Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.

BACKGROUND: Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs). This would require researchers and clinicians to manually navigate through extensive amounts of unstructured data, which is one of the main reasons that smoking habits are rarely integrated into larger studies. Our aim is to develop machine learning models to classify patients' smoking status from their EHRs. METHODS: This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients' smoking status and providing explanations for the decisions. The proposed NLP pipeline comprises four distinct components, which are; (1) considering preprocessing techniques to address abbreviations, punctuation, and other textual irregularities, (2) four cutting-edge feature extraction techniques, i.e. Embedding, BERT, Word2Vec, and Count Vectorizer, employed to extract the optimal features, (3) utilization of a Stacking-based Ensemble (SE) model and a Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) for the identification of smoking status, and (4) application of a local interpretable model-agnostic explanation to explain the decisions rendered by the detection models. The EHRs of 23,132 patients with suspected lung cancer were collected from the Region of Southern Denmark during the period 1/1/2009-31/12/2018. A medical professional annotated the data into 'Smoker' and 'Non-Smoker' with further classifications as 'Active-Smoker', 'Former-Smoker', and 'Never-Smoker'. Subsequently, the annotated dataset was used for the development of binary and multiclass classification models. An extensive comparison was conducted of the detection performance across various model architectures. RESULTS: The results of experimental validation confirm the consistency among the models. However, for binary classification, BERT method with CNN-LSTM architecture outperformed other models by achieving precision, recall, and F1-scores between 97% and 99% for both Never-Smokers and Active-Smokers. In multiclass classification, the Embedding technique with CNN-LSTM architecture yielded the most favorable results in class-specific evaluations, with equal performance measures of 97% for Never-Smoker and measures in the range of 86 to 89% for Active-Smoker and 91-92% for Never-Smoker. CONCLUSION: Our proposed NLP pipeline achieved a high level of classification performance. In addition, we presented the explanation of the decision made by the best performing detection model. Future work will expand the model's capabilities to analyze longer notes and a broader range of categories to maximize its utility in further research and screening applications.

Urinary MicroRNA Biomarkers of Nephrotoxicity in Macaca fascicularis.

Drug-induced kidney injury (DIKI) refers to kidney damage resulting from the administration of medications. The aim of this project was to identify reliable urinary microRNA (miRNAs) biomarkers that can be used as potential predictors of DIKI before disease diagnosis. This study quantified a panel of six miRNAs (miRs-210-3p, 423-5p, 143-3p, 130b-3p, 486-5p, 193a-3p) across multiple time points using urinary samples from a previous investigation evaluating effects of a nephrotoxicant in cynomolgus monkeys. Exosome-associated miRNA exhibited distinctive trends when compared to miRNAs quantified in whole urine, which may reflect a different urinary excretion mechanism of miRNAs than those released passively into the urine. Although further research and mechanistic studies are required to elucidate how these miRNAs regulate signaling in disease pathways, we present, for the first time, data that several miRNAs displayed strong correlations with histopathology scores, thus indicating their potential use as biomarkers to predict the development of DIKI in preclinical studies and clinical trials. Also, these findings can potentially be translated into other non-clinical species or human for the detection of DIKI.

The efficacy of curcumin supplementation on serum total antioxidant capacity, malondialdehyde, and disease activity in women with rheumatoid arthritis: A randomized, double-blind, placebo-controlled clinical trial.

Oxidative stress plays a crucial role in the physiopathology of rheumatoid arthritis (RA), which is associated with impaired antioxidant defenses. This study aimed to investigate the effects of curcumin supplementation on serum levels of total antioxidant capacity (TAC), malondialdehyde (MDA), and disease activity in women with RA. In this clinical trial, 48 women with RA were treated with one capsule of curcumin (500 mg daily) or placebo for 8 weeks. Anthropometric measurements and fasting blood samples were collected at baseline and end of the study. Finally, we assessed the Disease Activity Score in 28 joints (DAS-28), dietary intake, and physical activity levels. While curcumin supplementation for 8 weeks significantly increased the serum levels of TAC (p < 0.05), it decreased tender joint counts, swollen joint counts, visual analog scale (VAS) for pain, and DAS-28 compared to the placebo at the end of the study (p < 0.001 for all). MDA levels significantly decreased in the curcumin group (p < 0.05). However, changes in MDA concentration were not significant between groups at the end of the trial (p = 0.145). Curcumin supplementation had a beneficial effect on increasing the serum levels of TAC and decreased DAS-28 in women with RA.

