Journal of Medical Systems最新文献

Wireless medical telemetry systems (WMTSs) are typical radio communication-based medical devices that monitor various biological parameters, such as electrocardiograms and respiration rates. In Japan, the assigned frequency band for WMTSs is 400 MHz. However, the issues accounting for poor reception in WMTS constitute major concerns. In this study, we analyzed the effects of electromagnetic interferences (EMIs) caused by other radio communication systems, the intermodulation (IM) effect, and noises generated from electrical devices on WMTS and discussed their management. The 400-MHz frequency band is also shared by other radio communication systems. We showed the instantaneous and impulsive voltages generated from the location-detection system for wandering patients and their potential to exhibit EMI effects on WMTS. Further, we presented the IM effect significantly reduces reception in WMTS. Additionally, the electromagnetic noises generated from electrical devices, such as light-emitting diode lamps and security cameras, can exceed the 400 MHz frequency band as these devices employ the switched-mode power supply and/or central processing unit and radiate wideband emissions. Moreover, we proposed and evaluated simple and facile methods using a simplified spectrum analysis function installed in the WMTS receiver and software-defined radio for evaluating the electromagnetic environment.

The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.

This is the second in a series of studies assessing the usability and reliability of a novel voice-based delivery system of mental health screening assessments. The previous study demonstrated the reliability and patient preference of a voice-based format of the Patient Health Questionnaire 9 (PHQ 9) for measuring major depression compared to a traditional paper format. Through this study, we further examined the Amazon Alexa tool in the administration of the General Anxiety Disorder 7 (GAD 7). With a replicated methodology to the first study, 40 newly administered patients completed the GAD 7 in one format at their first session and the alternate format at their follow up. Results from the new in clinic population replicated the findings observed in the first PHQ 9 study: GAD 7 assessment scores for the Alexa and paper version showed a high degree of reliability (α = 0.77), patients showed higher overall positive attitudes for the voice-based GAD 7 format, and subscales for attractiveness, stimulation, and novelty were significantly higher for the voiced-based format. Results also demonstrated 42 (84%) of the 50 patients who completed the voice-based format responded as being willing to use the device from home. With new recommendations of universal screening of anxiety disorders for patients below the age of 65 and rapid changes in virtual mental healthcare, convenient screenings are more important than ever. We believe this novel clinical assessment tool has the potential to improve patient behavioral healthcare while mitigating the workload of healthcare professionals.

Background:  Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors for UCE, but determining which factors are most important is unclear. Machine learning (ML) can predict clinical events. We hypothesized that ML could predict patient UCE after PACU discharge in surgical patients and identify specific risk factors.

Methods: We conducted a single center, retrospective analysis of all patients undergoing non-cardiac surgery (elective and emergent). We collected data from pre-operative visits, intra-operative records, PACU admissions, and the rate of UCE. We trained a ML model with this data and tested the model on an independent data set to determine its efficacy. Finally, we evaluated the individual patient and clinical factors most likely to predict UCE risk.

Results: Our study revealed that ML could predict UCE risk which was approximately 5% in both the training and testing groups. We were able to identify patient risk factors such as patient vital signs, emergent procedure, ASA Status, and non-surgical anesthesia time as significant variable. We plotted Shapley values for significant variables for each patient to help determine which of these variables had the greatest effect on UCE risk. Of note, the UCE risk factors identified frequently by ML were in alignment with anesthesiologist clinical practice and the current literature.

Conclusions: We used ML to analyze data from a single-center, retrospective cohort of non-cardiac surgical patients, some of whom had an UCE. ML assigned risk prediction for patients to have UCE and determined perioperative factors associated with increased risk. We advocate to use ML to augment anesthesiologist clinical decision-making, help decide proper disposition from the PACU, and ensure the safest possible care of our patients.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events. In this study, a DLM was trained on a dataset of 158,471 ECGs from 42,903 academic medical center patients, with additional validation involving 25,640 medical center patients and 26,538 community hospital patients. Primary analysis focused on predicting PMI within 90 days, while all-cause mortality, cardiovascular disease (CVD) mortality, and the development of various cardiovascular conditions were addressed with secondary analysis. The study's raw ECG DLM achieved area under the curve (AUC) values of 0.870, 0.878, and 0.883 for PMI prediction within 30, 60, and 90 days, respectively, along with sensitivities exceeding 82.0% and specificities over 81.9% in the internal validation. Significant ECG features included the PR interval, corrected QT interval, heart rate, QRS duration, P-wave axis, T-wave axis, and QRS complex axis. The AI-predicted PMI group had higher risks of PMI after 90 days (hazard ratio [HR]: 7.49, 95% CI: 5.40-10.39), all-cause mortality (HR: 1.91, 95% CI: 1.74-2.10), CVD mortality (HR: 3.53, 95% CI: 2.73-4.57), and new-onset adverse cardiovascular events. External validation confirmed the model's accuracy. Through ECG analyses, our AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.

Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This systematic review (PROSPERO protocol CRD42023492655) includes 33 studies that utilised machine learning (ML) and deep learning (DL) models for survival outcome in CVD prediction. We provided details on the employed ML and DL models, eXplainable AI (XAI) techniques, and type of included variables, with a focus on social determinants of health (SDoH) and gender-stratification. Approximately half of the studies were published in 2023 with the majority from the United States. Random Survival Forest (RSF), Survival Gradient Boosting models, and Penalised Cox models were the most frequently employed ML models. DeepSurv was the most frequently employed DL model. DL models were better at predicting CVD outcomes than ML models. Permutation-based feature importance and Shapley values were the most utilised XAI methods for explaining AI models. Moreover, only one in five studies performed gender-stratification analysis and very few incorporate the wide range of SDoH factors in their prediction model. In conclusion, the evidence indicates that RSF and DeepSurv models are currently the optimal models for predicting CVD outcomes. This study also highlights the better predictive ability of DL survival models, compared to ML models. Future research should ensure the appropriate interpretation of AI models, accounting for SDoH, and gender stratification, as gender plays a significant role in CVD occurrence.

Three-dimensional (3D) printing has gained popularity across various domains but remains less integrated into medical surgery due to its complexity. Existing literature primarily discusses specific applications, with limited detailed guidance on the entire process. The methodological details of converting Computed Tomography (CT) images into 3D models are often found in amateur 3D printing forums rather than scientific literature. To address this gap, we present a comprehensive methodology for converting CT images of bone fractures into 3D-printed models. This involves transferring files in Digital Imaging and Communications in Medicine (DICOM) format to stereolithography format, processing the 3D model, and preparing it for printing. Our methodology outlines step-by-step guidelines, time estimates, and software recommendations, prioritizing free open-source tools. We also share our practical experience and outcomes, including the successful creation of 72 models for surgical planning, patient education, and teaching. Although there are challenges associated with utilizing 3D printing in surgery, such as the requirement for specialized expertise and equipment, the advantages in surgical planning, patient education, and improved outcomes are evident. Further studies are warranted to refine and standardize these methodologies for broader adoption in medical practice.

Physical activity is essential to interrupt the cycle of deconditioning associated with chronic kidney disease (CKD). However, access to targeted physical activity interventions remain under-supported due to limited funding and specialised staff. Digital interventions may address some of these factors. This systematic review sought to examine the evidence base of digital interventions focused on promoting physical activity or exercise and their effect on health outcomes for people living with CKD. Electronic databases (PubMed, CINAHL, Embase, Cochrane) were searched from 1 January 2000 to 1 December 2023. Interventions (smartphone applications, activity trackers, websites) for adults with CKD (any stage, including transplant) which promoted physical activity or exercise were included. Study quality was assessed, and a narrative synthesis was conducted. Of the 4057 records identified, eight studies (five randomised controlled trials, three single-arm studies) were included, comprising 550 participants. Duration ranged from 12-weeks to 1-year. The findings indicated acceptability and feasibility were high, with small cohort numbers and high risk of bias. There were inconsistent measures of physical activity levels, self-efficacy, body composition, physical function, and psychological outcomes which resulted in no apparent effects of digital interventions on these domains. Data were insufficient for meta-analysis. The evidence for digital interventions to promote physical activity and exercise for people living with CKD is limited. Despite popularity, there is little evidence that current digital interventions yield the effects expected from traditional face-to-face interventions. However, 14 registered trials were identified which may strengthen the evidence-base.