Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model

Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.

COMPARISON OF ILEAL CONDUIT AND TUUC: A 4 YEARS STUDY

Objective: This study aims to compare 4 years of experience of IC and TUUC in the same period and among similar experienced surgeons. Material & Methods: Between January 2016 and August 2019, 44 radical cystectomies were performed, but 4 patients were excluded due to incomplete data or who underwent neo-bladder procedures. The primary endpoint was 30 days of complication rate and intraoperative complications. Bowel movement, ambulation, and length of stay (LOS) postoperatively were followed-up over a period of 30-day postoperatively. Results: 12 male patients underwent TUUC and 24 male patients IC, while only 4 female patients underwent IC. The mean of LOS of IC was 12.72 8.6 and 10.08 3.5 for TUUC; there were no significant differences between arms. However, TUUC had lower intra-operatively bleeding (779.17 441.15 ml) compared to IC (1328.57 810.40 ml). There was no difference in early complications between arms. Conclusion: Our results suggest that TUU with UC diversion may be used as a viable option of urinary diversion in radical cystectomy. This technique provides similar safety both surgically and oncologically.

Ubiquitous Usage of Over the Counter Medications in Pregnancy: Perspectives of Maternal Apprehensiveness

The World Health Organization delineates self-medication as the utilization of medications by individuals in search of treating symptoms or self-diagnosed health state. During pregnancy, drug utilization is complicated because of incomplete data as clinical trials frequently don't enclose pregnant women, with reference to benefits and implicit undesirable outcomes on both the mother and the foetus. Bestowed limited data on the variety of over the counter medications applicable, physi­cians seek to counsel pregnant women about implicit pitfalls, and it is beneficial to give information on entire over the counter medications the patient is receiving at the preconception visit and entire distinctive ordinary visits. Antacid that containing sodium bicarbonate can cause fluid buildup in the tissues if used during pregnancy redundantly. Hypericum perforatum is ordinarily not recommended in preg­nancy because of a dearth of human data and it perhaps antecedent miscarriage and it also escalates the birth deformities of fetus. Early aspirinusage at the time of conception or in the 1st several weeks of pregnancy does not escalate the pitfall of spontaneous abortion.

Clustering with Missing Features: A Density-Based Approach

Density clustering has been widely used in many research disciplines to determine the structure of real-world datasets. Existing density clustering algorithms only work well on complete datasets. In real-world datasets, however, there may be missing feature values due to technical limitations. Many imputation methods used for density clustering cause the aggregation phenomenon. To solve this problem, a two-stage novel density peak clustering approach with missing features is proposed: First, the density peak clustering algorithm is used for the data with complete features, while the labeled core points that can represent the whole data distribution are used to train the classifier. Second, we calculate a symmetrical FWPD distance matrix for incomplete data points, then the incomplete data are imputed by the symmetrical FWPD distance matrix and classified by the classifier. The experimental results show that the proposed approach performs well on both synthetic datasets and real datasets.

Intermittent abdominal pain in IgA vasculitis

ABSTRACT Objective: To assess intermittent abdominal pain in IgA vasculitis patients and its relation to demographic data, clinical manifestations and treatments. Methods: A retrospective cohort study included 322 patients with IgA vasculitis (EULAR/PRINTO/PRES criteria) seen at the Pediatric Rheumatology Unit in the last 32 years. Sixteen patients were excluded due to incomplete data in medical charts. Intermittent abdominal pain was characterized by new abdominal pain after complete resolution in the first month of disease. Results: Intermittent abdominal pain was observed in 35/306 (11%) IgA vasculitis patients. The median time between first and second abdominal pain was 10 days (3–30 days). The main treatment of intermittent abdominal pain included glucocorticoid [n=26/35 (74%)] and/or ranitidine [n=22/35 (63%)]. Additional analysis showed that the frequency of intermittent purpura/petechiae (37 vs. 21%; p=0.027) and the median of purpura/petechiae duration [20 (3–90) vs. 14 (1–270) days; p=0.014] were significantly higher in IgA vasculitis patients with intermittent abdominal pain compared to those without. Gastrointestinal bleeding (49 vs. 13%; p<0.001), nephritis (71 vs. 45%; p=0.006), glucocorticoid (74 vs. 44%; p=0.001) and intravenous immunoglobulin use (6 vs. 0%; p=0.036) were also significantly higher in the former group. The frequency of ranitidine use was significantly higher in IgA vasculitis patients with intermittent abdominal pain versus without (63 vs. 28%; p<0.001), whereas the median of ranitidine duration was reduced in the former group [35 (2–90) vs. 60 (5–425) days; p=0.004]. Conclusions: Intermittent abdominal pain occurred in nearly a tenth of IgA vasculitis patients, in the first 30 days of disease, and was associated with other severe clinical features. Therefore, this study suggests that these patients should be followed strictly with clinical and laboratorial assessment, particularly during the first month of disease course.

Conscious Machines for Autonomous Agents and Cybersecurity

<div>Although consciousness has been difficult to define, most researchers in artificial intelligence would agree that AI systems to date have not exhibited anything resembling consciousness. But is a conscious machine possible in the near future? I suggest that a new definition of consciousness may provide a basis for developing a conscious machine. The key is pattern recognition of correlated events in time, leading to the identification of a unified self-agent. Such a conscious system can create a simplified virtual environment, revise it to reflect updated sensor inputs, and partition the environment into self, other agents, and relevant objects. It can track recent time sequences of events, predict future events based on models and patterns in memory, and attribute causality to events and agents. It can make rapid decisions based on incomplete data, and can dynamically learn new responses based on appropriate measures of success and failure. The central aspect of consciousness is the generation of a dynamic narrative, a real-time model of a self-agent pursuing goals in a virtual reality. A conscious machine of this type may be implemented using an appropriate neural network linked to episodic memories. Near-term applications may include autonomous vehicles and online agents for cybersecurity.</div><div>Paper presented at virtual IEEE International Conference on Rebooting Computing (ICRC), Nov. 2021. To be published in conference proceedings 2022.</div>