AUD-DSS: a decision support system for early detection of patients with alcohol use disorder.

BACKGROUND: Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems. Many individuals suffering from AUD never undergo specialist treatment during their addiction due to obstacles such as taboo and the poor performance of current screening tools. Therefore, there is a lack of rapid intervention. This can be mitigated by the early detection of patients with AUD. A clinical decision support system (DSS) powered by machine learning (ML) methods can be used to diagnose patients' AUD status earlier. METHODS: This study proposes an effective AUD prediction model (AUDPM), which can be used in a DSS. The proposed model consists of four distinct components: (1) imputation to address missing values using the k-nearest neighbours approach, (2) recursive feature elimination with cross validation to select the most relevant subset of features, (3) a hybrid synthetic minority oversampling technique-edited nearest neighbour approach to remove noise and balance the distribution of the training data, and (4) an ML model for the early detection of patients with AUD. Two data sources, including a questionnaire and electronic health records of 2571 patients, were collected from Odense University Hospital in the Region of Southern Denmark for the AUD-Dataset. Then, the AUD-Dataset was used to build ML models. The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. Finally, a combination of all these models in an ensemble learning approach was selected for the AUDPM. RESULTS: The results revealed that the proposed ensemble AUDPM outperformed other single models and our previous study results, achieving 0.96, 0.94, 0.95, and 0.97 precision, recall, F1-score, and accuracy, respectively. In addition, we designed and developed an AUD-DSS prototype. CONCLUSION: It was shown that our proposed AUDPM achieved high classification performance. In addition, we identified clinical factors related to the early detection of patients with AUD. The designed AUD-DSS is intended to be integrated into the existing Danish health care system to provide novel information to clinical staff if a patient shows signs of harmful alcohol use; in other words, it gives staff a good reason for having a conversation with patients for whom a conversation is relevant.

Treatment with quercetin increases Nrf2 expression and neuronal differentiation of sub ventricular zone derived neural progenitor stem cells in adult rats.

BACKGROUND: The presence of neural precursor stem cells (NPSCs) in some parts of the adult brain and the potency of these types of cells with a therapeutic viewpoint, has opened up a new approach for the treatment and recovery of the defects of central nervous system (CNS). Quercetin, as an herbal flavonoid, has been extensively investigated and shown to have numerous restoratives, inhibitory, and protective effects on some cell-lines and disorders. The purpose of this study is to simultaneously investigate the effect of quercetin on the expression of the nuclear factor erythroid 2-related factor 2 (Nrf2) gene and the effect on the proliferation and differentiation of NPSCs derived from the subventricular zone (SVZ) of the brain of adult rats. METHODS AND RESULTS: The cell obtained from SVZ cultured for one week and treated with quercetin at the concentrations of 1, 5, and 15 µM to evaluate the Nrf2 expression, proliferation and differentiation of NSCs after one week. Cellular and genetic results was performed by RT-PCR, MTT assay test, quantification of images with Image-J and counting. The results indicated that the quercetin increases expression of Nrf2 at concentration above 5 µM. Also differentiation and proliferation rate of NSCs is affected by various concentrations of quercetin in a dose-dependent manner. CONCLUSION: These findings confirmed the dose-dependent effect of quercetin on proliferation and differentiation of cell. In addition, quercetin increased the expression of Nrf2 gene. By combining these two effects of quercetin, this substance can be considered an effective compound in the treatment of degenerative defects in CNS.

A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders.

