Digital biomarkers and sex impacts in Alzheimer’s disease management — potential utility for innovative 3P medicine approach

Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer’s, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida’s digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida’s application, achieved a 75% ROC-AUC (receiver operating characteristic — area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline.RLH and IT were supported by Altoida Inc. JM was supported in this work by the Charles University Grant Agency (GA UK) project no. 436119 at Charles University, Second Faculty of Medicine, Prague, Czech Republic.Peer Reviewed"Article signat per 16 autors/es: Robbert L. Harms, Alberto Ferrari, Irene B. Meier, Julie Martinkova, Enrico Santus, Nicola Marino, Davide Cirillo, Simona Mellino, Silvina Catuara Solarz, Ioannis Tarnanas, Cassandra Szoeke, Jakub Hort, Alfonso Valencia, Maria Teresa Ferretti, Azizi Seixas & Antonella Santuccione Chadha "Postprint (published version

Characterization of Postoperative Recovery After Cardiac Surgery- Insights into Predicting Individualized Recovery Pattern

Understanding the patterns of postoperative recovery after cardiac surgery is important from several perspectives: to facilitate patient-centered treatment decision making, to inform health care policy targeted to improve postoperative recovery, and to guide patient care after cardiac surgery. Our works aimed to address the following: 1) to summarize existing approaches to measuring and reporting postoperative recovery after cardiac surgery, 2) to develop a framework to efficiently measure patient-reported outcome measures to understand longitudinal recovery process, and 3) to explore ways to summarize the longitudinal recovery data in an actionable way, and 4) to evaluate whether addition of patient information generated through different phases of care would improve the ability to predict patient’s outcome. We first conducted a systematic review of the studies reporting on postoperative recovery after cardiac surgery using patient-reported outcome measures. Our systematic review demonstrated that the current approaches to measuring and reporting recovery as a treatment outcome varied widely across studies. This made synthesis of collective knowledge challenging and highlighted key gaps in knowledge, which we sought to address in our prospective cohort study. We conducted a prospective single-center cohort study of patients after cardiac surgery to measure their recovery trajectory across multiple domains of recovery. Using a digital platform, we measured patient recovery in various domains over 30 days after surgery to visualize a granular evolution of patient recovery after cardiac surgery. We used a latent class analysis to facilitate identification of dominant trajectory patterns that had been obscured in a conventional way of reporting such time-series data using group-level means. For the pain domain, we identified 4 trajectory classes, one of which was a group of patients with persistently high pain trajectory that only became distinguishable from less concerning group after 10 days. Therefore, we obtained a potentially actionable insights to tailoring individualized follow-up timing after surgery to improve the pain control. The prospective study embodied several important features to successfully conducting such studies of patient-reported outcomes. This included the use of digital platform to facilitate efficient data collection extending after hospital discharge, iteratively improving the protocol to optimize patient engagement including evaluation of potential barriers to survey completion, and using latent class analysis to identify dominant patterns of recovery trajectories. We outlined these insights in the protocol manuscript to inform subsequent studies aiming to leverage such a digital platform to measure longitudinal patient-centered outcome. Finally, we evaluated the potential value of incorporating health care data generated in the different phases of patient care in improving the prediction of postoperative outcomes after cardiac surgery. The current standard of risk prediction in cardiac surgery is the Society of Thoracic Surgeons’ (STS) risk model, which only uses patient information available preoperatively. We demonstrated through prediction models fitted on the national STS risk model for coronary artery bypass graft surgery that the addition of intraoperative variables to the conventional preoperative variable set improved the performance of prediction models substantially. Using machine learning approach to such a high-dimensional dataset proved to be marginally important. This work demonstrated the potential value and importance of being able to leverage health care data to continuously update the prediction to inform patient outcomes and guide clinical care. Our work collectively advanced knowledge in several key aspects of postoperative recovery. First, we highlighted the knowledge gap in the existing literature through characterizing the variability in the ways such studies had been conducted. Second, we designed and described a framework to measure postoperative recovery and an analytical approach to informatively characterize longitudinal patient recovery. Third, we employed these designs in a prospective cohort study to measure and analyze recovery trajectories and described clinical insights obtained from the study. Finally, we demonstrated the potential value of a dynamic risk model to iteratively improve its predictive performance by incorporating new data generated as the patient progresses through the phase of care. Such a platform has the potential to individualize patient’s post-acute care in a data-driven manner

Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression

Advancements in machine learning algorithms have had a beneficial impact on representation learning, classification, and prediction models built using electronic health record (EHR) data. Effort has been put both on increasing models' overall performance as well as improving their interpretability, particularly regarding the decision-making process. In this study, we present a temporal deep learning model to perform bidirectional representation learning on EHR sequences with a transformer architecture to predict future diagnosis of depression. This model is able to aggregate five heterogenous and high-dimensional data sources from the EHR and process them in a temporal manner for chronic disease prediction at various prediction windows. We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence. The model generated the highest increases of precision-recall area under the curve (PRAUC) from 0.70 to 0.76 in depression prediction compared to the best baseline model. Furthermore, the self-attention weights in each sequence quantitatively demonstrated the inner relationship between various codes, which improved the model's interpretability. These results demonstrate the model's ability to utilize heterogeneous EHR data to predict depression while achieving high accuracy and interpretability, which may facilitate constructing clinical decision support systems in the future for chronic disease screening and early detection.Comment: in IEEE Journal of Biomedical and Health Informatics (2021

Pragmatic Evaluation of Health Monitoring & Analysis Models from an Empirical Perspective

Implementing and deploying several linked modules that can conduct real-time analysis and recommendation of patient datasets is necessary for designing health monitoring and analysis models. These databases include, but are not limited to, blood test results, computer tomography (CT) scans, MRI scans, PET scans, and other imaging tests. A combination of signal processing and image processing methods are used to process them. These methods include data collection, pre-processing, feature extraction and selection, classification, and context-specific post-processing. Researchers have put forward a variety of machine learning (ML) and deep learning (DL) techniques to carry out these tasks, which help with the high-accuracy categorization of these datasets. However, the internal operational features and the quantitative and qualitative performance indicators of each of these models differ. These models also demonstrate various functional subtleties, contextual benefits, application-specific constraints, and deployment-specific future research directions. It is difficult for researchers to pinpoint models that perform well for their application-specific use cases because of the vast range of performance. In order to reduce this uncertainty, this paper discusses a review of several Health Monitoring & Analysis Models in terms of their internal operational features & performance measurements. Readers will be able to recognise models that are appropriate for their application-specific use cases based on this discussion. When compared to other models, it was shown that Convolutional Neural Networks (CNNs), Masked Region CNN (MRCNN), Recurrent NN (RNN), Q-Learning, and Reinforcement learning models had greater analytical performance. They are hence suitable for clinical use cases. These models' worse scaling performance is a result of their increased complexity and higher implementation costs. This paper compares evaluated models in terms of accuracy, computational latency, deployment complexity, scalability, and deployment cost metrics to analyse such scenarios. This comparison will help users choose the best models for their performance-specific use cases. In this article, a new Health Monitoring Metric (HMM), which integrates many performance indicators to identify the best-performing models under various real-time patient settings, is reviewed to make the process of model selection even easier for real-time scenarios

Use of wearable technology in the treatment of mental health difficulties and chronic pain

This portfolio thesis has three parts: a Systematic Literature Review followed by an empirical research study and finally, a set of appendices. This thesis explored the potential uses of wearable technology in the treatment of mental health difficulties and in the management of chronic pain.The systematic literature review explored recent mental health care research that utilised wearable technology. From this body of literature, three potential areas of use for wearable technology in mental health care were synthesised. Consideration is given to the clinical implications and limitations of the research. Potential areas of future research are also discussed.The empirical section describes a study aimed to investigate the utility of incorporating activity tracking technology into a pain management programme (PMP). This pilot study employed mixed methodology in order to investigate both whether being provided with an activity tracker is beneficial to individuals experiencing chronic pain. No significant improvements on outcome measures for various domains (including sleep, pain levels and mental wellbeing) were found for a group with trackers compared to a group without. However those in the tracker group provided positive feedback about benefits of the trackers. The implications for the findings of this study and avenues for future research are discussed.The appendices section contains a set of appendices for both the preceding sections. It also contains an epistemological statement and reflective statement in order to provide context about the researcher’s experience of conducting the research and the philosophical position from which the design of the research was approached

