The Significance of Machine Learning in Clinical Disease Diagnosis: A Review

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics.Comment: 8 page

Progression and Challenges of IoT in Healthcare: A Short Review

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.Comment: 7 page

Relation of radial artery occlusion after trans-radial percutaneous coronary intervention with the duration of hemostatic compression

Background: Trans-radial percutaneous coronary intervention (PCI) in cardiac procedures accesses coronary arteries through the wrist's radial artery. Post-PCI, hemostatic compression on the radial artery prevents bleeding and aids healing. Radial artery occlusion (RAO), a possible complication, involves blockage of the radial artery. This study aimed to assess the relationship between radial artery occlusion after trans-radial percutaneous coronary intervention with the duration of hemostatic compression. Methods: This prospective observational study was conducted in the Department of Cardiology, National Institute of Cardiovascular Diseases (NICVD), Dhaka, Bangladesh, spanning from September 2018 to August 2019. The study enrolled 140 patients who underwent percutaneous coronary intervention (PCI) through the trans-radial approach (TRA), randomly assigned to two groups: Group I (2-hour hemostatic compression after PCI) and Group II (6-hour hemostatic compression post-procedure). Data analysis was performed using SPSS version 23.0. Results: In this study, early radial artery occlusion was observed in 4.3% of patients in group I and 12.8% in group II (P=0.04), while late radial artery occlusion occurred in 2.8% of patients in group I and 11.4% in group II, with a statistically significant difference (P=0.04). Multivariate logistic regression analysis identified a 6-hour hemostatic compression duration (P=0.01), post-procedural nitroglycerine use (P=0.03), and procedure time (P=0.03) as predictors of radial artery occlusion. Conclusions: Reduced hemostatic compression duration is linked to a decreased occurrence of both early and late radial artery occlusion following trans-radial intervention

Study on clinical features and factors associated with thickness of chronic subdural hematoma in adult

Patients with chronic subdural hematoma encounter certain difficulties in diagnosis, especially in elderly, due to the characteristically non-specific symptoms and signs. Early diagnosis and proper operative treatment, on the other hand, results in complete recovery in most of the cases. In this study, the clinical features and factors of 31 patients with chronic subdural hematoma, associated with the thickness of chronic subdural hematoma were analyzed. The mean age was 62 ± 13.9 years. The maximum hematoma thickness in the axial CT scan was 25 mm. The thickness of hematoma obtained from axial plain CT had a positive relationship with the patient’s age where r=0.895 and p<0.001 signifies that the thickness of hematoma increased with the increasing age. But the hematoma thickness was not related to co-morbidity such as diabetes mellitus, hypertension and ischemic heart disease. The presentation of the patient with higher hematoma thickness with hemiparesis was statistically significant and with lower thickness with headache and vomiting

Study on clinical features and factors associated with thickness of chronic subdural hematoma in adult

Patients with chronic subdural hematoma encounter certain difficulties in diagnosis, especially in elderly, due to the characteristically non-specific symptoms and signs. Early diagnosis and proper operative treatment, on the other hand, results in complete recovery in most of the cases. In this study, the clinical features and factors of 31 patients with chronic subdural hematoma, associated with the thickness of chronic subdural hematoma were analyzed. The mean age was 62 ± 13.9 years. The maximum hematoma thickness in the axial CT scan was 25 mm. The thickness of hematoma obtained from axial plain CT had a positive relationship with the patient’s age where r=0.895 and p<0.001 signifies that the thickness of hematoma increased with the increasing age. But the hematoma thickness was not related to co-morbidity such as diabetes mellitus, hypertension and ischemic heart disease. The presentation of the patient with higher hematoma thickness with hemiparesis was statistically significant and with lower thickness with headache and vomiting

Integrating Machine Learning And Simulation For Resource Planning Of Hospital Systems Based On Predicted Length Of Stay