Walking ability of elderly individuals, who suffer from walking difficulties, is limited, which restricts their mobility independence. The physical health and well-being of the elderly population are affected by their level of physical activity. Therefore, monitoring daily activities can help improve the quality of life. This becomes especially a huge challenge for those, who suffer from dementia and Alzheimer's disease. Thus, it is of great importance for personnel in care homes/rehabilitation centers to monitor their daily activities and progress. Unlike normal subjects, it is required to place the sensor on the back of this group of patients, which makes it even more challenging to detect walking from other activities. With the latest advancements in the field of health sensing and sensor technology, a huge amount of accelerometer data can be easily collected. In this study, a Machine Learning (ML) based algorithm was developed to analyze the accelerometer data collected from patients with walking difficulties, who live in one of the municipalities in Denmark. The ML algorithm is capable of accurately classifying the walking activity of these individuals with different walking abnormalities. Various statistical, temporal, and spectral features were extracted from the time series data collected using an accelerometer sensor placed on the back of the participants. The back sensor placement is desirable in patients with dementia and Alzheimer's disease since they may remove visible sensors to them due to the nature of their diseases. Then, an evolutionary optimization algorithm called Particle Swarm Optimization (PSO) was used to select a subset of features to be used in the classification step. Four different ML classifiers such as k-Nearest Neighbors (kNN), Random Forest (RF), Stacking Classifier (Stack), and Extreme Gradient Boosting (XGB) were trained and compared on an accelerometry dataset consisting of 20 participants. These models were evaluated using the leave-one-group-out cross-validation (LOGO-CV) technique. The Stack model achieved the best performance with average sensitivity, positive predictive values (precision), F1-score, and accuracy of 86.85%, 93.25%, 88.81%, and 93.32%, respectively, to classify walking episodes. In general, the empirical results confirmed that the proposed models are capable of classifying the walking episodes despite the challenging sensor placement on the back of the patients, who suffer from walking disabilities.

Simultaneous Online Registration-Independent Stiffness Identification and Tip Localization of Surgical Instruments in Robot-assisted Eye Surgery.

Notable challenges during retinal surgery lend themselves to robotic assistance which has proven beneficial in providing a safe steady-hand manipulation. Efficient assistance from the robots heavily relies on accurate sensing of surgery states (e.g. instrument tip localization and tool-to-tissue interaction forces). Many of the existing tool tip localization methods require preoperative frame registrations or instrument calibrations. In this study using an iterative approach and by combining vision and force-based methods, we develop calibration- and registration-independent (RI) algorithms to provide online estimates of instrument stiffness (least squares and adaptive). The estimations are then combined with a state-space model based on the forward kinematics (FWK) of the Steady-Hand Eye Robot (SHER) and Fiber Brag Grating (FBG) sensor measurements. This is accomplished using a Kalman Filtering (KF) approach to improve the deflected instrument tip position estimations during robot-assisted eye surgery. The conducted experiments demonstrate that when the online RI stiffness estimations are used, the instrument tip localization results surpass those obtained from pre-operative offline calibrations for stiffness.

Validation of the Personality Inventory for DSM-5-Brief Form (PID-5-BF) with Iranian University Students and Clinical Samples: Factor Structure, Measurement Invariance, and Convergent, Discriminant, and Known-Groups Validity.

The Personality Inventory for DSM-5, Brief Form (PID-5-BF) was developed to assess DSM-5's Alternative Trait Model for Diagnosing Personality Disorders (AMPD). This study aimed to examine the factor structure, internal consistency, measurement invariance, and convergent, discriminant, and known-group validity of the Persian PID-5-BF with 941 university students (aged 18-67, M age= 28.36, SD = 9.09, 39.1% males) and 178 male from a clinical (aged 18-60, M age= 33.77, SD = 10.60) sample in Iran. Confirmatory factor analyses supported the five-factor model in both groups, being fully and partially invariant across gender and study groups, respectively. PID-5-BF subscales were internally consistent, yielded expected associations with other personality variables, and differentiated the student sample from the clinical group, supporting the measure's convergent, discriminant, and known-group validity. Our results indicated that the PID-5-BF holds promise as a screening measure of dimensional maladaptive personality traits in Iranian students and clinical samples.

Validation of the Proposed Specifiers for Conduct Disorder (PSCD) Self-Report Version in Iranian School-Attending Adolescents.

The Proposed Specifiers for Conduct Disorder (PSCD) scale was developed to advance the study of child and adolescent psychopathy, especially as it relates to conduct disorder. This study is the first to test the factor structure, measurement invariance, internal consistency, and validity of the Persian PSCD self-report version in a gender-mixed sample of 1,506 school-attending 11 to 18 years old youth (M age = 15.23; SD = 1.83; 49.60% boys). Confirmatory factor analysis supported the proposed four-factor hierarchical structure of the PSCD, though with 19 items loaded on grandiose-manipulative, callous-unemotional, daring-impulsive, and conduct disorder components. This factor structure was also invariant across gender. The PSCD total and four components scores were internally consistent and exhibited the expected relations with proactive aggression, externalizing problems, anxiety and depression, and poor school performance, supporting the PSCD scores' convergent, discriminant, and criteria validity. The findings indicated that with some modifications, the Persian PSCD might hold promise for assessing psychopathy components in Iranian school-attending adolescents and may spark additional research in a variety of settings.

