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.

Background: Assessment of cognitive decline in the earliest stages of Alzheimer disease (AD) is important but challenging. AD is a neurodegenerative disease characterized by gradual cognitive decline. Disease stages range from preclinical AD, in which individuals are cognitively unimpaired, to mild cognitive impairment (MCI) and dementia. Digital technologies promise to enable detection of early, subtle cognitive changes. Although the field of digital cognitive biomarkers is rapidly evolving, a comprehensive overview of the reporting of psychometric properties (ie, validity, reliability, responsiveness, and clinical meaningfulness) is missing. Insight into the extent to which these properties are evaluated is needed to identify the validation steps toward implementation. Objective: This scoping review aimed to identify the reporting on quality characteristics of smartphone- and tablet-based cognitive tools with potential for remote administration in individuals with preclinical AD or MCI. We focused on both psychometric properties and practical tool characteristics. Methods: This scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. In total, 4 databases (PubMed, Embase, Web of Science, and PsycINFO) were systematically searched from January 1, 2008, to January 5, 2023. Studies were included that assessed the psychometric properties of cognitive smartphone- or tablet-based tools with potential for remote administration in individuals with preclinical AD or MCI. In total, 2 reviewers independently screened titles and abstracts in ASReview, a screening tool that combines manual and automatic screening using an active learning algorithm. Thereafter, we manually screened full texts in the web application Rayyan. For each included study, 2 reviewers independently explored the reported information on practical and psychometric properties. For each psychometric property, examples were provided narratively. Results: In total, 11,300 deduplicated studies were identified in the search. After screening, 50 studies describing 37 different digital tools were included in this review. Average administration time was 13.8 (SD 10.1; range 1-32) minutes, but for 38% (14/37) of the tools, this was not described. Most tools (31/37, 84%) were examined in 1 language. The investigated populations were mainly individuals with MCI (34/37, 92%), and fewer tools were examined in individuals with preclinical AD (8/37, 22%). For almost all tools (36/37, 97%), construct validity was assessed through evaluation of clinical or biological associations or relevant group differences. For a small number of tools, information on structural validity (3/37, 8%), test-retest reliability (12/37, 32%), responsiveness (6/37, 16%), or clinical meaningfulness (0%) was reported. Conclusions: Numerous smartphone- and tablet-based tools to assess cognition in early AD are being developed, whereas studies concerning their psychometric properties are limited. Often, initial validation steps have been taken, yet further validation and careful selection of psychometrically valid outcome scores are required to demonstrate clinical usefulness with regard to the context of use, which is essential for implementation.

Background: Guideline‐directed treatment is often withheld in frail patients with heart failure (HF) due to concerns about intolerability, yet they may benefit given the higher risk of HF complications. This systematic review evaluates the efficacy and safety of HF therapy according to frailty levels. Methods: We searched Medline, Embase, and Web of Science for randomized clinical trials on HF therapies reporting outcomes by frailty status (as defined by the study). Data were summarized in a harvest plot, and a meta‐analysis was conducted where possible. Results: We identified 12 studies: 6 on medication; 3 on exercise/rehabilitation; and 1 each on disease management programs, telemonitoring, and implantable cardioverter‐defibrillators. Most studies found no significant interaction between intervention and frailty, except for some specific outcomes where certain interventions had an interaction in favor of the frail. Conclusions: HF therapies, with the exception of implantable cardioverter‐defibrillators, are safe and appear to have at least similar benefits in frail and nonfrail patients. Given higher event rates in frail patients, the absolute risk reduction for the studied interventions is greater in frail patients. Optimal guideline‐directed treatment should therefore also be considered for frail patients.

