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

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

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

The impact of land use and land cover change on groundwater recharge in northwestern Bangladesh

Abstract Groundwater recharge is affected by various anthropogenic activities, land use and land cover (LULC) change among these. The long-term temporal and seasonal changes in LULC have a substantial influence on groundwater flow dynamics. Therefore, assessment of the impacts of LULC changes on recharge is necessary for the sustainable management of groundwater resources. The objective of this study is to examine the effects of LULC changes on groundwater recharge in the northwestern part of Bangladesh. Spatially distributed monthly groundwater recharge was simulated using a semi-physically based water balance model. Long-term temporal LULC change analysis was conducted using LULC maps from 2006 to 2016, while wet and dry LULC maps were used to examine seasonal changes. The results show that the impervious built-up area has increased by 80.3%, whereas vegetated land cover has decreased by 16.4% over the study period. As a result, groundwater recharge in 2016 has decreased compared to the level seen in 2006. However, the decrease in recharge due to long-term temporal LULC changes is very small at the basin scale (2.6 mm/year), although the impact on regional level is larger (17.1 mm/year) due to urbanization. Seasonal LULC variations also affect recharge due to the higher potential for dry seasonal LULC compared to the wet seasonal LULC, a substantial difference (20.6 mm/year). The results reveal important information about the groundwater system and its response to land cover changes in northwestern Bangladesh