The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.

BACKGROUND: Electronic health records (EHRs) contain patients' health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data. Although ML has become well developed, studies mainly focus on engineering but lack medical outcomes. OBJECTIVE: This study aims for a scoping review of the evidence on how the use of ML on longitudinal EHRs can support the early detection and prevention of disease. The medical insights and clinical benefits that have been generated were investigated by reviewing applications in a variety of diseases. METHODS: This study was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A literature search was performed in 2022 in collaboration with a medical information specialist in the following databases: PubMed, Embase, Web of Science Core Collection (Clarivate Analytics), and IEEE Xplore Digital Library and computer science bibliography. Studies were eligible when longitudinal EHRs were used that aimed for the early detection of disease via ML in a prevention context. Studies with a technical focus or using imaging or hospital admission data were beyond the scope of this review. Study screening and selection and data extraction were performed independently by 2 researchers. RESULTS: In total, 20 studies were included, mainly published between 2018 and 2022. They showed that a variety of diseases could be detected or predicted, particularly diabetes; kidney diseases; diseases of the circulatory system; and mental, behavioral, and neurodevelopmental disorders. Demographics, symptoms, procedures, laboratory test results, diagnoses, medications, and BMI were frequently used EHR data in basic recurrent neural network or long short-term memory techniques. By developing and comparing ML and DL models, medical insights such as a high diagnostic performance, an earlier detection, the most important predictors, and additional health indicators were obtained. A clinical benefit that has been evaluated positively was preliminary screening. If these models are applied in practice, patients might also benefit from personalized health care and prevention, with practical benefits such as workload reduction and policy insights. CONCLUSIONS: Longitudinal EHRs proved to be helpful for support in health care. Current ML models on EHRs can support the detection of diseases in terms of accuracy and offer preliminary screening benefits. Regarding the prevention of diseases, ML and specifically DL models can accurately predict or detect diseases earlier than current clinical diagnoses. Adding personally responsible factors allows targeted prevention interventions. While ML models based on textual EHRs are still in the developmental stage, they have high potential to support clinicians and the health care system and improve patient outcomes.

Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review.

BACKGROUND: There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health. OBJECTIVE: This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care. METHODS: We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review. RESULTS: We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients. CONCLUSIONS: This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.

Toward personalized cognitive diagnostics of at-genetic-risk Alzheimer's disease.

Spatial navigation is emerging as a critical factor in identifying preclinical Alzheimer's disease (AD). However, the impact of interindividual navigation ability and demographic risk factors (e.g., APOE, age, and sex) on spatial navigation make it difficult to identify persons "at high risk" of AD in the preclinical stages. In the current study, we use spatial navigation big data (n = 27,108) from the Sea Hero Quest (SHQ) game to overcome these challenges by investigating whether big data can be used to benchmark a highly phenotyped healthy aging laboratory cohort into high- vs. low-risk persons based on their genetic (APOE) and demographic (sex, age, and educational attainment) risk factors. Our results replicate previous findings in APOE ε4 carriers, indicative of grid cell coding errors in the entorhinal cortex, the initial brain region affected by AD pathophysiology. We also show that although baseline navigation ability differs between men and women, sex does not interact with the APOE genotype to influence the manifestation of AD-related spatial disturbance. Most importantly, we demonstrate that such high-risk preclinical cases can be reliably distinguished from low-risk participants using big-data spatial navigation benchmarks. By contrast, participants were undistinguishable on neuropsychological episodic memory tests. Taken together, we present evidence to suggest that, in the future, SHQ normative benchmark data can be used to more accurately classify spatial impairments in at-high-risk of AD healthy participants at a more individual level, therefore providing the steppingstone for individualized diagnostics and outcome measures of cognitive symptoms in preclinical AD.

Ethical Issues of Digital Twins for Personalized Health Care Service: Preliminary Mapping Study.