Catalytic electrodes' characterization study serving polluted water treatment: environmental healthcare and ecological risk assessment.

Pesticide residues in the environment have irreparable effects on human health and other organisms. Hence, it is necessary to treat and degrade them from polluted water. In the current work, the electrochemical removal of the fenitrothion (FT), trifluralin (TF), and chlorothalonil (CT) pesticides were performed by catalytic electrode. The characteristics of SnO2-Sb2O3, PbO2, and Bi-PbO2 electrodes were described by FE-SEM and XRD. Dynamic electrochemical techniques including cyclic voltammetry, electrochemical impedance spectroscopy, accelerated life, and linear polarization were employed to investigate the electrochemical performance of fabricated electrodes. Moreover, evaluate the risk of toxic metals release from the catalytic electrode during treatment process was investigated. The maximum degradation efficiency of 99.8, 100, and 100% for FT, TF, and CT was found under the optimal condition of FT, TF, and CT concentration 15.0 mg L-1, pH 7.0, current density 7.0 mA cm-2, and electrolysis time of 120 min. The Bi-PbO2, PbO2, and SnO2-Sb2O3 electrodes revealed the oxygen evolution potential of 2.089, 1.983, 1.914 V, and the service lifetime of 82, 144, and 323 h, respectively. The results showed that after 5.0 h of electrolysis, none of the heavy metals such as Bi, Pb, Sb, Sn, and Ti were detected in the treated solution.

Cooperation and spatial self-organization determine rate and efficiency of particulate organic matter degradation in marine bacteria.

The recycling of particulate organic matter (POM) by microbes is a key part of the global carbon cycle. This process is mediated by the extracellular hydrolysis of polysaccharides, which can trigger social behaviors in bacteria resulting from the production of public goods. Despite the potential importance of public good-mediated interactions, their relevance in the environment remains unclear. In this study, we developed a computational and experimental model system to address this challenge and studied how the POM depolymerization rate and its uptake efficiency (2 main ecosystem function parameters) depended on social interactions and spatial self-organization on particle surfaces. We found an emergent trade-off between rate and efficiency resulting from the competition between oligosaccharide diffusion and cellular uptake, with low rate and high efficiency being achieved through cell-to-cell cooperation between degraders. Bacteria cooperated by aggregating in cell clusters of ∼10 to 20 µm, in which cells were able to share public goods. This phenomenon, which was independent of any explicit group-level regulation, led to the emergence of critical cell concentrations below which degradation did not occur, despite all resources being available in excess. In contrast, when particles were labile and turnover rates were high, aggregation promoted competition and decreased the efficiency of carbon use. Our study shows how social interactions and cell aggregation determine the rate and efficiency of particulate carbon turnover in environmentally relevant scenarios.

Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods.

BACKGROUND: High dimensionality in electronic health records (EHR) causes a significant computational problem for any systematic search for predictive, diagnostic, or prognostic patterns. Feature selection (FS) methods have been indicated to be effective in feature reduction as well as in identifying risk factors related to prediction of clinical disorders. This paper examines the prediction of patients with alcohol use disorder (AUD) using machine learning (ML) and attempts to identify risk factors related to the diagnosis of AUD. METHODS: A FS framework consisting of two operational levels, base selectors and ensemble selectors. The first level consists of five FS methods: three filter methods, one wrapper method, and one embedded method. Base selector outputs are aggregated to develop four ensemble FS methods. The outputs of FS method were then fed into three ML algorithms: support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to compare and identify the best feature subset for the prediction of AUD from EHRs. RESULTS: In terms of feature reduction, the embedded FS method could significantly reduce the number of features from 361 to 131. In terms of classification performance, RF based on 272 features selected by our proposed ensemble method (Union FS) with the highest accuracy in predicting patients with AUD, 96%, outperformed all other models in terms of AUROC, AUPRC, Precision, Recall, and F1-Score. Considering the limitations of embedded and wrapper methods, the best overall performance was achieved by our proposed Union Filter FS, which reduced the number of features to 223 and improved Precision, Recall, and F1-Score in RF from 0.77, 0.65, and 0.71 to 0.87, 0.81, and 0.84, respectively. Our findings indicate that, besides gender, age, and length of stay at the hospital, diagnosis related to digestive organs, bones, muscles and connective tissue, and the nervous systems are important clinical factors related to the prediction of patients with AUD. CONCLUSION: Our proposed FS method could improve the classification performance significantly. It could identify clinical factors related to prediction of AUD from EHRs, thereby effectively helping clinical staff to identify and treat AUD patients and improving medical knowledge of the AUD condition. Moreover, the diversity of features among female and male patients as well as gender disparity were investigated using FS methods and ML techniques.