Recently Hospital Systems faced a high invasion of patients generated by several events such as health crisis related epidemic (COVID, FLU) or seasonal flows. Hence, managing hospital bed availability and efficiency with proper care is obligatory for addressing the challenges associated with the overburden of patients. However, the Length of stay (LOS) is often increased due to the high patient influx and overcrowding problem occurs within the Hospital. It resolves these issues, it is essential for hospital authority to predict the Patients LOS which is the crucial indicator for the use of medical resources (allocation, utilization of providers and resource) and assessing the overcrowding within the hospital premises. Thus, accurate LOS and proper resource management is indispensable for hospital authority to ensure the maximum profit with optimized system utilization. This Study proposes a Machine Learning driven approach integrated with Simulation Software for the prediction of LOS and resource management within the hospital System. Artificial Neural Network is used for predicting the LOS in the simulation environment that learns the pertinent information from nonlinear and linear processes without prior assumption on data distribution and substantially boosts the prediction accuracy. Dynamic resources Planning is also integrated within this model that allows the management to make plans on the very first day for hospital resources based on Predicted LOS for the next few days as required. Most importantly, in this proposed Machine Learning Based Data-driven simulation method, Patients are generated randomly from a Dynamic simulation environment with different disease attributes and the simulation Predicts the LOS as per trained model in Brain. Based on this LOS, authorities can make decisions regarding the Bed allocation, Doctors requirements and Patients satisfactions for proper treatment in the hospital System. Hence this model significantly helps healthcare professionals and patients care to manage their resources and increases the patientâ??s satisfaction allowing early treatment and dynamic resource planning alongside with finance

Clozapine Can Be the Good Option in Resistant Mania

Bipolar mood disorder is a mental disorder with a lifetime prevalence rate of about 1% in the general population and there are still a proportion of individuals who suffer from bipolar mood disorders that are resistant to standard treatment. Reporting clozapine responsive mania that was not responding to two previous consecutive atypical antipsychotics and one typical antipsychotic was aimed at. A 17-year-old male manic patient was admitted into the psychiatry inpatient department and was nonresponsive to Risperidone 12 mg daily for 4 weeks, Olanzapine 30 mg daily for 3 weeks, and Haloperidol 30 mg daily for 3 weeks, along with valproate preparation 1500 mg daily. He was started on clozapine as he was nonresponsive to Lithium in previous episodes and did not consent to starting Electroconvulsive Therapy (ECT). He responded adequately to 100 mg clozapine and 1500 mg valproate preparation and remission happened within 2 weeks of starting clozapine. Clozapine can be a good option for resistant mania and further RCT based evidences will strengthen the options in treating resistant mania

Varietal improvement options for higher rice productivity in salt affected areas using crop modelling

The rice model ORYZA v3 has been recently improved to account for salt stress effect on rice crop growth and yield. This paper details subsequent studies using the improved model to explore opportunities for improving salinity tolerance in rice. The objective was to identify combinations of plant traits influencing rice responses to salinity and to quantify yield gains by improving these traits. The ORYZA v3 model was calibrated and validated with field experimental data collected between 2012 and 2014 in Satkhira, Bangladesh and Infanta, Quezon, Philippines, then used for simulations scenario considering virtual varieties possessing different combinations of crop model parameter values related to crop salinity response and the soil salinity dynamic observed at Satkhira site. Simulation results showed that (i) short duration varieties could escape end of season increase in salinity, while long duration varieties could benefit from an irrigated desalinization period occurring during the later stages of crop growth in the Satkhira situation; (ii) combining short duration growth with salt tolerance (bTR and bPN) above 12 dS m(-1) and a resilience trait (aSalt) of 0.11 in a variety, allows maintenance of 65-70% of rice yield under increasing salinity levels of up to 16 dS m(-1); and (iii) increasing the value of the tolerance parameter b by 1% results in 0.3-0.4% increase in yield. These results are relevant for defining directions to increase rice productivity in saline environments, based on improvements in phenology and quantifiable salt tolerance traits

The impact of irrigation return flow on seasonal groundwater recharge in northwestern Bangladesh

Abstract Irrigation is vital in Bangladesh in order to meet the growing food demand as a result of the increasing population. During the dry season, groundwater irrigation is the main source of water for agriculture. However, excessive abstraction of groundwater for irrigation causes groundwater level depletion. At the same time, the loss from excessive irrigation could end up contributing to aquifer recharge as return flow. Therefore, investigating the influence of irrigation on groundwater is important for the sustainable management of this resource. This study aims to assess the impact of irrigation on groundwater recharge in the northwest Rajshahi district in Bangladesh. A semi-physically based water balance model was used to simulate spatially distributed groundwater recharge with two scenarios (with and without irrigation). To evaluate the effect of irrigation, groundwater recharges from these two scenarios were compared. The result showed that the use of groundwater for irrigation increased over the study period whereas, there was a persistent trend of decrease in groundwater level during the study period. Groundwater provides 91% of overall irrigation in the study area. However, on average, about 33% of the total irrigation becomes return flow and contributes to groundwater recharge in the dry season. Irrigation return flow is around 98% of the total recharge during the dry season in this region. The spatially distributed seasonal return flow varies from 305 to 401 mm. In brief, irrigation has a significant role in groundwater recharge in the study area during the dry season. Hence, proper irrigation water measurement and management are necessary for sustainable groundwater resource management in this region