BACKGROUND: The concept of digital twins has great potential for transforming the existing health care system by making it more personalized. As a convergence of health care, artificial intelligence, and information and communication technologies, personalized health care services that are developed under the concept of digital twins raise a myriad of ethical issues. Although some of the ethical issues are known to researchers working on digital health and personalized medicine, currently, there is no comprehensive review that maps the major ethical risks of digital twins for personalized health care services. OBJECTIVE: This study aims to fill the research gap by identifying the major ethical risks of digital twins for personalized health care services. We first propose a working definition for digital twins for personalized health care services to facilitate future discussions on the ethical issues related to these emerging digital health services. We then develop a process-oriented ethical map to identify the major ethical risks in each of the different data processing phases. METHODS: We resorted to the literature on eHealth, personalized medicine, precision medicine, and information engineering to identify potential issues and developed a process-oriented ethical map to structure the inquiry in a more systematic way. The ethical map allows us to see how each of the major ethical concerns emerges during the process of transforming raw data into valuable information. Developers of a digital twin for personalized health care service may use this map to identify ethical risks during the development stage in a more systematic way and can proactively address them. RESULTS: This paper provides a working definition of digital twins for personalized health care services by identifying 3 features that distinguish the new application from other eHealth services. On the basis of the working definition, this paper further layouts 10 major operational problems and the corresponding ethical risks. CONCLUSIONS: It is challenging to address all the major ethical risks that a digital twin for a personalized health care service might encounter proactively without a conceptual map at hand. The process-oriented ethical map we propose here can assist the developers of digital twins for personalized health care services in analyzing ethical risks in a more systematic manner.

Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data.

Dynamic treatment regimes (DTRs) include a sequence of treatment decision rules, in which treatment is adapted over time in response to the changes in an individual's disease progression and health care history. In medical practice, nested test-and-treat strategies are common to improve cost-effectiveness. For example, for patients at risk of prostate cancer, only patients who have high prostate-specific antigen (PSA) need a biopsy, which is costly and invasive, to confirm the diagnosis and help determine the treatment if needed. A decision about treatment happens after the biopsy, and is thus nested within the decision of whether to do the test. However, current existing statistical methods are not able to accommodate such a naturally embedded property of the treatment decision within the test decision. Therefore, we developed a new statistical learning method, step-adjusted tree-based reinforcement learning, to evaluate DTRs within such a nested multistage dynamic decision framework using observational data. At each step within each stage, we combined the robust semiparametric estimation via augmented inverse probability weighting with a tree-based reinforcement learning method to deal with the counterfactual optimization. The simulation studies demonstrated robust performance of the proposed methods under different scenarios. We further applied our method to evaluate the necessity of prostate biopsy and identify the optimal test-and-treat regimes for prostate cancer patients using data from the Johns Hopkins University prostate cancer active surveillance dataset.

The development of CHAMP: a checklist for the appraisal of moderators and predictors.

BACKGROUND: Personalized healthcare relies on the identification of factors explaining why individuals respond differently to the same intervention. Analyses identifying such factors, so called predictors and moderators, have their own set of assumptions and limitations which, when violated, can result in misleading claims, and incorrect actions. The aim of this study was to develop a checklist for critically appraising the results of predictor and moderator analyses by combining recommendations from published guidelines and experts in the field. METHODS: Candidate criteria for the checklist were retrieved through systematic searches of the literature. These criteria were evaluated for appropriateness using a Delphi procedure. Two Delphi rounds yielded a pilot checklist, which was tested on a set of papers included in a systematic review on reinforced home-based palliative care. The results of the pilot informed a third Delphi round, which served to finalize the checklist. RESULTS: Forty-nine appraisal criteria were identified in the literature. Feedback was obtained from fourteen experts from (bio)statistics, epidemiology and other associated fields elicited via three Delphi rounds. Additional feedback from other researchers was collected in a pilot test. The final version of our checklist included seventeen criteria, covering the design (e.g. a priori plausibility), analysis (e.g. use of interaction tests) and results (e.g. complete reporting) of moderator and predictor analysis, together with the transferability of the results (e.g. clinical importance). There are criteria both for individual papers and for bodies of evidence. CONCLUSIONS: The proposed checklist can be used for critical appraisal of reported moderator and predictor effects, as assessed in randomized or non-randomized studies using individual participant or aggregate data. This checklist is accompanied by a user's guide to facilitate implementation. Its future use across a wide variety of research domains and study types will provide insights about its usability and feasibility.

Osteoarthritis--a case for personalized health care?

For both economic and ethical reasons, identification of the optimal treatment for each individual patient is a pressing concern, not only for the patients and their physician, but also health care payers and the pharmaceutical industry. In the field of osteoarthritis (OA) this is of particular relevance, due to the heterogeneity of the disease and the very large number of affected individuals. There is a need to pair the right patients with the right therapeutic modes of action. At present, the clinical trial failures in OA may be a consequence of both bona fide treatment failures and trial failures due to clinical design deficiencies. Tools are needed for characterization and segregation of patients with OA. Key lessons may be learned from advances with another form of arthritis, namely rheumatoid arthritis (RA). Personalized health care (PHC) may be more advantageous for a number of specific indications which are characterized by costly therapy, low response rates and significant problems associated with trial and error prescription, including the risk of serious side effects. We discuss the use of diagnostic practices guiding RA treatment, which may serve as a source of key insights for diagnostic practices in OA. We discuss the emerging concept of PHC, and outline the opportunities and current successes and failures across the RA field, as the OA field collects further data to support the hypothesis. We attempt to outline a possible path forward to assist patients, physicians, payers and the pharmaceutical industry in assuring the 'right' patients are treated with the 'right drug' in OA. Finally we highlight methods for possible segregation of OA patients that would allow identification of patient subtypes, such as OA driven by inflammation that may be ideally suited for PHC and for targeted therapies.

Pharmacogenomics in the nursing literature: an integrative review.

BACKGROUND: Pharmacogenomics is a rapidly growing component of personalized health care, and nurses must be competent to deliver genomic-focused nursing care. METHODS: We conducted an integrative review of pharmacogenomics in the nursing literature. A comprehensive search of the nursing literature was conducted using the key words pharmacogenomics and pharmacogenetics. A total of 47 unique articles were included. RESULTS: Articles represented mainly narrative reviews, with limited discussions of the implications for nursing practice, education, or research. As such, they provide limited direction for advancing either clinical practice or scientific inquiry. CONCLUSIONS: This review serves as a call to action for more systematic and empirical publications addressing pharmacogenomics in nursing practice, education, and research. Nurses must be involved in and contribute to interdisciplinary conversations and burgeoning clinical practice initiatives related to pharmacogenomics.

Switching from Galenic to Advanced Dressings or Vacuum Assisted Closure Therapy Can Improve Quality of Life of Patients with Chronic Non-Responsive Pressure Skin Ulcers: Preliminary Data with Italian Translation of WOUND-Q.

Objective: A few studies have focused on the quality of life (QoL) of patients with chronic non-responsive pressure skin ulcers. The aim of this study was to assess how correct treatment (advanced wound care [AWC] dressings alone or vacuum assisted closure [VAC] therapy alone) changes the QoL of these patients. Approach: One hundred six patients with chronic non-responsive pressure skin ulcers, who had previously used galenic dressings, applied without proper therapeutic indication, were included in this study. We administered the WOUND-Q, at time 0 and after 1 month of appropriate therapy, to assess patient-reported outcome measures. Group 1 consisted of 30 patients treated with advanced dressings, Group 2: 22 patients treated with VAC therapy, and Group 3: 30 patients continuing conventional galenic dressings (Control group). Statistical analysis allowed us to analyze QoL changes over time and to compare WOUND-Q Group 1 and 2 deltas with those of Group 3. The study followed the STROBE statement. Results and Innovation: In all the scales evaluated (Assessment, Drainage, Smell, Life impact, Psychological, Social, Sleep and Dressing), there were significant improvements in mean values for Groups 1 and 2. Kruskal-Wallis tests with Dunn's multiple-comparisons tests and Brown-Forsythe and Welch Analysis of Variance tests demonstrated significant differences between deltas of Group 1 and Group 2 compared with those of Group 3 for most scales analyzed. Conclusions: Administration of the WOUND-Q demonstrated that the application of advanced dressings alone or VAC therapy alone positively affects the QoL of patients with chronic nonresponsive pressure wounds, in comparison with galenic dressings alone. The WOUND-Q has been shown to be a valid tool in studying changes in QoL of these patients.

