Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit

Multiple organ failures are the main cause of mortality and morbidity in the intensive care unit (ICU). The progression of organ failures in the ICU is usually monitored using the Sequential Organ Failure Assessment (SOFA) score. This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. Ninety-eight ICU patients’ data were obtained retrospectively from Universiti Malaya Medical Centre for analysis. Several machine learning algorithms which are decision tree, linear discriminant, naïve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for the classification. The classifiers were trained on 80% of the patients with 10-fold cross-validations and assessed on 20% of patients using 34 variables in the ICU. The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. Meanwhile, the AdaBoost algorithm achieved 99.1% sensitivity in the testing dataset. This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. The feature selection shows respiratory rate and mean arterial pressure (MAP) as the most important variables using chi-square test while insulin and fraction of oxygenated hemoglobin are the most important predictors by the mutual information test

Model-Based Glycaemic Control in Multicentre ICUs within Diabetic Patients: In-silico Analysis

Sliding-scale insulin therapy has been vastly used for glycaemic control but dysglycaemia remains high. Model-based glycaemic control that incorporates insulin nutrition protocol was proposed as this therapy provides personalized care to avoid dysglycaemia. Thus, this paper aims to implement in-silico simulation and identify which model-based control protocols yield better protocol within ICU diabetic patients based on performance and safety. Multicentre ICU patients of 282 were divided into diabetes mellitus (DM) and non-diabetes mellitus (NDM) cohort where in-silico simulations were done using Specialised Relative Insulin Nutrition Therapy (SPRINT), SPRINT+Glargine and Stochastic Targeted (STAR) protocols. Performance was verified based on the percentage of blood glucose (BG) time in band (TIB) 6.0 – 10.0 mmol/L and safety with number of mild and severe hypoglycaemia episodes. Among the three protocols, STAR protocol showed the highest median and interquartile range % BG TIB 6.0 – 10.0 mmol/L for DM and NDM patients with 71.6 % [57.9 – 79.8] and 77.4 % [62.9 – 88.8]. The number of hypoglycaemia episodes are the lowest in DM and NDM patients too compared to other protocols. These advantages show that STAR protocol can provide better patient outcomes for glycaemic control with personalized care

Virtual trial of glycaemic control performance and nursing workload assessment in diabetic critically ill patients

Tight glycaemic control in critically ill patients is used to reduce mortality in intensive care units. However, its usage is debatable in reducing hypoglycaemia or accurately maintain normoglycaemia level. This paper presents the assessment for two 'wider' Stochastic TARgeted (STAR) glycemic controllers, namely Controller A (blood glucose (BG) target 4.4-8.0 mmol/L) and Controller B (BG target 4.4-10.0 mmol/L) with 1 to 3 hour nursing interventions. These controllers were assessed to determine the better control on diabetic and non-diabetic patients. 66 diabetic and 66 non-diabetic critically ill patient's data from Hospital Tunku Ampuan Afzan (HTAA) were employed for virtual trial simulations with a clinically validated physiological model. Performance metrics were assessed within the percentage time in band (TIB) of 4.4 to 8.0 mmol/L, 4.4 to 10.0 mmol/L, and 6.0 to 10.0 mmol/L. Controller A shows better performance in normoglycaemic TIB of 4.4 to 10.0 mmol/L where non-diabetic and diabetic patients achieved 92.5% and 83.8% respectively. In conclusion, Controller A is higher in efficiency and safer to be used for both patients cohorts. However, higher clinical interventions in diabetic patients within this control raise the alarm to reduce nursing workload. This is believed to improve clinical interventions burnout and ensure patient's comfortability. © 2018 Authors

Model-based glycemic control in a Malaysian intensive care unit: performance and safety study

Background: Stress-induced hyperglycemia is common in critically ill patients. A few forms of model-based glycemic control have been introduced to reduce this phenomena and among them is the automated STAR protocol which has been used in the Christchurch and Gyulá hospitals’ intensive care units (ICUs) since 2010. Methods: This article presents the pilot trial assessment of STAR protocol which has been implemented in the International Islamic University Malaysia Medical Centre (IIUMMC) Hospital ICU since December 2017. One hundred and forty-two patients who received STAR treatment for more than 20 hours were used in the assessment. The initial results are presented to discuss the ability to adopt and adapt the model-based control framework in a Malaysian environment by analyzing its performance and safety. Results: Overall, 60.7% of blood glucose measurements were in the target band. Only 0.78% and 0.02% of cohort measurements were below 4.0 mmol/L and 2.2 mmol/L (the limitsfor mild and severe hypoglycemia, respectively). Treatment preference-wise, the clinical staff were favorable of longer intervention options when available. However, 1 hourly treatments were still used in 73.7% of cases. Conclusion: The protocol succeeded in achieving patient-specific glycemic control while maintaining safety and was trusted by nurses to reduce workload. Its lower performance results, however, give the indication for modification in some of the control settings to better fit the Malaysian environment. © 2019 Abu-Samah et al

Virtual trial of glycaemic control performance and nursing workload assessment in diabetic critically ill patients

