Multiclass Logistic Regression Research Articles

Unveiling the magic of mega-city block environments: investigating the intriguing mechanisms shaping children's spontaneous play preferences.

This study delves into the spatial preferences of children for play spaces within high-density urban block environments, specifically targeting the area of Baihua Second Road in Shenzhen, China. Recognizing the critical role of play in children's development, and the unique challenges posed by dense urban settings, this research employs multiclass logistic regression models and negative binomial regression models to construct a detailed mathematical analysis of neighborhood spatial elements and children's play space preferences. Data was meticulously gathered through both objective observations of 14 different types of spaces within the block, and subjective assessments via children's responses to a series of environment photos, capturing the essence of over 3,000 child participants' interactions and choices. Key findings reveal a pronounced preference among children for soft facility features and visually appealing spatial experiences, suggesting a nuanced understanding of play space needs beyond traditional playground designs. Notably, the study identifies that while cartoon-style designs in play facilities might increase moderate attractiveness, ordinary designs hold broader appeal, indicating a preference for diversity in play space aesthetics. These insights offer profound implications for urban planners and designers, advocating for a child-centered approach in the creation of urban play environments that prioritize aesthetic diversity, and the integration of natural elements. Moreover, the study situates Baihua Second Road as a paradigmatic case, illustrating the methodology and analytical framework applied in addressing the complex interplay between children's play preferences and urban spatial configurations. By incorporating a comprehensive data-driven analysis, this research contributes significantly to the discourse on child-friendly urban design, offering valuable strategies for cultivating inclusive and engaging urban play spaces for children amidst the constraints of high-density city living.

Masked facial recognition using ensemble convolutional neural network and grey-level co-occurrence matrix

The COVID-19 pandemic proved how face masks became necessary to stop the spread of infection. Due to this, effective identification of people wearing face mask became challenging. Masked facial recognition has significantly increased in accuracy because of developments in convolutional neural networks (CNNs). Small size of the dataset of masked facial images has been a problem in earlier research. As would be expected, this results in poorer accuracy when the model tries to identify faces. In this study, a novel model is proposed with textural feature extraction using grey-level co-occurrence matrix (GLCM) and an ensemble of two pre-trained CNNs DenseNet-121 and VGG-16. Using the minimum redundancy and maximum relevance, the model has improved accuracy by choosing the most important features of the image. The model was trained using in-house dataset that included 38,290 photos of 2,500 people with approximately equal distribution of properly masked, partially masked, and unmasked images. In this, we evaluated the performance of the model on different classifiers multi-class logistic regression (LR) and support vector machine (SVM) with one-vs-rest (OvR) classification and artificial neural network (ANN) and applied a soft voting scheme. The model achieved the highest accuracy of 98.56% at a learning rate of 0.001 on the ANN classifier.

Identifying the personal characteristics of decent work perception for nursing students in China using latent profile analysis.

Given the importance of perceptions of decent workfor nursing students' future career choices, we attempted to determine potential classifications and characteristics of nursing students' perceptions of decent work so that targeted interventions could be developed. A convenience sampleof 1004s- to fourth-year nursing students completed theGeneral Information Questionnaire, Self-Regulatory Fatigue Scale, Occupational Identity Questionnaire, and Decent Work Perceptions Scale in a cross-sectional survey in Heilongjiang Province, China, resulting in 630 valid questionnaires with a valid return rate of 62.75%. Nursing students' perceptions of decent work were defined using descriptive and regression analysis. Latent profile analysis (LPA) identified three subgroups: low perceived decent work group, medium perceived decent work group, and high perceived decent work group, accounting for 4.76%, 69.37%, and 25.87% of the sample, respectively. The results of unordered multiclass logistic regression show that nursing students with relatively low levels of perceived decent work are more likely to have a low professional identity, a lack of respect for nursing seniors, an involuntary choice of nursing major, and a low family income. Different types of nursing students have different perceptions of decent work, and these universities and related departments can use different educational guidance strategies.

