Developing a Smartwatch-Based Healthcare Application: Notes to Consider.

Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems.

The Presence of a Cost-Volume Relationship in Robotic-assisted Thoracoscopic Lung Resections.

OBJECTIVE: To characterize the relationship between institutional robotic-assisted pulmonary lobectomy volume and hospitalization costs. BACKGROUND: The high cost of robotic-assisted thoracoscopic surgery (RATS) is among several drivers of hesitation among nonadopters. Studies examining the impact of institutional experience on costs of RATS lobectomy are lacking. METHODS: Adults undergoing RATS lobectomy for primary lung cancers were identified from the 2016 to 2018 Nationwide Readmissions Database. A multivariable regression to model hospitalization costs was developed with the inclusion of hospital RATS lobectomy volume as restricted cubic splines. The volume corresponding to the inflection point of the spline was used to categorize hospitals as high- (HVH) or low-volume (LVH). We subsequently examined the association of HVH status with adverse events, length of stay, costs, and 30-day, nonelective readmissions. RESULTS: An estimated 14,756 patients underwent RATS lobectomy during the study period, with median cost of $23,000. Upon adjustment for patient and operative characteristics, hospital RATS volume was inversely associated with costs. Although only 17.2% of centers were defined as HVH, 51.7% of patients were managed at these centers. Patients at HVH and LVH had similar age, sex, and distribution of comorbidities. Notably, patients at HVH had decreased risk-adjusted odds of adverse events (adjusted odds ratio: 0.62, P <0.001), as well as significantly reduced length of stay (-0.8 d, P <0.001) and costs (-$3900, P <0.001). CONCLUSIONS: Increasing hospital RATS lobectomy volume was associated with reduced hospitalization costs. Our findings suggest the presence of streamlined care pathways at high-volume centers, which influence costs of care.

Temporal convolutional networks and data rebalancing for clinical length of stay and mortality prediction.

It is critical for hospitals to accurately predict patient length of stay (LOS) and mortality in real-time. We evaluate temporal convolutional networks (TCNs) and data rebalancing methods to predict LOS and mortality. This is a retrospective cohort study utilizing the MIMIC-III database. The MIMIC-Extract pipeline processes 24 hour time-series clinical objective data for 23,944 unique patient records. TCN performance is compared to both baseline and state-of-the-art machine learning models including logistic regression, random forest, gated recurrent unit with decay (GRU-D). Models are evaluated for binary classification tasks (LOS > 3 days, LOS > 7 days, mortality in-hospital, and mortality in-ICU) with and without data rebalancing and analyzed for clinical runtime feasibility. Data is split temporally, and evaluations utilize tenfold cross-validation (stratified splits) followed by simulated prospective hold-out validation. In mortality tasks, TCN outperforms baselines in 6 of 8 metrics (area under receiver operating characteristic, area under precision-recall curve (AUPRC), and F-1 measure for in-hospital mortality; AUPRC, accuracy, and F-1 for in-ICU mortality). In LOS tasks, TCN performs competitively to the GRU-D (best in 6 of 8) and the random forest model (best in 2 of 8). Rebalancing improves predictive power across multiple methods and outcome ratios. The TCN offers strong performance in mortality classification and offers improved computational efficiency on GPU-enabled systems over popular RNN architectures. Dataset rebalancing can improve model predictive power in imbalanced learning. We conclude that temporal convolutional networks should be included in model searches for critical care outcome prediction systems.

Parsimonious machine learning models to predict resource use in cardiac surgery across a statewide collaborative.

Objective: We sought to several develop parsimonious machine learning models to predict resource utilization and clinical outcomes following cardiac operations using only preoperative factors. Methods: All patients undergoing coronary artery bypass grafting and/or valve operations were identified in the 2015-2021 University of California Cardiac Surgery Consortium repository. The primary end point of the study was length of stay (LOS). Secondary endpoints included 30-day mortality, acute kidney injury, reoperation, postoperative blood transfusion and duration of intensive care unit admission (ICU LOS). Linear regression, gradient boosted machines, random forest, extreme gradient boosting predictive models were developed. The coefficient of determination and area under the receiver operating characteristic (AUC) were used to compare models. Important predictors of increased resource use were identified using SHapley summary plots. Results: Compared with all other modeling strategies, gradient boosted machines demonstrated the greatest performance in the prediction of LOS (coefficient of determination, 0.42), ICU LOS (coefficient of determination, 0.23) and 30-day mortality (AUC, 0.69). Advancing age, reduced hematocrit, and multiple-valve procedures were associated with increased LOS and ICU LOS. Furthermore, the gradient boosted machine model best predicted acute kidney injury (AUC, 0.76), whereas random forest exhibited greatest discrimination in the prediction of postoperative transfusion (AUC, 0.73). We observed no difference in performance between modeling strategies for reoperation (AUC, 0.80). Conclusions: Our findings affirm the utility of machine learning in the estimation of resource use and clinical outcomes following cardiac operations. We identified several risk factors associated with increased resource use, which may be used to guide case scheduling in times of limited hospital capacity.

