNN by 4.5%. Subject-independent models using the same approach resulted in an average of 72.3% accuracy (SD 0.038). The proposed methodology helps improve performance and overcome problems with small training datasets.

Hypokinetic Dysarthria (HD) is a hampering speech symptom appearing as a consequence of Parkinson’s Disease (PD). HD has been traditionally evaluated using diadochokinetic tests such as the fast repetition of monosyllables as [pa], [ta], and [ka] and multisyllable sequences [pataka] and [pakata] towards assessing speech performance. However, the practical validity of these tests in assessing the speech of a person with PD (PwP) to assess performance degradation and infer PD-related symptoms has not been thoroughly investigated. The aim of the present work is to explore the performance of tests consisting in a monosyllabic repetition [...tatata...] vs a multisyllable one [...pataka...]). The methodology proposed is based on estimating distributions of syllable and inter-syllable interval durations obtained from diadichokinetic tests using Kolmogorov-Smirnov Approximations (KSA), and comparing the resulting distributions by means of Jensen-Shannon Divergence (JSD) to assess the efficiency of both types of tests confronting utterances from Healthy Controls (HC) with the ones from PD participants. The results from the evaluation of 30 gender-balanced participants, 18 PwP and 12 HC, show that the monosyllable test does not appear to differentiate well between the two cohorts, whereas the multisyllable test shows better performance. Although the relatively small sample size suggests findings should be cautiously interpreted, they tentatively underline the need to use the most adequate tests to assess HD diadochokinetic performance.

Importance Preterm birth and socioeconomic status (SES) are associated with brain structure in childhood, but the relative contributions of each during the neonatal period are unknown. Objective To investigate associations of birth gestational age (GA) and SES with neonatal brain morphology by testing 3 hypotheses: GA and SES are associated with brain morphology; associations between SES and brain morphology vary with GA; and associations between SES and brain structure and morphology depend on how SES is operationalized. Design, Setting, and Participants This cohort study recruited participants from November 2016 to September 2021 at a single center in the United Kingdom. Participants were 170 extremely and very preterm infants and 91 full-term or near-term infants. Exclusion criteria were major congenital malformation, chromosomal abnormality, congenital infection, cystic periventricular leukomalacia, hemorrhagic parenchymal infarction, and posthemorrhagic ventricular dilatation. Exposures Birth GA and SES, operationalized at the neighborhood level (using the Scottish Index of Multiple Deprivation), the family level (using parental education and occupation), and subjectively (World Health Organization Quality of Life measure). Main Outcomes and Measures Brain volume (85 parcels) and 5 whole-brain cortical morphology measures (gyrification index, thickness, sulcal depth, curvature, surface area) at term-equivalent age (median [range] age, 40 weeks, 5 days [36 weeks, 2 days to 45 weeks, 6 days] and 42 weeks [38 weeks, 2 days to 46 weeks, 1 day] for preterm and full-term infants, respectively). Results Participants were 170 extremely and very preterm infants (95 [55.9%] male; 4 of 166 [2.4%] Asian, 145 of 166 [87.3%] White) and 91 full-term or near-term infants (50 [54.9%] male; 3 of 86 [3.5%] Asian, 78 of 86 [90.7%] White infants) with median (range) birth GAs of 30 weeks, 0 days (22 weeks, 1 day, to 32 weeks, 6 days) and 39 weeks, 4 days (36 weeks, 3 days, to 42 weeks, 1 day), respectively. In fully adjusted models, birth GA was associated with a higher proportion of brain volumes (27 of 85 parcels [31.8%]; β range, −0.20 to 0.24) than neighborhood-level SES (1 of 85 parcels [1.2%]; β = 0.17 [95% CI, −0.16 to 0.50]) or family-level SES (maternal education: 4 of 85 parcels [4.7%]; β range, 0.09 to 0.15; maternal occupation: 1 of 85 parcels [1.2%]; β = 0.06 [95% CI, 0.02 to 0.11] respectively). There were interactions between GA and both family-level and subjective SES measures on regional brain volumes. Birth GA was associated with cortical surface area (β = 0.10 [95% CI, 0.02 to 0.18]) and gyrification index (β = 0.16 [95% CI, 0.07 to 0.25]); no SES measure was associated with cortical measures. Conclusions and Relevance In this cohort study of UK infants, birth GA and SES were associated with neonatal brain morphology, but low GA had more widely distributed associations with neonatal brain structure than SES. Further work is warranted to elucidate the mechanisms underlying the association of both GA and SES with early brain development.

Background: Digital biomarkers continue to make headway in the clinic and clinical trials for neurological conditions. Speech is a domain with great promise. Objectives: Using Friedreich ataxia (FRDA) as an exemplar population, we aimed to align objective measures of speech with markers of disease severity, speech related quality of life and subjective judgements of speech using supervised machine learning techniques. Methods: 132 participants with genetically confirmed diagnosis of FRDA were assessed using digital speech tests, disease severity scores (Friedreich Ataxia Rating Scale, FARS) and speech related quality of life ratings over a 10-year period. Speech was analyzed perceptually by expert listeners for intelligibility (ability to be understood) and naturalness (deviance from healthy norm) and acoustically across 344 features. Features were selected and presented into a random forest and a support vector machine classifier in a standard supervised learning setup designed to replicate expert-produced scores. Results: We demonstrate a subset of measures are strongly associated with all four clinical scales. Objective speech data replicated expert assessments of naturalness and intelligibility. These scores represent a lower level of variability than observed between subjective listener ratings. Findings provide evidence there are specific objective markers of speech that change over time and reflect clinical aspects of the disease. Discussion: The use of a large dataset yielded a speech assay capable of accurately approximating expert listener ratings of key clinical aspects of dysarthria severity. Distinct but complementary subsets align with disease severity and speech related quality of life.

Background Medication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case. Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study. Methods Over 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared. Results Estimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures. Conclusions We evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human -level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

Endometriosis is a common chronic pain condition with no known cure and limited treatment options. Digital technologies, ranging from smartphone apps to wearable sensors, have shown potential toward facilitating chronic pain assessment and management; however, to date, many of these tools have not been specifically deployed or evaluated in patients with endometriosis-associated pain. Informed by previous studies in related chronic pain conditions, we discuss how digital technologies may be used in endometriosis to facilitate objective, continuous, and holistic symptom tracking. We postulate that these pervasive and increasingly affordable technologies present promising opportunities toward developing decision-support tools assisting healthcare professionals and empowering patients with endometriosis to make better-informed choices about symptom management.