A study on the influencing factors of mental health of Chinese garden workers: a cross-sectional study

BackgroundOccupational hazards occur in all walks of life. China’s horticulture industry is undergoing rapid development. However, the mental health of garden workers has not received much attention. This study investigates the mental health status and influencing factors of Chinese garden workers and provides a basis for promoting their mental health and ensuring the healthy development of Chinese horticulture.MethodsA cross-sectional survey of garden workers in Beijing was conducted from 10 July 2021 to 10 October 2021. A total of 3349 valid questionnaires were recovered, with an effective response rate of 95.69%. Descriptive statistical analysis was carried out on the demographic characteristics, job satisfaction, stress, anxiety, and depression of garden workers, and the influencing factors affecting the mental health of Chinese garden workers were found through a t-test, variance analysis, and ordinal multi-class logistic regression analysis.ResultsSurvey respondents were mostly male (54.4%) and under the age of 40 (64.1%). The anxiety and depression symptoms of the garden workers were moderate. Among staff members, 40.2% were in a normal state of stress. Gender, three meals on time, monthly income, and job satisfaction were the factors influencing stress, anxiety, and depression symptoms among garden workers.ConclusionCompared to medical staff and other groups, the stress, anxiety, and depression symptoms of Chinese garden workers are severe. Gender, monthly income, and job satisfaction are important factors affecting their mental health. Managers should continuously improve the working environment of garden workers, provide salaries that match their positions, and improve their job recognition and satisfaction to reduce the impact of negative emotions on personal health.

Machine learning classification of metallic objects using pulse induction electromagnetic data

This paper presents the classification of metallic objects using total and scattered pulse induction electromagnetic data, with a classification accuracy greater than 90%. Machine learning classification is applied to raw electromagnetic induction (EMI) data without the use of a physics-based model. The EMI method is applied to 8 metallic objects placed at increasing distances from 10–55 mm to the EMI sensing system. The EMI sensing system consists of two RL circuits placed in close proximity. Metallic objects are classified using linear algorithms including a perceptron and multiclass logistic regression, and nonlinear algorithms including a neural network, a 1D and 2D convolutional neural network (CNN). EMI data was collected using an experiment in an electromagnetically shielded laboratory. Feature maps are presented that explain the salient components of the EMI data used by the 1D and 2D CNN.

Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections

PurposeSigmoid diverticulitis is a disease with a high socioeconomic burden, accounting for a high number of left-sided colonic resections worldwide. Modern surgical scheduling relies on accurate prediction of operation times to enhance patient care and optimize healthcare resources. This study aims to develop a predictive model for surgery duration in laparoscopic sigmoid resections, based on preoperative CT biometric and demographic patient data.MethodsThis retrospective single-center cohort study included 85 patients who underwent laparoscopic sigmoid resection for diverticular disease. Potentially relevant procedure-specific anatomical parameters recommended by a surgical expert were measured in preoperative CT imaging. After random split into training and test set (75% / 25%) multiclass logistic regression was performed and a Random Forest classifier was trained on CT imaging parameters, patient age, and sex in the training cohort to predict categorized surgery duration. The models were evaluated in the test cohort using established performance metrics including receiver operating characteristics area under the curve (AUROC).ResultsThe Random Forest model achieved a good average AUROC of 0.78. It allowed a very good prediction of long (AUROC = 0.89; specificity 0.71; sensitivity 1.0) and short (AUROC = 0.81; specificity 0.77; sensitivity 0.56) procedures. It clearly outperformed the multiclass logistic regression model (AUROC: average = 0.33; short = 0.31; long = 0.22).ConclusionA Random Forest classifier trained on demographic and CT imaging biometric patient data could predict procedure duration outliers of laparoscopic sigmoid resections. Pending validation in a multicenter study, this approach could potentially improve procedure scheduling in visceral surgery and be scaled to other procedures.

A Multimodal Biometric System for Iris and Face Traits Based on Hybrid Approaches and Score Level Fusion

The increasing need for information security on a worldwide scale has led to the widespread adoption of appropriate rules. Multimodal biometric systems have become an effective way to increase recognition precision, strengthen security guarantees, and reduce the drawbacks of unimodal biometric systems. These systems combine several biometric characteristics and sources by using fusion methods. Through score-level fusion, this work integrates facial and iris recognition techniques to present a multimodal biometric recognition methodology. The Histogram of Oriented Gradients (HOG) descriptor is used in the facial recognition system to extract facial characteristics, while the deep Wavelet Scattering Transform Network (WSTN) is applied in the iris recognition system to extract iris features. Then, for customized recognition classification, the feature vectors from every facial and iris recognition system are fed into a multiclass logistic regression. These systems provide scores, which are then combined via score-level fusion to maximize the efficiency of the human recognition process. The realistic multimodal database known as (MULB) is used to assess the suggested system's performance. The suggested technique exhibits improved performance across several measures, such as precision, recall, accuracy, equal error rate, false acceptance rate, and false rejection rate, as demonstrated by the experimental findings. The face and iris biometric systems have individual accuracy rates of 96.45% and 95.31% respectively. The equal error rates for the face and iris are 1.79% and 2.36% respectively. Simultaneously, the proposed multimodal biometric system attains a markedly enhanced accuracy rate of 100% and an equal error rate as little as 0.26%.

