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The proposed end-to-end system achieved encouraging results and demonstrated the feasibility of using deep learning methods to extract medication information from free-text data.
Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU.
We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted.
We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machiations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.
Orofacial cleft, one of the most common congenital deformities, presents with a plethora of defects, subjecting the patient to a multitude of treatments from a young age. Among the oral hard tissue problems, absence of a maxillary permanent tooth in the cleft region either due to congenital absence or extraction due to compromised prognosis is a common finding. Conventionally, the missing tooth is replaced using a removable or fixed partial denture; however, the treatment modality does not satisfactorily meet patient expectations. The most recent decade has seen increasing use of dental implants in the cleft region; however, the outcome of an immediately loaded dental implant is still elusive for orofacial cleft patients.
This protocol is for a single-arm clinical trial aimed at determining the treatment outcome of immediately loaded dental implants in patients with a nonsyndromic orofacial cleft.
Patients meeting the set criteria will be sequentially enrolled until a sample size of 30 dental implants iinicaltrials/showallp.php?mid1=47659&EncHid=&userName=dental%20implants.
PRR1-10.2196/25244.
PRR1-10.2196/25244.
Canadian public safety personnel (PSP) experience high rates of mental health disorders and face many barriers to treatment. Internet-delivered cognitive behavioral therapy (ICBT) overcomes many such barriers, and is effective for treating depression, anxiety, and posttraumatic stress disorder (PTSD) symptoms.
This study was designed to fill a gap in the literature regarding the use of ICBT tailored specifically for PSP. We examined the effectiveness of a tailored ICBT program for treating depression, anxiety, and PTSD symptoms among PSP in the province of Saskatchewan.
We employed a longitudinal single-group open-trial design (N=83) with outcome measures administered at screening and at 8 weeks posttreatment. Data were collected between December 5, 2019 and September 11, 2020. Primary outcomes included changes in depression, anxiety, and PTSD symptoms. Secondary outcomes included changes in functional impairment; symptoms of panic, social anxiety, and anger; as well as treatment satisfaction, working alliance, and program usage patterns.
Clients reported large symptom reductions on measures of depression and anxiety, as well as moderate reductions on measures of PTSD and secondary symptoms, except for social anxiety. Most clients who reported symptoms above clinical cut-offs on measures of depression, anxiety, and PTSD during screening experienced clinically significant symptom reductions. Results suggested good engagement, treatment satisfaction, and working alliance.
Tailored, transdiagnostic ICBT demonstrated promising outcomes as a treatment for depression, anxiety, and PTSD among Saskatchewan PSP and warrants further investigation.
Clinicaltrials.gov NCT04127032; https//www.clinicaltrials.gov/ct2/show/NCT04127032.
Clinicaltrials.gov NCT04127032; https//www.clinicaltrials.gov/ct2/show/NCT04127032.
Telehealth has potential to help individuals in rural areas overcome geographical barriers and to improve access to care. The factors that influence the implementation and use of telehealth in critical access hospitals are in need of exploration.
The aim of this study is to understand the factors that influenced telehealth uptake and use in a set of frontier critical access hospitals in the United States.
This work was conducted as part of a larger evaluation of a Centers for Medicare & Medicaid Services-funded demonstration program to expand cost-based reimbursement for services for Medicare beneficiaries for frontier critical access hospitals. Our sample was 8 critical access hospitals in Montana, Nevada, and North Dakota that implemented the telehealth aspect of that demonstration. We reviewed applications and progress reports for the demonstration program and conducted in-person site visits. We used a semistructured discussion guide to facilitate conversations with clinical, administrative, and ce from specialty practitioners and workforce challenges. Telehealth can be used for provider-to-patient and provider-to-provider interactions to improve access to care, remove barriers, and improve quality. However, the ability of telehealth to improve outcomes is limited by factors such as workflow and infrastructure changes, practitioner acceptance and availability, and financing.
Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. selleck kinase inhibitor Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects.
The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients.
The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels.