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Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Lymph node status is a well-established prognostic factor for colon cancer, but the optimal number of nodes for accurate staging remains unclear. This study explored the relationship between lymph node yield (LNY) and 5-year mortality rates in colon cancer patients in New Zealand. Data from the New Zealand Cancer Registry were retrospectively analyzed for patients with TNM stage I, II, and III colon cancer between August 2003 and December 2021, with follow-up until January 2024. The primary outcome was the 5-year all-cause mortality rate, with LNY, age, sex, ethnicity, tumor site, district health board (DHB), and the number of positive nodes as covariates. Statistical analyses included univariate analysis, Cox regression modeling, and chi-squared tests. LNY was a significant predictor of 5-year mortality risk (hazard ratio 0.985, p < 0.0001), adjusted for age, sex, ethnicity, tumor site, and DHB. The strongest association between LNY and mortality rate was observed at 12 nodes. Further increases in LNY beyond 22 nodes did not lead to statistically significant differences in mortality rates. Lymph node ratio (LNR) was strongly associated with survival in stage III colon cancer, independent of LNY and the number of positive nodes. Higher LNY is significantly associated with reduced 5-year mortality rates in stage I-III colon cancer up to the 22-node mark. The strong correlation between LNR and mortality highlights its potential value for improving treatment planning in future clinical practice.

pH sensors are increasingly being utilized in the biomedical field and have been implicated in health applications that aim to improve the monitoring and treatment of patients. In this work, a previously developed Titanium Nitride (TiN) solid-state pH sensor is further enhanced, with the potential to be used for pH regulation inside the human body and for other biomedical, industrial, and environmental applications. One of the main limitations of existing solid-state pH sensors is their reduced performance in high redox mediums. The potential shift E0 value of the previously developed TiN pH electrode in the presence of oxidizing or reducing agents is 30 mV. To minimize this redox shift, a Nafion-modified TiN electrode was developed, tested, and evaluated in various mediums. The Nafion-modified electrode has been shown to shift the E0 value by only 2 mV, providing increased accuracy in highly redox samples while maintaining acceptable reaction times. Overcoming the redox interference for pH measurement enables several advantages of the Nafion-modified TiN electrode over the standard pH glass electrode, implicating its use in medical diagnosis, real-time health monitoring, and further development of miniaturized smart sensors.

The pressures Asian Americans feel to be socially and economically exceptional include an unspoken mandate to always be healthy.Nowhere is this more evident than in the expectation for Asian Americans to enter the field of medicine, principally as providers of care rather than those who require care.

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission can improve a device’s battery life via an inference algorithm. Furthermore, this approach creates issues for improving transmission metrics related to accuracy and efficiency, which are traded-off against each other, with increasing accuracy reducing efficiency. This paper demonstrates that machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The results showed that the DLSA provides the best performance, with an efficiency of 3.33 times for reduced sample data size and an accuracy of 95.6%, with similar accuracies observed in seven different sampling cases adopted for testing that demonstrate improved efficiency. This proposed method significantly improve both metrics using ML without sacrificing one metric over the other compared to existing methods with high efficiency.

Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices.

In the Edoxaban Versus Enoxaparin–Warfarin in Patients Undergoing Cardioversion of Atrial Fibrillation (ENSURE-AF) study (NCT 02072434), edoxaban was compared with enoxaparin–warfarin in 2,199 patients undergoing electrical cardioversion of nonvalvular atrial fibrillation (AF). In this multicenter prospective randomized open blinded end-point trial, we analyzed patients randomized to enoxaparin–warfarin. We determined time to achieve therapeutic range (TtTR); time in therapeutic range (TiTR); their clinical determinants; relation to sex, age, medical history, treatment, tobacco use, race risk (SAMe-TT2R2) score; and impact on primary end points (composite of stroke, systemic embolic event[SEE], myocardial infarction [MI], and cardiovascular death [CVD] and composite of major + clinically relevant nonmajor bleeding). Among 1,104 patients randomized to enoxaparin–warfarin, 27% were naïve to oral anticoagulants. Mean age was 64.2 ± 11 years and mean congestive heart failure, hypertension, age ≥75 (doubled), diabetes mellitus, prior stroke or transient ischemic attack (doubled), vascular disease, age 65–74, female (CHA2DS2-VASc) score was 2.6. Mean TtTR was 7.7 days (median 7 days) and mean TiTR after reaching an international normalized ratio of 2.0 to 3.0 was 71%. In 695 patients who had an INR <2.0 before the first dose and who reached an INR ≥2.0, 436 had a SAMe-TT2R2 score ≤2 and 259 had a score >2. On multivariate regression, an independent predictor of extended TtTR was creatinine clearance (p = 0.02). TtTR was marginally related to stroke/SEE/MI/CVD (p = 0.06; odds ratio  0.23, 95% confidence interval 0.02 to 1.17) but not to any bleeding. Independent predictors of TiTR were previous vitamin K antagonist experience (p <0.01) and low hypertension, abnormal renal or liver function, stroke, bleeding, labile INRs, age >65, concomitant drugs or alcohol (HAS-BLED) score (p = 0.02). TiTR was related to any bleeding (p = 0.02; odds ratio  0.39, 95% confidence interval 0.16 to 0.88), but not stroke/SEE/MI/CVD. In this cohort of warfarin users with a high TiTR no difference was seen between TtTR and TiTR in relation to SAMe-TT2R2 score. In conclusion, even in this short-term study, TiTR was significantly related to bleeding events.