Effects of curcumin supplementation on metabolic parameters, inflammatory factors and obesity values in women with rheumatoid arthritis: A randomized, double-blind, placebo-controlled clinical trial.

Rheumatoid arthritis (RA) is a chronic inflammatory disease that leads to cartilage damage with mostly accompanied by metabolic disorders. This study aimed to investigate the effects of curcumin supplementation on metabolic parameters (lipid profile and glycemic indices), inflammatory factors, visfatin levels, and obesity values in women with RA. This randomized, double-blind, placebo-controlled clinical trial was conducted on 48 women with RA. The patients were treated with curcumin (500 mg once a day) or placebo for 8 weeks. Fasting blood samples, anthropometric measurements, dietary intakes, and physical activity levels of subjects were collected at baseline and the end of the study. Curcumin supplementation significantly decreased homeostatic model assessment for insulin resistance (HOMA-IR), erythrocyte sedimentation rate, serum levels of high-sensitivity C-reactive protein and triglycerides, weight, body mass index, and waist circumference of patients compared with the placebo at the end of the study (p < .05 for all). HOMA-IR and triglyceride levels significantly increased within the placebo group. Changes in fasting blood sugar, insulin, other lipids profile, and visfatin levels were not significant in any of the groups (p > .05). These results support the consumption of curcumin, as a part of an integrated approach to modulate metabolic factors, inflammation, and adiposity in women with RA.

Health risk assessment of inhalational exposure to heavy metals in drivers working in an urban desert city in the Middle East.

Heavy metals are threatening the lives of people around the world. This study aims to quantify the adverse health risks of seven heavy metals, including arsenic, cadmium, cobalt, chromium, lead, manganese, and nickel in taxi drivers in an urban desert city, Yazd, Iran. The exposure concentrations were determined through air sampling in the breathing zone of 40 randomly selected intercity taxi drivers, 20 in winter and 20 in summer, in 2019. An ICP-MAS spectrometer was applied to measure the elements. Target hazard quotient (THQ) and excessive cancer risk (ECR) indices were applied to calculate the non-cancer and cancer risks based on the United States Environmental Protection Agency (USEPA) guidelines, respectively. The results showed that arsenic and lead had the highest exposure concentrations among the seven measured heavy metals while cobalt and chromium metals had the lowest concentrations. Arsenic, cadmium, manganese, and nickel would probably cause some adverse non-carcinogenic health problems (THQ > 1) in the drivers over their working life. The percentile 95% ECR of measured heavy metals was 1.3E - 03 in total, which is much higher than the value of 1E - 06. The concentration of arsenic and nickel was higher in winter than in summer. Taxi drivers in Yazd city are at considerable health risk; therefore, swift and serious controlling measures should be taken by responsible authorities. Besides, the taxi drivers should be educated about heavy metals' health effects and their protective behaviors.

Cell-to-cell bacterial interactions promoted by drier conditions on soil surfaces.