Enhancing health care through medical cognitive virtual agents.

Objective: The modern era of cognitive intelligence in clinical space has led to the rise of 'Medical Cognitive Virtual Agents' (MCVAs) which are labeled as intelligent virtual assistants interacting with users in a context-sensitive and ambient manner. They aim to augment users' cognitive capabilities thereby helping both patients and medical experts in providing personalized healthcare like remote health tracking, emergency healthcare and robotic diagnosis of critical illness, among others. The objective of this study is to explore the technical aspects of MCVA and their relevance in modern healthcare. Methods: In this study, a comprehensive and interpretable analysis of MCVAs are presented and their impacts are discussed. A novel system framework prototype based on artificial intelligence for MCVA is presented. Architectural workflow of potential applications of functionalities of MCVAs are detailed. A novel MCVA relevance survey analysis was undertaken during March-April 2023 at Bhubaneswar, Odisha, India to understand the current position of MCVA in society. Results: Outcome of the survey delivered constructive results. Majority of people associated with healthcare showed their inclination towards MCVA. The curiosity for MCVA in Urban zone was more than in rural areas. Also, elderly citizens preferred using MCVA more as compared to youths. Medical decision support emerged as the most preferred application of MCVA. Conclusion: The article established and validated the relevance of MCVA in modern healthcare. The study showed that MCVA is likely to grow in future and can prove to be an effective assistance to medical experts in coming days.

Towards personalized and optimized fitting of cochlear implants.

A cochlear implant (CI) is a neurotechnological device that restores total sensorineural hearing loss. It contains a sophisticated speech processor that analyzes and transforms the acoustic input. It distributes its time-enveloped spectral content to the auditory nerve as electrical pulsed stimulation trains of selected frequency channels on a multi-contact electrode that is surgically inserted in the cochlear duct. This remarkable brain interface enables the deaf to regain hearing and understand speech. However, tuning of the large (>50) number of parameters of the speech processor, so-called "device fitting," is a tedious and complex process, which is mainly carried out in the clinic through 'one-size-fits-all' procedures. Current fitting typically relies on limited and often subjective data that must be collected in limited time. Despite the success of the CI as a hearing-restoration device, variability in speech-recognition scores among users is still very large, and mostly unexplained. The major factors that underly this variability incorporate three levels: (i) variability in auditory-system malfunction of CI-users, (ii) variability in the selectivity of electrode-to-auditory nerve (EL-AN) activation, and (iii) lack of objective perceptual measures to optimize the fitting. We argue that variability in speech recognition can only be alleviated by using objective patient-specific data for an individualized fitting procedure, which incorporates knowledge from all three levels. In this paper, we propose a series of experiments, aimed at collecting a large amount of objective (i.e., quantitative, reproducible, and reliable) data that characterize the three processing levels of the user's auditory system. Machine-learning algorithms that process these data will eventually enable the clinician to derive reliable and personalized characteristics of the user's auditory system, the quality of EL-AN signal transfer, and predictions of the perceptual effects of changes in the current fitting.

Factors Predicting Older People's Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study.

BACKGROUND: Mobile health (mHealth) services enable real-time measurement of information on individuals' biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. OBJECTIVE: The study aims to identify the predictors of older people's intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions. METHODS: A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. RESULTS: Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (ß=.453; P=.003) and social influence (ß=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (ß=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=-2.165). Bootstrapping also confirmed that device trust (ß=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. CONCLUSIONS: This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users.

A Self-Powered Wearable Sensor for Continuous Wireless Sweat Monitoring.

Wireless wearable sweat analysis devices can monitor biomarkers at the molecular level continuously and in situ, which is highly desired for personalized health care. The miniaturization, integration, and wireless operation of sweat sensors improve the comfort and convenience while also bringing forward new challenges for power supply technology. Herein, a wireless self-powered wearable sweat analysis system (SWSAS) is designed that effectively converts the mechanical energy of human motion into electricity through hybrid nanogenerator modules (HNGMs). The HNGM shows stable output characteristics at low frequency with a current of 15 mA and a voltage of 60 V. Through real-time on-body sweat analysis powered by HNGM, the SWSAS is demonstrated to selectively monitor biomarkers (Na+ and K+ ) in sweat and wirelessly transmit the sensing data to the user interface via Bluetooth.