Seizure Detection, Seizure Prediction, and Closed-Loop Warning Systems in Epilepsy

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy

The role of electrocardiography in occupational medicine, from einthoven’s invention to the digital era of wearable devices

Clinical-instrumental investigations, such as electrocardiography (ECG), represent a corollary of a procedures that, nowadays, is called upon as part of the principles of precision medicine. However when carrying out the professional routine examinations, most tend to ignore how a “simple” instrument can offer indispensable support in clinical practice, even in occupational medicine. The advent of the digital age, made of silicon and printed circuit boards, has allowed the miniaturization of the electronic components of these electro-medical devices. Finally, the adoption of patient wearables in medicine has been rapidly expanding worldwide for a number of years. This has been driven mainly by consumers’ demand to monitor their own health. With the ongoing research and development of new features capable of assessing and transmitting real-time biometric data, the impact of wearables on cardiovascular management has become inevitable. Despite the potential offered by this technology, as evident from the scientific literature, the application of these devices in the field of health and safety in the workplace is still limited. This may also be due to the lack of targeted scientific research. While offering great potential, it is very important to consider and evaluate ethical aspects related to the use of these smart devices, such as the management of the collected data relating to the physiological parameters and the location of the worker. This technology is to be considered as being aimed at monitoring the subject’s physiological parameters, and not at the diagnosis of any pathological condition, which should always be on charge of the medical specialist We conducted a review of the evolution of the role that electrophysiology plays as part of occupational health and safety management and on its possible future use, thanks to ongoing technological innovation

Reporting health data in waiting rooms with mobile technology: Patient expectation and confirmation

Objectives: Hospitals and medical staff use digital devices such as mobile phones and tablets to treat patients. Prior research has examined patient-reported outcomes, and the use of medical devices to do diagnosis and prognosis of patients, but not whether patients like using, and intend to use in future, mobile devices to self-report medical data. We address this research gap by developing a theoretical model based on the expectancy confirmation model (ECM) and testing it in an empirical study of patients using mobile technology to self-report data. Design: This study adopts a non-interventional cross-sectional research design. Randomly-selected patients provided data via survey and physical measurements. The target population comprises adults visiting a healthcare laboratory to get their blood drawn. Materials and methods: We surveyed 190 randomly-selected patients waiting for treatment in the clinic. They were surveyed at two points in time - before and after their blood was drawn - on their demographic characteristics, research variables concerning their use of mobile devices to provide medical information, and perceived clinical data (blood pressure, height and weight). The research model was tested using structural equation modeling. Results: The study found strong support for the research model, with seven of eight hypotheses being supported. Both self-disclosure effort and feedback expectation positively affect both perceived feedback quality and confirmation. Contrary to expectations, perceived feedback quality was not found to affect confirmation. Perceived feedback quality, along with confirmation, was found to positively affect satisfaction, which was found to affect intention to disclose medical data through mobile technology. Conclusions: The study\u27s findings support the proposed path from feedback expectation and self-disclosure effort to confirmation to satisfaction to disclosure intention. Although perceived feedback does not affect confirmation, it affects satisfaction. Overall, we believe the results provide novel insights to both scientific research community and practitioners about using mobile technologies for self-reporting medical data

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individuals’ physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nine-item Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the k-nearest neighbor and artificial neural network. This method of identifying depression is a cost-effective solution for long-term use and can monitor individuals for depression without invading their personal space or creating other day-to-day disturbances