Bacterial cell-to-cell interactions are in the core of evolutionary and ecological processes in soil and other environments. Under most conditions, natural soils are unsaturated where the fragmented aqueous habitats and thin liquid films confine bacterial cells within small volumes and close proximity for prolonged periods. We report effects of a range of hydration conditions on bacterial cell-level interactions that are marked by plasmid transfer between donor and recipient cells within populations of the soil bacterium Pseudomonas putida Using hydration-controlled sand microcosms, we demonstrate that the frequency of cell-to-cell contacts under prescribed hydration increases with lowering water potential values (i.e., under drier conditions where the aqueous phase shrinks and fragments). These observations were supported using a mechanistic individual-based model for linking macroscopic soil water potential to microscopic distribution of liquid phase and explicit bacterial cell interactions in a simplified porous medium. Model results are in good agreement with observations and inspire confidence in the underlying mechanisms. The study highlights important physical factors that control short-range bacterial cell interactions in soil and on surfaces, specifically, the central role of the aqueous phase in mediating bacterial interactions and conditions that promote genetic information transfer in support of soil microbial diversity.

Extracellular polymeric substances induced cell-surface interactions facilitate bacteria transport in saturated porous media.

Bacteria often respond to dynamic soil environment through the secretion of extracellular polymeric substances (EPS). The EPS modifies cell surface properties and soil pore-scale hydration status, which in turn, influences bacteria transport in soil. However, the effect of soil particle size and EPS-mediated surface properties on bacterial transport in the soil is not well understood. In this study, the simultaneous impacts of EPS and collector size on Escherichia coli (E. coli) transport and deposition in a sand column were investigated. E. coli transport experiments were carried out under steady-state flow in saturated columns packed with quartz sand with different size ranges, including 0.300-0.425 mm (sand-I), 0.212-0.300 mm (sand-II), 0.106-0.150 mm (sand-III) and 0.075-0.106 mm (sand-IV). Bacterial retention increased with decreasing sand collector size, suggesting that straining played an important role in fine-textured media. Both experiment and simulation results showed a clear drop in the retention rate of the bacterial population with the presence of additional EPS (200 mg L-1) (EPS+). The inhibited retention of cells in sand columns under EPS+ scenario was likely attributed to enhanced bacteria hydrophilicity and electrostatic repulsion between cells and sand particles as well as reduced straining. Calculations of the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) interactions energies revealed that high repulsive energy barrier existed between bacterial cells and sand particles in EPS+ environment, primarily due to high repulsive electrostatic force and Lewis acid-base force, as well as low attractive Lifshitz-van der Waals force, which retarded bacterial population deposition. Steric stabilization of EPS would also prevent the approaching of cells close to the quartz surface and thereby hinder cell attachment. This study was the first to show that EPS reduced bacterial straining in saturated porous media. These findings provide new insight into the functional effects of extrinsic EPS on bacterial transport behavior in the saturated soil environment, e.g., aquifers.

Quantifying the impact of addressing data challenges in prediction of length of stay.

BACKGROUND: Prediction of length of stay (LOS) at admission time can provide physicians and nurses insight into the illness severity of patients and aid them in avoiding adverse events and clinical deterioration. It also assists hospitals with more effectively managing their resources and manpower. METHODS: In this field of research, there are some important challenges, such as missing values and LOS data skewness. Moreover, various studies use a binary classification which puts a wide range of patients with different conditions into one category. To address these shortcomings, first multivariate imputation techniques are applied to fill incomplete records, then two proper resampling techniques, namely Borderline-SMOTE and SMOGN, are applied to address data skewness in the classification and regression domains, respectively. Finally, machine learning (ML) techniques including neural networks, extreme gradient boosting, random forest, support vector machine, and decision tree are implemented for both approaches to predict LOS of patients admitted to the Emergency Department of Odense University Hospital between June 2018 and April 2019. The ML models are developed based on data obtained from patients at admission time, including pulse rate, arterial blood oxygen saturation, respiratory rate, systolic blood pressure, triage category, arrival ICD-10 codes, age, and gender. RESULTS: The performance of predictive models before and after addressing missing values and data skewness is evaluated using four evaluation metrics namely receiver operating characteristic, area under the curve (AUC), R-squared score (R2), and normalized root mean square error (NRMSE). Results show that the performance of predictive models is improved on average by 15.75% for AUC, 32.19% for R2 score, and 11.32% for NRMSE after addressing the mentioned challenges. Moreover, our results indicate that there is a relationship between the missing values rate, data skewness, and illness severity of patients, so it is clinically essential to take incomplete records of patients into account and apply proper solutions for interpolation of missing values. CONCLUSION: We propose a new method comprised of three stages: missing values imputation, data skewness handling, and building predictive models based on classification and regression approaches. Our results indicated that addressing these challenges in a proper way enhanced the performance of models significantly, which led to a more valid prediction of LOS.

