Numerous Features Research Articles

Sarcopenia prediction using shear-wave elastography, grayscale ultrasonography, and clinical information with machine learning fusion techniques: feature-level fusion vs. score-level fusion

This study aimed to develop and evaluate a sarcopenia prediction model by fusing numerical features from shear-wave elastography (SWE) and gray-scale ultrasonography (GSU) examinations, using the rectus femoris muscle (RF) and categorical/numerical features related to clinical information. Both cohorts (development, 70 healthy subjects; evaluation, 81 patients) underwent ultrasonography (SWE and GSU) and computed tomography. Sarcopenia was determined using skeletal muscle index calculated from the computed tomography. Clinical and ultrasonography measurements were used to predict sarcopenia based on a linear regression model with the least absolute shrinkage and selection operator (LASSO) regularization. Furthermore, clinical and ultrasonography features were combined at the feature and score levels to improve sarcopenia prediction performance. The accuracies of LASSO were 70.57 ± 5.00–81.54 ± 4.83 (clinical) and 69.00 ± 4.52–69.73 ± 5.47 (ultrasonography). Feature-level fusion of clinical and ultrasonography (accuracy, 70.29 ± 6.63 and 83.55 ± 4.32) showed similar performance with clinical features. Score-level fusion by AdaBoost showed the best performance (accuracy, 73.43 ± 6.57–83.17 ± 5.51) in the development and evaluation cohorts, respectively. This study might suggest the potential of machine learning fusion techniques to enhance the accuracy of sarcopenia prediction models and improve clinical decision-making in patients with sarcopenia.

Conjecturing-Based Discovery of Patterns in Data

We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and Boolean expressions for categorical features. The proposed Conjecturing framework recovers known nonlinear and Boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the method to a previously proposed framework for symbolic regression on the ability to recover equations that are satisfied among features in a data set. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in the medical literature. Discovering patterns in data is a first step toward establishing causal relationships, which can be the basis for effective decision making. Data Ethics & Reproducibility Note: Code and data to reproduce results are available here: https://github.com/jpbrooks/conjecturing . COVID-19 synthetic patient data were obtained as part of the Veterans Health Administration (VHA) Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge and are used here with permission from the Food and Drug Administration (FDA). The e-companion is available at https://doi.org/10.1287/ijds.2021.0043 . History: Olivia Sheng served as the senior editor for this article.

A Similarity Measurement Method of Normal Cloud Models for the Operational Status Perception and Computing of Urban Rail Transit

The similarity measurement method is the key part of the normal cloud model as well as its applications. Too much attention has been paid on geometric and numerical features, leading to the weak interpretability and unreasonable results in the similarity measurement method of the normal cloud models. A bidirectional and weighted similarity measurement (BWSM) method is proposed by the number distribution and membership of cloud droplets on a normal cloud model. First, the cloud droplets are analyzed, and their characteristics of number distribution and membership that belong to different cloud models are obtained. Second, the similarity measurement strategy of the normal cloud models is analyzed and used to propose a BWSM method, which is compared with two commonly used similarity measurement methods of normal cloud models. The influence of expectation, entropy, and hyper entropy on the similarity measurement method of the normal cloud models is analyzed. Finally, the method is applied on the operational status perception and computing of urban rail transit. Results show the method has high rationality, validity, and applicability for perceiving and computing the operational status of urban rail transit. The method improves the interpretability of the similarity measurement of the normal cloud models and the credibility of the research based on the similarity measurement of the normal cloud models.

