Quality of Life of Colorectal Cancer Survivors: Mapping the Key Indicators by Expert Consensus and Measures for Their Assessment.

Quality of life (QoL) assessments are integral to cancer care, yet their effectiveness in providing essential information for supporting survivors varies. This study aimed to elucidate key indicators of QoL among colorectal cancer survivors from the perspective of healthcare professionals, and to evaluate existing QoL questionnaires in relation to these indicators. Two studies were conducted: a Delphi study to identify key QoL indicators and a scoping review of questionnaires suitable for colorectal cancer survivors.

Multilingual Framework for Risk Assessment and Symptom Tracking (MRAST).

The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution.

Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from Electronic Health Records.

Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone.

An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease.

Objective: We retrospectively screened 350,116 electronic health records (EHRs) to identify suspected patients for Pompe disease. Using these suspected patients, we then describe their phenotypical characteristics and estimate the prevalence in the respective population covered by the EHRs.

Methods: We applied Symptoma's Artificial Intelligence-based approach for identifying rare disease patients to retrospective anonymized EHRs provided by the "University Hospital Salzburg" clinic group.

Establishing an Expert Consensus on Key Indicators of the Quality of Life among Breast Cancer Survivors: A Modified Delphi Study.

(1) Background: The needs of cancer survivors are often not reflected in practice. One of the main barriers of the use of patient-reported outcomes is associated with data collection and the interpretation of patient-reported outcomes (PROs) due to a multitude of instruments and measuring approaches. The aim of the study was to establish an expert consensus on the relevance and key indicators of quality of life in the clinical practice of breast cancer survivors.

Language, Speech, and Facial Expression Features for Artificial Intelligence-Based Detection of Cancer Survivors' Depression: Scoping Meta-Review.

Background: Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed.

Inhibition of Sphingosine-1-Phosphate Receptor 2 by JTE013 Promoted Osteogenesis by Increasing Vesicle Trafficking, Wnt/Ca, and BMP/Smad Signaling.

Sphingosine-1-phosphate receptor 2 (S1PR2) is a G protein-coupled receptor that regulates various immune responses. Herein, we determine the effects of a S1PR2 antagonist (JTE013) or a S1PR2 shRNA on osteogenesis by culturing murine bone marrow stromal cells (BMSCs) in osteogenic media with JTE013, dimethylsulfoxide (DMSO), a S1PR2 shRNA, or a control shRNA. Treatment with JTE013 or the S1PR2 shRNA increased alkaline phosphatase and alizarin red s staining, and enhanced alkaline phosphatase, RUNX2, osteocalcin, and osterix mRNA levels in BMSCs compared with the controls.

Children with Amalgam Dental Restorations Have Significantly Elevated Blood and Urine Mercury Levels.

Human exposure to organic mercury (Hg) as methylmercury (MeHg) from seafood consumption is widely considered a health risk because pure methylmercury is extremely neurotoxic. In contrast, the clinical significance of Hg exposure from amalgam (AMG) dental restorations, the only other major nonoccupational source of Hg exposure, has long been debated. Here, we examined data from the two most recent National Health and Nutrition Examination Surveys (NHANES) on 14 181 subjects to assess the contributions of seafood consumption versus AMG to blood total mercury (THg), inorganic mercury (IHg), and methyl mercury (MeHg) and to urine creatinine corrected mercury (UTHg).

Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies (PERSIST): a multicenter study protocol to evaluate efficacy of digital tools supporting cancer survivors.

Background: It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when it comes to improving their quality of life (QoL).

Unjust: the health records of youth with personal/family justice involvement in a large pediatric health system.

Background: Mass incarceration has had an undeniable toll on childhood poverty and inequality, however, little is known about the consequences on pediatric health. The purpose of this study was to identify and describe the health of pediatric patients with probable personal or family history involvement with the correctional system.

Methods: A descriptive study was conducted using electronic health record data of 2.

Delivering Perinatal Health Information via a Voice Interactive App (SMILE): Mixed Methods Feasibility Study.

Background: Perinatal health care is critically important for maternal health outcomes in infants. The United States fares considerably worse than comparable countries for maternal and infant mortality rates. As such, alternative models of care or engagement are warranted.

A Machine Learning Approach to Uncovering Hidden Utilization Patterns of Early Childhood Dental Care Among Medicaid-Insured Children.

Early childhood dental care () is a significant public health opportunity since dental caries is largely preventable and a prime target for reducing healthcare expenditures. This study aims to discover underlying patterns in ECDC utilization among Ohio Medicaid-insured children, which have significant implications for public health prevention, innovative service delivery models, and targeted cost-saving interventions. Using 9 years of longitudinal Medicaid data of 24,223 publicly insured child members of an accountable care organization (), Partners for Kids in Ohio, we applied unsupervised machine learning to cluster patients based on their cumulative dental cost curves in early childhood (24-60 months).

Clinical Advice by Voice Assistants on Postpartum Depression: Cross-Sectional Investigation Using Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana.

Background: A voice assistant (VA) is inanimate audio-interfaced software augmented with artificial intelligence, capable of 2-way dialogue, and increasingly used to access health care advice. Postpartum depression (PPD) is a common perinatal mood disorder with an annual estimated cost of $14.2 billion.

Assessment of 135 794 Pediatric Patients Tested for Severe Acute Respiratory Syndrome Coronavirus 2 Across the United States.

Importance: There is limited information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing and infection among pediatric patients across the United States.

Objective: To describe testing for SARS-CoV-2 and the epidemiology of infected patients.

Design, Setting, And Participants: A retrospective cohort study was conducted using electronic health record data from 135 794 patients younger than 25 years who were tested for SARS-CoV-2 from January 1 through September 8, 2020.

Pyridine alkaloids with activity in the central nervous system.

This review discusses all pyridine alkaloids with CNS activity, their therapeutic potential, and the interesting array of sources whence they originate.

Readiness for voice assistants to support healthcare delivery during a health crisis and pandemic.

To prevent the spread of COVID-19 and to continue responding to healthcare needs, hospitals are rapidly adopting telehealth and other digital health tools to deliver care remotely. Intelligent conversational agents and virtual assistants, such as chatbots and voice assistants, have been utilized to augment health service capacity to screen symptoms, deliver healthcare information, and reduce exposure. In this commentary, we examined the state of voice assistants (e.

A scoping review of patient-facing, behavioral health interventions with voice assistant technology targeting self-management and healthy lifestyle behaviors.

Engaging in positive healthy lifestyle behaviors continues to be a public health challenge, requiring innovative solutions. As the market for voice assistants (Amazon Alexa, Google Assistant, and Apple Siri) grows and people increasingly use them to assist their daily tasks, there is a pressing need to explore how voice assistant (VA) technology may be used in behavioral health interventions. A scoping review of literature was conducted to address a PICO (Population, Intervention, Comparison, and Outcome) question: across populations, how does the use of voice assistants in behavioral health research/interventions influence healthy lifestyle behaviors versus control or comparison interventions? To inform the science, a secondary aim of this review was to explore characteristics of VAs used in behavioral health research.

Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning.

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit.

Figure Correction: Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning.

[This corrects the article DOI: 10.2196/18279.].

Pediatric Telebehavioral Health: A Transformational Shift in Care Delivery in the Era of COVID-19.

The use of telebehavioral health has been expanding in the past decade to improve access to psychiatric care and address critical shortages in the psychiatric workforce. The coronavirus (COVID-19) pandemic forced a sudden shift from traditional in-person visits to alternative modalities. There are key factors associated with successful transitional and large-scale implementation of telehealth with existing resources.