Target controllability with minimal mediators in complex biological networks.

Controllability of a complex network system is related to finding a set of minimum number of nodes, known as drivers, controlling which allows having a full control on the dynamics of the network. For some applications, only a portion of the network is required to be controlled, for which target control has been proposed. Often, along the controlling route from driver nodes to target nodes, some mediators (intermediate nodes) are also unwillingly controlled, which might cause various side effects. In controlling cancerous cells, unwillingly controlling healthy cells, might result in weakening them, thus affecting the immune system against cancer. This manuscript proposes a suitable candidate solution to the problem of finding minimum number of driver nodes under minimal mediators. Although many others have attempted to develop algorithms to find minimum number of drivers for target control, the newly proposed algorithm is the first one that is capable of achieving this goal and at the same time, keeping the number of the mediators to a minimum. The proposed controllability condition, based on path lengths between node pairs, meets Kalman's controllability rank condition and can be applied on directed networks. Our results show that the path length is a major determinant of in properties of the target control under minimal mediators. As the average path length becomes larger, the ratio of drivers to target nodes decreases and the ratio of mediators to targets increases. The proposed methodology has potential applications in biological networks. The source code of the algorithm and the networks that have been used are available from the following link: https://github.com/LBBSoft/Target-Control-with-Minimal-Mediators.git.

Automatic Super-Surface Removal in Complex 3D Indoor Environments Using Iterative Region-Based RANSAC.

Removing bounding surfaces such as walls, windows, curtains, and floor (i.e., super-surfaces) from a point cloud is a common task in a wide variety of computer vision applications (e.g., object recognition and human tracking). Popular plane segmentation methods such as Random Sample Consensus (RANSAC), are widely used to segment and remove surfaces from a point cloud. However, these estimators easily result in the incorrect association of foreground points to background bounding surfaces because of the stochasticity of randomly sampling, and the limited scene-specific knowledge used by these approaches. Additionally, identical approaches are generally used to detect bounding surfaces and surfaces that belong to foreground objects. Detecting and removing bounding surfaces in challenging (i.e., cluttered and dynamic) real-world scene can easily result in the erroneous removal of points belonging to desired foreground objects such as human bodies. To address these challenges, we introduce a novel super-surface removal technique for 3D complex indoor environments. Our method was developed to work with unorganized data captured from commercial depth sensors and supports varied sensor perspectives. We begin with preprocessing steps and dividing the input point cloud into four overlapped local regions. Then, we apply an iterative surface removal approach to all four regions to segment and remove the bounding surfaces. We evaluate the performance of our proposed method in terms of four conventional metrics: specificity, precision, recall, and F1 score, on three generated datasets representing different indoor environments. Our experimental results demonstrate that our proposed method is a robust super-surface removal and size reduction approach for complex 3D indoor environments while scoring the four evaluation metrics between 90% and 99%.

Stochastic Force-based Insertion Depth and Tip Position Estimations of Flexible FBG-Equipped Instruments in Robotic Retinal Surgery.

Vitreoretinal surgery is among the most delicate surgical tasks during which surgeon hand tremor may severely attenuate surgeon performance. Robotic assistance has been demonstrated to be beneficial in diminishing hand tremor. Among the requirements for reliable assistance from the robot is to provide precise measurements of system states e.g. sclera forces, tool tip position and tool insertion depth. Providing this and other sensing information using existing technology would contribute towards development and implementation of autonomous robot-assisted tasks in retinal surgery such as laser ablation, guided suture placement/assisted needle vessel cannulation, among other applications. In the present work, we use a state-estimating Kalman filtering (KF) to improve the tool tip position and insertion depth estimates, which used to be purely obtained by robot forward kinematics (FWK) and direct sensor measurements, respectively. To improve tool tip localization, in addition to robot FWK, we also use sclera force measurements along with beam theory to account for tool deflection. For insertion depth, the robot FWK is combined with sensor measurements for the cases where sensor measurements are not reliable enough. The improved tool tip position and insertion depth measurements are validated using a stereo camera system through preliminary experiments and a case study. The results indicate that the tool tip position and insertion depth measurements are significantly improved by 77% and 94% after applying KF, respectively.