Advancing Inclusive Research: Establishing Collaborative Strategies to Improve Diversity in Clinical Trials.

Well-characterized disparities in clinical research have disproportionately affected patients of color, particularly in underserved communities. To tackle these barriers, Genentech formed the External Council for Advancing Inclusive Research, a 14-person committee dedicated to developing strategies to increase clinical research participation. To help improve the recruitment and retention of patients of color, this article chronicles our efforts to tangibly address the clinical research barriers at the system, study, and patient levels over the last four years. These efforts are one of the initial steps to fully realize the promise of personalized health care and provide increased patient benefit at less cost to society. Instead of simply acknowledging the problem, here we illuminate the collaborative and multilevel strategies that have been effective in delivering meaningful progress for patients.

Sex and age differences in migraine treatment and management strategies.

Migraine is one of the leading causes of disability worldwide, especially in women younger than 50 years old. Migraine has three times higher prevalence in women than in men and tends to decrease after the menopausal transition. Migraine has different clinical features in people of different ages. Clinical symptoms and factors associated with migraine can be various in women and men. Women have special types of migraine, such as pure menstrual migraine and menstrually related migraine. Besides, clinical symptoms of migraine can change during pregnancy, postpartum and lactation. Women are significantly more often than men consulting a doctor because of migraine. These features of migraine lead to different treatment and management strategies in females and males of different ages. Migraine therefore is a disorder that demonstrates the necessity of a personalization of healthcare-ensuring the proper treatment for the right patient, at the right time. Considering all the available literature and guidelines, in this chapter several strategies for management of acute and prophylactic treatments of migraine, according to sex and age differences, are discussed. The purpose of this chapter is to provide a useful piece of information improving the treatment and management of migraine.

Behavior Change App for Self-management of Gestational Diabetes: Design and Evaluation of Desirable Features.

BACKGROUND: Gestational diabetes (GDM) has considerable and increasing health effects as it raises both the mother's and the offspring's risk for short- and long-term health problems. GDM can usually be treated with a healthier lifestyle, such as appropriate dietary modifications and sufficient physical activity. Although telemedicine interventions providing weekly or more frequent feedback from health care professionals have shown the potential to improve glycemic control among women with GDM, apps without extensive input from health care professionals are limited and have not been shown to be effective. Different features in personalization and support have been proposed to increase the efficacy of GDM apps, but the knowledge of how these features should be designed is lacking. OBJECTIVE: The aim of this study is to investigate how GDM apps should be designed, considering the desirable features based on the previous literature. METHODS: We designed an interactive GDM prototype app that provided example implementations of desirable features, such as providing automatic and personalized suggestions and social support through the app. Women with GDM explored the prototype and provided feedback in semistructured interviews. RESULTS: We identified that (1) self-tracking data in GDM apps should be extended with written feedback, (2) habits and goals should be highly customizable to be useful, (3) the app should have different functions to provide social support, and (4) health care professionals should be notified through the app if something unusual occurs. In addition, we found 2 additional themes. First, basic functionalities that are fast to learn by women with GDM who have recently received the diagnosis should be provided, but there should also be deeper features to maintain interest for women with GDM at a later stage of pregnancy. Second, as women with GDM may have feelings of guilt, the app should have a tolerance for and a supporting approach to unfavorable behavior. CONCLUSIONS: The feedback on the GDM prototype app supported the need for desirable features and provided new insights into how these features should be incorporated into GDM apps. We expect that following the proposed designs and feedback will increase the efficacy of GDM self-management apps. TRIAL REGISTRATION: ClinicalTrials.gov NCT03941652; https://clinicaltrials.gov/ct2/show/NCT03941652.

Immune Signature Linked to COVID-19 Severity: A SARS-Score for Personalized Medicine.