Tight glycaemic control in critically ill patients is used to reduce mortality in intensive care units. However, its usage is debatable in reducing hypoglycaemia or accurately maintain normoglycaemia level. This paper presents the assessment for two ‘wider’ Stochastic TARgeted (STAR) glycemic controllers, namely Controller A (blood glucose (BG) target 4.4-8.0 mmol/L) and Controller B (BG target 4.4-10.0 mmol/L) with 1 to 3 hour nursing interventions. These controllers were assessed to determine the better control on diabetic and non-diabetic patients. 66 diabetic and 66 non-diabetic critically ill patient’s data from Hospital Tunku Ampuan Afzan (HTAA) were employed for virtual trial simulations with a clinically validated physiological model. Performance metrics were assessed within the percentage time in band (TIB) of 4.4 to 8.0 mmol/L, 4.4 to 10.0 mmol/L, and 6.0 to 10.0 mmol/L. Controller A shows better performance in normoglycaemic TIB of 4.4 to 10.0 mmol/L where non-diabetic and diabetic patients achieved 92.5% and 83.8% respectively. In conclusion, Controller A is higher in efficiency and safer to be used for both patients cohorts. However, higher clinical interventions in diabetic patients within this control raise the alarm to reduce nursing workload. This is believed to improve clinical interventions burnout and ensure patient’s comfortability

Assessment of glycemic control protocol (STAR) through compliance analysis amongst Malaysian ICU patients

Purpose: This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage. Patients and Methods: STAR proposes 1– 3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed. Results: The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance. Conclusion: The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions

Classification of translational landslide activity using vegetation anomalies indicator (VAI) in Kundasang, Sabah

This paper introduced a novel method of landslide activity mapping using vegetation anomalies indicators (VAIs) obtained from high resolution remotely sensed data. The study area was located in a tectonically active area of Kundasang, Sabah, Malaysia. High resolution remotely sensed data were used to assist manual landslide inventory process and production on VAIs. The inventory process identified 33, 139, and 31 of active, dormant, and relict landslides, respectively. Landslide inventory map were randomly divided into two groups for training (70%) and validation (30%) datasets. Overall, 7 group of VAIs were derived including (i) tree height irregularities, (ii) tree canopy gap, (iii) density of different layer of vegetation, (iv) vegetation type distribution, (v) vegetation indices (VIs), (vi) root strength index (RSI), and (vii) distribution of water-loving trees. The VAIs were used as the feature layer input of the classification process with landslide activity as the target results. The landslide activity of the study area was classified using support vector machine (SVM) approach. SVM parameter optimization was applied by using Grid Search (GS) and Genetic Algorithm (GA) techniques. The results showed that the overall accuracy of the validation dataset is between 61.4-86%, and kappa is between 0.335-0.769 for deep-seated translational landslide. SVM RBF-GS with 0.5m spatial resolution produced highest overall accuracy and kappa values. Also, the overall accuracy of the validation dataset for shallow translational is between 49.8-71.3%, and kappa is between 0.243-0.563 where SVM RBF-GS with 0.5m resolution recorded the best result. In conclusion, this study provides a novel framework in utilizing high resolution remote sensing to support labour intensive process of landslide inventory. The nature-based vegetation anomalies indicators have been proved to be reliable for landslide activity identification in Malaysia

Data hiding in medical images using encryption and steganography techniques

This paper presents securing the transmission of medical images over the Internet. The presented algorithms will be applied to medical data. This work presents a method that combines encryption, steganography and compression techniques for data transmission purpose. In this paper, for the sender side, the secret text will be encrypted using Advanced Encryption Standard algorithm and embedded into the chosen cover image using Least Significant Bit algorithm for more security. A stego-image will be generated and compressed using Discrete Cosine Transform to reduce the size of stego- image in order to save storage. In the receiver side, the inverse methods will be implemented in reverse order to get the cover image along with the secret text. MATLAB simulation tool is used to implement the chosen technique and also for further development. In the end of this research, the performance of the algorithm will be assessed in the aspect of mean square error and peak signal noise ratio to compare the results of the chosen method

The issues in academic library / Noor Farahain Abdul Razak and Nur Athirah Hanani Jailani

The academic libraries need to change the user perception about the libraries which is more to bad perception. Hence, to change that perception the academic libraries need to identify the issues that always occur in the libraries because it can easier the analysis process. Therefore, by doing the analysis, the libraries will know their problem such as lack in management and easier for them to overcome it. The first issue that always occurs in the academic library is about the collections. The other issue is the lack of the facilities. Besides that, the academic libraries do not develop the strategic planning. Furthermore, by developing the strategic planning, it can make the staff aware about their responsibilities, easy for the academic libraries to achieve their vision and mission and the management of the libraries will be more effective. In addition, the other issue is the relationship between the academic libraries and user is not strong enough. If the libraries want to gain a lot of benefits the director should play the important role. The customers and stakeholder also need to cooperate to ensure that all the efforts to change the library are successful

The issues in academc libraries / Nur Aliah Zainal, Noor Farahain Abdul Razak and Nur Athirah Hanani Jailan

The academic libraries need to change the user perception about the libraries which is more to bad perception. Hence, to change that perception the academic libraries need to identify the issues that always occur in the libraries because it can easier the analysis process. Therefore, by doing the analysis, the libraries will know their problem such as lack in management and easier for them to overcome it. The first issue that always occurs in the academic library is about the collections. The other issue is the lack of the facilities. Besides that, the academic libraries do not develop the strategic planning. Furthermore, by developing the strategic planning, it can make the staff aware about their responsibilities, easy for the academic libraries to achieve their vision and mission and the management of the libraries will be more effective. In addition, the other issue is the relationship between the academic libraries and user is not strong enough. If the libraries want to gain a lot of benefits the director should play the important role. The customers and stakeholder also need to cooperate to ensure that all the efforts to change the library are successful