Physical Activity Behavior of Patients at a Skilled Nursing Facility: Longitudinal Cohort Study.

BACKGROUND: On-body wearable sensors have been used to predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In our previous work, we introduced a generic remote patient monitoring framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and the extraction of indoor localization using Bluetooth low energy beacons, in concert. Using the same framework, this paper addresses the longitudinal analyses of a group of patients in a skilled nursing facility. We try to investigate if the metrics derived from a remote patient monitoring system comprised of physical activity and indoor localization sensors, as well as their association with therapist assessments, provide additional insight into the recovery process of patients receiving rehabilitation. OBJECTIVE: The aim of this paper is twofold: (1) to observe longitudinal changes of sensor-based physical activity and indoor localization features of patients receiving rehabilitation at a skilled nursing facility and (2) to investigate if the sensor-based longitudinal changes can complement patients' changes captured by therapist assessments over the course of rehabilitation in the skilled nursing facility. METHODS: From June 2016 to November 2017, patients were recruited after admission to a subacute rehabilitation center in Los Angeles, CA. Longitudinal cohort study of patients at a skilled nursing facility was followed over the course of 21 days. At the time of discharge from the skilled nursing facility, the patients were either readmitted to the hospital for continued care or discharged to a community setting. A longitudinal study of the physical therapy, occupational therapy, and sensor-based data assessments was performed. A generalized linear mixed model was used to find associations between functional measures with sensor-based features. Occupational therapy and physical therapy assessments were performed at the time of admission and once a week during the skilled nursing facility admission. RESULTS: Of the 110 individuals in the analytic sample with mean age of 79.4 (SD 5.9) years, 79 (72%) were female and 31 (28%) were male participants. The energy intensity of an individual while in the therapy area was positively associated with transfer activities (ß=.22; SE 0.08; P=.02). Sitting energy intensity showed positive association with transfer activities (ß=.16; SE 0.07; P=.02). Lying down energy intensity was negatively associated with hygiene activities (ß=-.27; SE 0.14; P=.04). The interaction of sitting energy intensity with time (ß=-.13; SE 0.06; P=.04) was associated with toileting activities. CONCLUSIONS: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information to the story line of daily and longitudinal activity patterns of patients receiving rehabilitation at a skilled nursing facility. The findings suggest that detecting physical activity changes within locations may offer some insight into better characterizing patients' progress or decline.

Machine learning-based modeling of acute respiratory failure following emergency general surgery operations.

BACKGROUND: Emergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored. OBJECTIVE: The present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort. METHODS: Non-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016-18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R2). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models. RESULTS: Of 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899-0.901 vs 0.894, 95% CI 0.862-0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R2: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8-11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0-3.2) and $11,900 (95% CI 11,600-12,300) in costs. CONCLUSIONS: Logistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.

Remote Monitoring of Patients With Hematologic Malignancies at High Risk of Febrile Neutropenia: Exploratory Study.

BACKGROUND: Febrile neutropenia is one of the most common oncologic emergencies and is associated with significant, preventable morbidity and mortality. Most patients who experience a febrile neutropenia episode are hospitalized, resulting in significant economic cost. OBJECTIVE: This exploratory study implemented a remote monitoring system comprising a digital infrared thermometer and a pulse oximeter with the capability to notify providers in real time of abnormalities in vital signs that could suggest early clinical deterioration and thereby improve clinical outcomes. METHODS: The remote monitoring system was implemented and compared to standard-of-care vital signs monitoring in hospitalized patients with underlying hematologic malignancies complicated by a febrile neutropenia episode in order to assess the feasibility and validity of the system. Statistical analysis was performed using the intraclass correlation coefficient (ICC) to assess the consistency between the measurements taken using traditional methods and those taken with the remote monitoring system for each of the vital sign parameters (temperature, heart rate, and oxygen saturation). A linear mixed-effects model with a random subject effect was used to estimate the variance components. Bland-Altman plots were created for the parameters to further delineate the direction of any occurring bias. RESULTS: A total of 23 patients were enrolled in the study (mean age 56, SD 23-75 years; male patients: n=11, 47.8%). ICC analysis confirmed the high repeatability and accuracy of the heart rate assessment (ICC=0.856), acting as a supplement to remote temperature assessment. While the sensitivity and specificity for capturing tachycardia above a rate of 100 bpm were excellent (88% and 97%, respectively), the sensitivity of the remote monitoring system in capturing temperatures >37.8 °C and oxygen saturation <92% was 45% and 50%, respectively. CONCLUSIONS: Overall, this novel approach using temperature, heart rate, and oxygen saturation assessments successfully provided real-time, clinically valuable feedback to providers. While temperature and oxygen saturation assessments lagged in terms of sensitivity compared to a standard in-hospital system, the heart rate assessment provided highly accurate complementary data. As a whole, the system provided additional information that can be applied to a clinically vulnerable population. By transitioning its application to high-risk patients in the outpatient setting, this system can help prevent additional use of health care services through early provider intervention and potentially improve outcomes.

The derivation of an International Classification of Diseases, Tenth Revision-based trauma-related mortality model using machine learning.

BACKGROUND: Existing mortality prediction models have attempted to quantify injury burden following trauma-related admissions with the most notable being the Injury Severity Score (ISS). Although easy to calculate, it requires additional administrative coding. International Classification of Diseases (ICD)-based models such as the Trauma Mortality Prediction Model (TMPM-ICD10) circumvent these limitations, but they use linear modeling, which may not adequately capture the intricate relationships of injuries on mortality. Using ICD-10 coding and machine learning (ML) algorithms, the present study used the National Trauma Data Bank to develop mortality prediction models whose performance was compared with logistic regression, ISS, and TMPM-ICD10. METHODS: The 2015 to 2017 National Trauma Data Bank was used to identify adults following trauma-related admissions. Of 8,021 ICD-10 codes, injuries were categorized into 1,495 unique variables. The primary outcome was in-hospital mortality. eXtreme Gradient Boosting (XGBoost), a ML technique that uses iterations of decision trees, was used to develop mortality models. Model discrimination was compared with logistic regression, ISS, and TMPM-ICD10 using receiver operating characteristic curve and probabilistic accuracy with calibration curves. RESULTS: Of 1,611,063 patients, 54,870 (3.41%) experienced in-hospital mortality. Compared with those who survived, those who died more frequently suffered from penetrating trauma and had a greater number of injuries. The XGBoost model exhibited superior receiver operating characteristic curve (0.863 [95% confidence interval (CI), 0.862-0.864]) compared with logistic regression (0.845 [95% CI, 0.844-0.846]), ISS (0.828 [95% CI, 0.827-0.829]), and TMPM-ICD10 (0.861 [95% CI, 0.860-0.862]) (all p < 0.001). Importantly, the ML model also had significantly improved calibration compared with other methodologies (XGBoost, coefficient of determination (R2) = 0.993; logistic regression, R2 = 0.981; ISS, R2 = 0.649; TMPM-ICD10, R2 = 0.830). CONCLUSION: Machine learning models using XGBoost demonstrated superior performance and calibration compared with logistic regression, ISS, and TMPM-ICD10. Such approaches in quantifying injury severity may improve its utility in mortality prognostication, quality improvement, and trauma research. LEVEL OF EVIDENCE: Prognostic and Epidemiologic; level III.

A developed composite hard-gelatin capsules: delayed-release enteric properties.

Present study focused on improvement of the formulation of conventional hard gelatin capsules using gastric acid-resistant polymers. We have utilized the same approach of making conventional drug capsules to develop novel capsules with delayed release properties. For this purpose, delayed-release capsules were produced by improving the formulation of hard gelatin capsules. In addition, the effect of adding intestinal polymers such as Hydroxy propyl methyl cellulose phthalate, Glucomannan, and Polyvinyl alcohol to hard gelatin capsules were investigated. The capsules' release rate was determined. The degradation tests in an acidic environment were performed and the results were recorded. In fact, the delayed-release hard gelatin capsules pass through the stomach with small amount of the drug release; but their shell remains intact and dissolves as it enters the intestine environment. This article shows that enteric polymers with out interactions, only by changing the formulations will have delayed release properties. this makes sensitive drugs pass through stomach environment and have higher absorption.

A Combination of Indoor Localization and Wearable Sensor-Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results.