Enhancing functional efficiency in information-extreme machine learning with logistic regression ensembles

The subject matter of the article is the application of supervised machine learning for the task of object class recognition. The goal is enhancing functional efficiency in information-extreme technology (IET) for object class recognition. The tasks to be solved are: to analyse possible ways of increasing the functional efficiency IET approach; implement an ensemble of models that include logistic regression for prioritizing recognition features and an IEI learning algorithm; compare the functional efficiency of the resulting ensemble of models on well-known dataset with classic approach and results of other researchers. The methods: The method is developed within the framework of the functional approach to modelling natural intelligence applied to the problem of object classification. The following results were obtained: The study tries to augment existing IET to support feature prioritization as part of the object class recognition algorithm. The classical information-extreme algorithm treats all input features equivalently important in forming the decisive rule. As a result, the object features with strong correlation are not prioritized by the algorithm's decisive mechanism – resulting in decreasing functional efficiency in exam mode. The proposed approach is solving this problem by applying a two-stage approach. In the first stage the multiclass logistic regression applied to the input training features vectors of objects to be classified – formed the normalized training matrix. To prevent overfitting of the logistic regression, a model the L2(ridge) regularization method was used. On the second stage, the information-extreme method as input takes the result of the first stage. The geometrical parameters of class containers and the control tolerances on the recognition features were considered as the optimization parameters. Conclusions. The proposed approach increases MNIST (Modified National Institute of Standards and Technology) dataset classification accuracy compared with the classic information-extreme method by 26,44%. The proposed approach has a 3.77% lower accuracy compared to neural-like approaches but uses fewer resources in the training phase and allows retraining the model, as well as expanding the dictionary of recognition classes without model retraining.

Volume Expansion Rate Index Reveals the Damage Process of Surrounding Rock: A Machine Learning-Based Effectiveness Evaluation

Energy sustainability and the establishment of the ‘national water network’ are all inseparable from the construction of underground engineering. Monitoring indices for the surrounding rock are vital for the safety management of underground engineering construction, which determines the actual state of the surrounding rock. The complexity of deep underground engineering construction leads to many situations that cannot be predicted and explained by existing experience. Therefore, it is necessary to identify which monitoring index best represents the surrounding rock damage. Currently, there are no advanced and convenient effectiveness evaluation schemes for surrounding rock monitoring information. To fill the technical gap, this study introduces the volume expansion rate (VER) index for surrounding rock and proposes a machine learning (ML)-based evaluation scheme for the effectiveness of monitoring indices. First, six conditions with different in situ stresses are designed, and tunnel excavation monitoring tests are conducted. Second, the surrounding rock damage determination experiments using the ML classification algorithm are performed, and the accuracy matrix and index significance scores are obtained. The evaluation results show that: (1) The multi-class logistic regression algorithm is more suitable for determining surrounding rock damage with high accuracy and more appropriate significance evaluation outcomes. (2) Under the higher in situ stress condition, the tangential stress is more sensitive to the surrounding rock damage. (3) As the in situ stress increases, the significant monitoring indices demonstrate a transition ‘from shallow to deep, from regional damage to point failure’, describing the instability of the surrounding rock and inspiring a new instability criterion for surrounding rock.

Modeling Pedestrian Injury Severity: A Case Study of Using Extreme Gradient Boosting Vs Random Forest in Feature Selection

Walking and bicycling are lauded for their negative net carbon impact and for their health benefits. However, national crash statistics suggest that pedestrians are disproportionately harmed in any vehicle–pedestrian conflict situation. Although automated transportation in the future is anticipated to increase overall safety, multiple incidents involving automated vehicles have been reported recently, indicating that the technology needs more training on real-world scenarios and conflicts. This research is motivated by the need for contextual data and related levels of harm in potential conflict scenarios in mixed traffic and we use a national police reported crash dataset, CRSS, to address this need. Our study uses a new gradient boosting algorithm, XGBoost, to identify important features among a host of seemingly significant variables. We compare the performance of XGBoost with the more frequently used random forest method and find that XGBoost is more reliable and robust for handling an unbalanced and sparse dataset like crash data, and the features extracted are more aligned to findings from previous research on the topic. We also compare feature importance between NASS-GES and CRSS—two national crash databases with different sampling strategies but the same objective—and find that sampling strategy influences selection of feature importance. We further use the features extracted using XGBoost in a multiclass logistic regression to quantify the effect of these features on different levels of pedestrian injury. Our findings indicate that speed limit, light conditions, pre-crash movements, and location of pedestrian are important contributors to crash severity, along with driver distraction and impairment.

Feature selection with multi-class logistic regression

Feature selection can help to reduce data redundancy and improve algorithm performance in actual tasks. Most of the embedded feature selection models are constructed based on square loss and hinge loss. However, these models based on the square loss cannot directly evaluate the discriminability of the samples in the feature subspace, and these methods based on the hinge loss are difficult to solve due to their complex objective functions. To deal with these problems, a Feature Selection method with Multi-class Logistic Regression (FSMLR) is proposed in this paper. Firstly, we construct a linear function to measure the difference between the distance from samples to their regression hyperplane and the distance from these samples to regression hyperplanes of other classes, which could be used to strengthen the discriminant property of the embedded model. Then, we design a re-weighting matrix with a ℓ2,0-norm sparse condition as well as a discrete condition, which is used to select features in the subspace. Considering that it is difficult to solve the re-weighting matrix with the discrete and sparse conditions in an optimization problem, we relax these two conditions and present a feature selection model via a re-weighted multi-class logistic regression with the two relaxed constraints. Finally, we add the F-norm regularization in our model to avoid overfitting, and its unconstrained equivalent transformation with ℓ2,p-norm regularization is derived to explore the function of the re-weighting matrix. The gradient descent algorithm could be used to solve the FSMLR. Especially, when the regularization term in the equivalence problem is set to ℓ2,1-norm, the global optimal solution can be obtained. Extensive experiments on multiple public data sets prove that FSMLR outperforms other competitors.

Airline flight delays using artificial intelligence in COVID-19 with perspective analytics

This study envisages assessing the effects of the COVID-19 on the on-time performance of US-airlines industry in the disrupted situations. The deep learning techniques used are neural network regression, decision forest regression, boosted decision tree regression and multi class logistic regression. The best technique is identified. In the perspective data analytics, it is suggested what the airlines should do for the on-time performance in the disrupted situation. The performances of all the methods are satisfactory. The coefficient of determination for the neural network regression is 0.86 and for decision forest regression is 0.85, respectively. The coefficient of determination for the boosted decision tree is 0.870984. Thus boosted decision tree regression is better. Multi class logistic regression gives an overall accuracy and precision of 98.4%. Recalling/remembering performance is 99%. Thus multi class logistic regression is the best model for prediction of flight delays in the COVID-19. The confusion matrix for the multi class logistic regression shows that 87.2% flights actually not delayed are predicted not delayed. The flights actually not delayed but wrongly predicted delayed are12.7%. The strength of relation with departure delay, carrier delay, late aircraft delay, weather delay and NAS delay, are 94%, 53%, 35%, 21%, and 14%, respectively. There is a weak negative relation (almost unrelated) with the air time and arrival delay. Security delay and arrival delay are also almost unrelated with strength of 1% relationship. Based on these diagnostic analytics, it is recommended as perspective to take due care reducing departure delay, carrier delay, Late aircraft delay, weather delay and Nas delay, respectively, considerably with effect of 94%, 53%, 35%, 21%, and 14% in disrupted situations. The proposed models have MAE of 2% for Neural Network Regression, Decision Forest Regression, Boosted Decision Tree Regression, respectively, and, RMSE approximately, 11%, 12%, 11%, respectively.

The reasons for not returning to work and health-related quality of life among young and middle-aged patients with stroke: A cross-sectional study.