Background: Personal Health Monitoring (PHM) has the potential to enhance soldier health outcomes. To promote morally responsible development, implementation, and use of PHM in the armed forces, it is important to be aware of the inherent ethical dimension of PHM. In order to improve the understanding of the ethical dimension, a scoping review of the existing academic literature on the ethical dimension of PHM was conducted. Methods: Four bibliographical databases (Ovid/Medline, Embase.com, Clarivate Analytics/Web of Science Core Collection, and Elsevier/SCOPUS) were searched for relevant literature from their inception to June 1, 2023. Studies were included if they sufficiently addressed the ethical dimension of PHM and were related to or claimed relevance for the military. After selection and extraction, the data was analysed using a qualitative thematic approach. Results: A total of 9,071 references were screened. After eligibility screening, 19 articles were included for this review. The review identifies and describes three categories reflecting the ethical dimension of PHM in the military: (1) utilitarian considerations, (2) value-based considerations, and (3) regulatory responsibilities. The four main values that have been identified as being of concern are those of privacy, security, trust, and autonomy. Conclusions: This review demonstrates that PHM in the armed forces is primarily approached from a utilitarian perspective, with a focus on its benefits, without explicit critical deliberation on PHM’s potential moral downsides. Also, the review highlights a significant research gap with a specific lack of empirical studies focussing specifically on the ethical dimension of PHM. Awareness of the inherent ethical dimension of PHM in the military, including value conflicts and how to balance them, can help to contribute to a morally responsible development, implementation, and use of PHM in the armed forces.

Dental calculus preserves oral microbes, enabling comparative studies of the oral microbiome and health through time. However, small sample sizes and limited dental health metadata have hindered health-focused investigations to date. Here we investigate the relationship between tobacco pipe smoking and dental calculus microbiomes. Dental calculus from 75 individuals from the 19th century Middenbeemster skeletal collection (Netherlands) were analyzed by metagenomics. Demographic and dental health parameters were systematically recorded, including the presence/number of pipe notches. Comparative data sets from European populations before and after the introduction of tobacco were also analyzed. Calculus species profiles were compared with oral pathology to examine associations between microbiome community, smoking behavior, and oral health status. The Middenbeemster individuals exhibited relatively poor oral health, with a high prevalence of periodontal disease, caries, heavy calculus deposits, and antemortem tooth loss. No associations between pipe notches and dental pathologies, or microbial species composition, were found. Calculus samples before and after the introduction of tobacco showed highly similar species profiles. Observed inter-individual microbiome differences were consistent with previously described variation in human populations from the Upper Paleolithic to the present. Dental calculus may not preserve microbial indicators of health and disease status as distinctly as dental plaque. Research Highlights: No associations between calculus species profiles and oral health metrics were detected in a single large population A minority of individuals have a dental calculus species profile characterized by low levels of Streptococcus and high levels of anaerobic taxa

Newborns are born with an immature immune system, making them susceptible to infections early in life. Human milk provides essential nutrients and immunological factors that support infant immunity. Maternal vaccination during lactation has the potential to enhance these benefits by triggering an immune response in the mother, potentially extending protection to her child. However, lactating individuals are often excluded from vaccine trials, leading to uncertainties about vaccine safety and efficacy during the postpartum period. This study critically evaluates the effectiveness of vaccines in enhancing the immune-supporting properties of human milk and assesses their safety and efficacy for lactating mothers and their infants. By examining potential benefits alongside safety concerns, we aim to provide a comprehensive understanding of postpartum vaccination’s impact on maternal and infant health. We utilized large-language models (LLMs) to enhance the review process and performed a structured literature search across Ovid/Medline, Embase, and Clarivate Analytics using terms like “breastfeeding”, “postpartum”, and “vaccination”. A three-stage screening process involving human and LLM-assisted evaluation focused on postpartum vaccines and their implications for maternal and infant health. We identified 73 studies covering vaccines against COVID-19, cholera, influenza, pertussis, pneumococcal, rabies, polio, rotavirus, rubella, varicella, typhoid, smallpox, and yellow fever. Most vaccines, such as those for COVID-19 and influenza, appear safe and effective for postpartum use without requiring precautionary measures. However, caution is advised with vaccines such as the yellow fever vaccine, where temporary breastfeeding cessation is recommended. Overall, this review underscores the compatibility of most vaccines with lactation and suggests its benefits for both mother and infant.