BACKGROUND: Health care, in recent years, has made great leaps in integrating wireless technology into traditional models of care. The availability of ubiquitous devices such as wearable sensors has enabled researchers to collect voluminous datasets and harness them in a wide range of health care topics. One of the goals of using on-body wearable sensors has been to study and analyze human activity and functional patterns, thereby predicting harmful outcomes such as falls. It can also be used to track precise individual movements to form personalized behavioral patterns, to standardize the concept of frailty, well-being/independence, etc. Most wearable devices such as activity trackers and smartwatches are equipped with low-cost embedded sensors that can provide users with health statistics. In addition to wearable devices, Bluetooth low-energy sensors known as BLE beacons have gained traction among researchers in ambient intelligence domain. The low cost and durability of newer versions have made BLE beacons feasible gadgets to yield indoor localization data, an adjunct feature in human activity recognition. In the studies by Moatamed et al and the patent application by Ramezani et al, we introduced a generic framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and extracting indoor localization using BLE beacons, in concert. OBJECTIVE: The study aimed to examine the ability of combination of physical activity and indoor location features, extracted at baseline, on a cohort of 154 rehabilitation-dwelling patients to discriminate between subacute care patients who are re-admitted to the hospital versus the patients who are able to stay in a community setting. METHODS: We analyzed physical activity sensor features to assess activity time and intensity. We also analyzed activities with regard to indoor localization. Chi-square and Kruskal-Wallis tests were used to compare demographic variables and sensor feature variables in outcome groups. Random forests were used to build predictive models based on the most significant features. RESULTS: Standing time percentage (P<.001, d=1.51), laying down time percentage (P<.001, d=1.35), resident room energy intensity (P<.001, d=1.25), resident bed energy intensity (P<.001, d=1.23), and energy percentage of active state (P=.001, d=1.24) are the 5 most statistically significant features in distinguishing outcome groups at baseline. The energy intensity of the resident room (P<.001, d=1.25) was achieved by capturing indoor localization information. Random forests revealed that the energy intensity of the resident room, as a standalone attribute, is the most sensitive parameter in the identification of outcome groups (area under the curve=0.84). CONCLUSIONS: This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features at baseline, a subset of which can better distinguish between at-risk patients that can gain independence versus the patients that are rehospitalized.

The effect of battery charge levels of Mobile phone on the amount of Electromagnetic waves emission.

PURPOSE: Over the past decades, mobile phone usage have increased dramatically. Extensive development and use of mobile telecommunication services has increased exposure to radio frequency electromagnetic waves (RF-EMW) in the daily lives of humans, and concerns about the harmful effects of mobile phones have also increased on human health. Therefore, this study aimed to investigate the effect of battery charge levels of the mobile phone on electromagnetic waves emission. METHODS: The mobile phone used in the current study was HTC One E9+ (0.181 W/kg SAR) with a non-removable battery model Li-Po 2800 mAh. The power density was measured with the mobile phone set to operate at the 2G mode by a SMP2 Portable Electromagnetic Field Monitoring System. Power density was measured in Calling mode (50 sec), Called mode (40 sec) and Talking mode (360 sec) at the battery charge levels of 1, 5, 10, 15, 20, 30, 50, 60, 70, 80 and 100%. RESULTS: In Calling mode, the maximum electromagnetic waves were determined when the mobile phone had 1% battery charge and also while it was being charged. Contrary to Calling mode, there is no statistically significant difference between the power density emitted in Called mode and Talking mode at the various battery charge levels. Power density was found to be highest in the Called mode (29.11 µw/cm2), and to be higher in the Talking mode (23.005 µw/cm2) than in the Calling mode (10.27 µw/cm2). CONCLUSIONS: The data of the present study can be used to monitor the daily exposure of mobile phone users as well as to estimate exposure levels in the laboratory and non-laboratory studies. As long as a mobile phone that is in the standby mode remains within the geographic domain of the operator's service zone, the power density emitted from that phone will be virtually zero, and any background wave can be attributed to other sources.

Quantified-Self for Obesity: Physical Activity Behaviour Sensing to Improve Health Outcomes.

Physical activity levels in bariatric patients have not been well documented, despite their importance in maintaining weight loss following surgery. This study investigated the feasibility of tracking physical activity using a smartphone app with minimal user interaction. Thus far, we have obtained good quality data from 255 patients at various points in their weight loss journey. Preliminary analyses indicate little change in physical activity levels following surgery with pre-surgery patients reaching an average of 16 minutes per day and post-surgery patients achieving a daily average of 21 minutes. Further analyses using machine-learning techniques will be conducted to determine whether physical activity is a critical factor in distinguishing between successful and unsuccessful weight loss outcomes and in the resolution of comorbid conditions in patients with similar clinical profiles.