This study aimed to explore the reasons and influencing factors for non-return to work (non-RTW) within 1 year among young and middle-aged patients with stroke and to assess their health-related quality of life (HRQoL) at 1 year across different reasons. The study was conducted as a telephone-based cross-sectional survey. Seven hundred eighty-nine young and middle-aged patients with stroke aged between 18 and 54 years for men and 18 and 49 years for women in the electronic medical system were included. Data collection included demographic characteristics, socioeconomic status, behavioral habits, history of chronic diseases, work status, reasons for non-RTW, and HRQoL. Of 789 patients, 435 (55.1%) (mean [SD] age, 47.7 [7.8] years) did not return to work within 1 year after stroke. Among the patients who did not RTW, 58.9% were unable to work, 9.7% retired early, 11.03% became full-time homemakers or were unemployed, and 20.5% were reluctant to work. The disordered multiclass logistic regression model showed that the factors influencing the reasons for non-RTW included age, gender, education, income, health insurance, diabetes comorbidity, ability to perform activities of daily living, and mobility of the right upper extremity. Furthermore, patients who were unable to work had significantly lower HRQoL compared to those who had RTW, followed by those who retired early. More than half did not RTW within 1 year in our study. The results will help inform future research to identify interventions to promote RTW and improve HRQoL for young and middle-aged patients with stroke.

Action potential metrics and automated data analysis pipeline for cardiotoxicity testing using optically mapped hiPSC-derived 3D cardiac microtissues.

Recent advances in human induced pluripotent stem cell (hiPSC)-derived cardiac microtissues provide a unique opportunity for cardiotoxic assessment of pharmaceutical and environmental compounds. Here, we developed a series of automated data processing algorithms to assess changes in action potential (AP) properties for cardiotoxicity testing in 3D engineered cardiac microtissues generated from hiPSC-derived cardiomyocytes (hiPSC-CMs). Purified hiPSC-CMs were mixed with 5-25% human cardiac fibroblasts (hCFs) under scaffold-free conditions and allowed to self-assemble into 3D spherical microtissues in 35-microwell agarose gels. Optical mapping was performed to quantify electrophysiological changes. To increase throughput, AP traces from 4x4 cardiac microtissues were simultaneously acquired with a voltage sensitive dye and a CMOS camera. Individual microtissues showing APs were identified using automated thresholding after Fourier transforming traces. An asymmetric least squares method was used to correct non-uniform background and baseline drift, and the fluorescence was normalized (ΔF/F0). Bilateral filtering was applied to preserve the sharpness of the AP upstroke. AP shape changes under selective ion channel block were characterized using AP metrics including stimulation delay, rise time of AP upstroke, APD30, APD50, APD80, APDmxr (maximum rate change of repolarization), and AP triangulation (APDtri = APDmxr-APD50). We also characterized changes in AP metrics under various ion channel block conditions with multi-class logistic regression and feature extraction using principal component analysis of human AP computer simulations. Simulation results were validated experimentally with selective pharmacological ion channel blockers. In conclusion, this simple and robust automated data analysis pipeline for evaluating key AP metrics provides an excellent in vitro cardiotoxicity testing platform for a wide range of environmental and pharmaceutical compounds.

A study on the correlation between family dynamic factors and depression in adolescents.

To evaluate the relationship between systemic family dynamics and adolescent depression. An offline survey was distributed to 4,109 students in grades 6-12, with the final analysis including 3,014 students (1,524 boys and 1,490 girls) aged 10-18 years. The questionnaire included the Self-Rating Scale of Systemic Family Dynamics (SSFD), the Self-Rating Depression Scale (SDS), and demographic characteristics. Family dynamics were negatively correlated with depressive symptoms, with better family dynamics (high scores) associated with lower levels of depression based on the SDS score. After adjusting for sociodemographic characteristics, an ordinal multiclass logistic regression analysis identified family atmosphere (OR = 0.952, 95% CI: 0.948-0.956, p < 0.001) as the most important protective family dynamic against depression, followed by individuality (OR = 0.964, 95% CI: 0.960-0.968, p < 0.001). Latent class analysis (LCA) created the low family dynamic and high family dynamic groups. There were significant differences in the mean SDS scores between the two groups (45.52 ± 10.57 vs. 53.78 ± 11.88; p < 0.001) that persisted after propensity matching. Family atmosphere and individuation had a favorable diagnostic value for depression, with AUCs of 0.778 (95% CI: 0.760-0.796) and 0.710 (95% CI: 0.690-0.730), respectively. The diagnostic models for depression performed well. Poor family dynamics may be responsible for adolescent depression. A variety of early intervention strategies focused on the family may potentially avoid adolescent depression.

Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm.