SARS-CoV-2 infection leads to a highly variable clinical evolution, ranging from asymptomatic to severe disease with acute respiratory distress syndrome, requiring intensive care units (ICU) admission. The optimal management of hospitalized patients has become a worldwide concern and identification of immune biomarkers predictive of the clinical outcome for hospitalized patients remains a major challenge. Immunophenotyping and transcriptomic analysis of hospitalized COVID-19 patients at admission allow identifying the two categories of patients. Inflammation, high neutrophil activation, dysfunctional monocytic response and a strongly impaired adaptive immune response was observed in patients who will experience the more severe form of the disease. This observation was validated in an independent cohort of patients. Using in silico analysis on drug signature database, we identify differential therapeutics that specifically correspond to each group of patients. From this signature, we propose a score-the SARS-Score-composed of easily quantifiable biomarkers, to classify hospitalized patients upon arrival to adapt treatment according to their immune profile.

Assessing the Effectiveness and Acceptability of a Personalized Mobile Phone App in Improving Adherence to Oral Hygiene Advice in Orthodontic Patients: Protocol for a Feasibility Study and a Randomized Controlled Trial.

BACKGROUND: Orthodontic treatment is a common health care intervention; treatment duration can be lengthy (2-3 years on average), and adherence to treatment advice is therefore essential for successful outcomes. It has been reported that up to 43% of patients fail to complete treatment, and there are currently no useful predictors of noncompletion. Given that the National Health Service England annual expenditure on primary-care orthodontic treatment is in excess of £200 million (US $267 million), noncompletion of treatment represents a significant inefficient use of public resources. Improving adherence to treatment is therefore essential. This necessitates behavior change, and interventions that improve adherence and are designed to elicit behavioral change must address an individual's capability, opportunity, and motivation. Mobile phones are potentially an invaluable tool in this regard, as they are readily available and can be used in a number of ways to address an individual's capability, opportunity, and motivation. OBJECTIVE: This study will assess the effectiveness and acceptability of a personalized mobile phone app in improving adherence to orthodontic treatment advice by way of a randomized controlled trial. METHODS: This study will be conducted in 2 phases at the Eastman Dental Hospital, University College London Hospitals Foundation Trust. Phase 1 is feasibility testing of the My Braces app. Participants will be asked to complete the user version of the Mobile Application Rating Scale. The app will be amended following analysis of the responses, if appropriate. Phase 2 is a randomized controlled trial to test the effectiveness and acceptability of the My Braces app. RESULTS: This study was approved by the London - Bloomsbury Research Ethics Committee on November 5, 2019 (reference 19/LO/1555). No patients have been recruited to date. The anticipated start date for recruitment to phase 1 is October 2020. CONCLUSIONS: Given the availability, affordability, and versatility of mobile phones, it is proposed that they will aid in improving adherence to treatment advice and hence improve treatment completion rates. If effective, the applicability of this methodology to developing behavior change/modification interventions and improving adherence to treatment across health care provides an exciting opportunity. TRIAL REGISTRATION: ClinicalTrials.gov NCT04184739; https://clinicaltrials.gov/ct2/show/NCT04184739. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/18021.

Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care.

Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clinical science, and address issues of ethics, privacy, and trust.

From Nanosystems to a Biosensing Prototype for an Efficient Diagnostic: A Special Issue in Honor of Professor Bansi D. Malhotra.

It has been proven that rapid bioinformatics analysis according to patient health profiles, in addition to biomarker detection at a low level, is emerging as essential to design an analytical diagnostics system to manage health intelligently in a personalized manner. Such objectives need an optimized combination of a nano-enabled sensing prototype, artificial intelligence (AI)-supported predictive analysis, and Internet of Medical Things (IoMT)-based bioinformatics analysis. Such a developed system began with a prototype demonstration of efficient diseases diagnostics performance is the future diseases management approach. To explore these aspects, the Special Issue planned for the nano-and micro-technology section of MDPI's Biosensors journal will honor and acknowledge the contributions of Prof. B.D. Malhotra, Ph.D., FNA, FNASc has made in the field of biosensors.