A significant challenge in high-dimensional and big data analysis is related to the classification and prediction of the variables of interest. The massive genetic datasets are complex. Gene expression datasets are enriched with useful genes that are associated with specific diseases such as cancer. In this study, we used two gene expression datasets from the Gene Expression Omnibus and preprocessed them before classification. We used optimal kernel principal component analysis in which the optimal kernel function was chosen for dataset dimensionality reduction and extraction of the most important features. The gene sets with a high validity index were collected using a combined hieratical clustering and optimal kernel principal component analysis (KHC-RLR) algorithm. Logistic regression is one of the most common methods for classification, and it has been shown to be a useful classification approach for gene expression data analysis. In this study, we used multi-class logistic regression to classify the collected gene sets. We found that ordinary logistic regression caused a major overfitting problem; therefore, we used regularized multi-class logistic regression to classify the gene sets. The proposed KHC-RLR algorithm showed a high performance and satisfied accuracy measures.

Analysis of the Harmfulness of Abnormal Riding Behaviors of Electric Bicycles Based on Improved Multiclass Logistic Regression Model

To analyze the harmfulness of abnormal riding behaviors of electric bicycles in-depth, the research focuses on the 2022 electric bicycle accident data in a specific city in China. Based on an improved multiclass logistic regression model, the relationship between different abnormal riding behaviors and the severity of electric bicycle traffic accidents is explored. Firstly, the severity of accidents is categorized into three levels as the dependent variable, while driver attributes and various hazardous driving behaviors serve as independent variables to construct the multiclass logistic regression model. Secondly, the model is optimized by eliminating irrelevant independent variables and improving the link function. Finally, the harmfulness of abnormal riding behaviors of electric bicycles is analyzed based on the results of the regression model. The results indicate that eight factors significantly influence the dependent variable, with three factors, including driving under the influence of alcohol, being more likely to lead to fatal accidents, requiring focused attention for intervention and regulation.

Topic Modeling Using Latent Dirichlet Allocation &amp; Multinomial Logistic Regression

Unsupervised categorization for datasets has benefits, but not without a few difficulties. Unsupervised algorithms cluster groups of documents in an unsupervised fashion, and often output findings as vectors containing distributions of words clustered according to their probability of occurring together. Additionally, this technique requires human or domain expert interpretation in order to correctly identify clusters of words as belonging to a certain topic. We propose combining Latent Dirichlet Allocation (LDA) with multi-class Logistic Regression for topic modelling as a multi-step classification process in order to extract and classify topics from unseen texts without relying on human labelling or domain expert interpretation in order to correctly identify clusters of words as belonging to a certain topic. The findings suggest that the two procedures were complementary in terms of identifying textual subjects and overcoming the difficulty of comprehending the array of topics from the output of LDA. Keywords: Natural Language Processing; Topic Modeling; Latent Dirichlet Allocation; Logistic Regression

A framework for group-wise summarization and comparison of chromatin state annotations.

Genome-wide maps of epigenetic modifications are powerful resources for non-coding genome annotation. Maps of multiple epigenetics marks have been integrated into cell or tissue type-specific chromatin state annotations for many cell or tissue types. With the increasing availability of multiple chromatin state maps for biologically similar samples, there is a need for methods that can effectively summarize the information about chromatin state annotations within groups of samples and identify differences across groups of samples at a high resolution. We developed CSREP, which takes as input chromatin state annotations for a group of samples. CSREP then probabilistically estimates the state at each genomic position and derives a representative chromatin state map for the group. CSREP uses an ensemble of multi-class logistic regression classifiers that predict the chromatin state assignment of each sample given the state maps from all other samples. The difference in CSREP's probability assignments for the two groups can be used to identify genomic locations with differential chromatin state assignments.Using groups of chromatin state maps of a diverse set of cell and tissue types, we demonstrate the advantages of using CSREP to summarize chromatin state maps and identify biologically relevant differences between groups at a high resolution. The CSREPsource code and generated data are available at http://github.com/ernstlab/csrep. Supplementary data are available at Bioinformatics online.

Classification of neurologic outcomes from medical notes using natural language processing

Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely ‘good recovery’, ‘moderate disability’, ‘severe disability’, and ‘death’ and on the Modified Rankin Scale (mRS), with 7 classes, namely ‘no symptoms’, ‘no significant disability’, ‘slight disability’, ‘moderate disability’, ‘moderately severe disability’, ‘severe disability’, and ‘death’. For 428 patients’ notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93–0.95) and 0.77 (0.75–0.80) for GOS, and 0.90 (0.89–0.91) and 0.59 (0.57–0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.