Optimization strategy of power purchase and sale for electricity retailers in a two-tier market

Against the backdrop of the gradual advancement of China's electricity market reform, the number of Power Trading Companies in China has been increasing year by year, and as of October 2022, the number has reached more than 10,000. As an important hub connecting the electricity market and users, electricity retailers face double risks from downstream user load fluctuations and electricity market price fluctuations. Therefore, a reasonable power purchase and sale strategy is very important for an electricity retailer. In this study, a block bidding mechanism is adopted to optimize the clearing of the medium-to long-term market and a DA-RBF neural network is established for spot electricity price forecasting model based on numerical feature similarity to improve the accuracy of electricity price forecasting. Furthermore, the model considers the differences in user demand responses and investigates the optimal power purchase and sale strategy, guided by differentiated time-of-use electricity pricing. The case study analysis demonstrated that the proposed power purchase and sale optimization strategy yields favorable results, improving profitability and enhancing the stability of the power system.

Empowering product photography skills among teenagers in Gunung Village, Boyolali Regency

Smartphones are devices equipped with numerous fascinating features, leading people to become increasingly attached to and dependent on this technology. However, in Gunung Village, there are still relatively few individuals who can utilize this medium for profitable production. One example is the utilization of the smartphone camera feature for product photography. The aim of this community service is to enhance teenagers' understanding and skills in practicing the basics of product photography using a smartphone. This enhancement enables them to present their potential products more professionally. The method employed involves providing instruction on basic photography and engaging in practical product photography sessions. The focus of this service is on processed food products from the partners. The outcomes of this service were significant, as evidenced by an average increase of 34.28% in test results regarding product photography material. Additionally, participants successfully produced product photos aligned with the objectives and techniques of product photography.

SU-VPDN: A scene understanding method for vehicle part detection

High-accuracy positioning and identification of vehicle parts are crucial for status detection, fault diagnosis, and the development of intelligent damage detection systems. However, detecting left–right and front-rear categories in vehicle parts remains challenging due to numerous categories and feature similarity. To address this, we propose the scene understanding vehicle part detection network (SU-VPDN). It comprises three subnetworks: vehicle orientation scene classification (VOSC), vehicle part instance segmentation (VPIS), and scene understanding (SU). VOSC performs preliminary classification of vehicle orientation scenes, while VPIS detects vehicle parts. SU fine-tunes VOSC results using the part logic correction module, fuses VOSC and VPIS results using the part replacement module, and obtains accurate VPIS results through confidence regulation and hierarchical filtering. SU-VPDN integrates results from multiple subnetworks, transforming the challenge of detecting similar objects into orientation scene detection for precise recognition. We evaluate the proposed method on a dataset containing 59 types of vehicle parts and compare it with advanced segmentation detection models. Experimental results demonstrate that SU-VPDN outperforms other models, achieving a 12.2% and 12.3% improvement in mean average precision (mAP) for box and mask, respectively, compared to the baseline model. Moreover, qualitative and quantitative experiments show that SU-VPDN significantly enhances the detection performance of objects with similar features. Overall, our proposed SU-VPDN offers a promising solution for detecting left–right and front-rear categories in vehicle parts. It surpasses existing models and can enable insurance companies to replace damaged parts and improve claims efficiency efficiently.

The Swedish National Facility for Magnetoencephalography Parkinson’s disease dataset

Parkinson’s disease (PD) is characterised by a loss of dopamine and dopaminergic cells. The consequences hereof are widespread network disturbances in brain function. It is an ongoing topic of investigation how the disease-related changes in brain function manifest in PD relate to clinical symptoms. We present The Swedish National Facility for Magnetoencephalography Parkinson’s Disease Dataset (NatMEG-PD) as an Open Science contribution to identify the functional neural signatures of Parkinson’s disease and contribute to diagnosis and treatment. The dataset contains whole-head magnetoencephalographic (MEG) recordings from 66 well-characterised PD patients on their regular dose of dopamine replacement therapy and 68 age- and sex-matched healthy controls. NatMEG-PD contains three-minute eyes-closed resting-state MEG, MEG during an active movement task, and MEG during passive movements. The data includes anonymised MRI for source analysis and clinical scores. MEG data is rich in nature and can be used to explore numerous functional features. By sharing these data, we hope other researchers will contribute to advancing our understanding of the relationship between brain activity and disease state or symptoms.

Prediction of Diabetes Using Data Mining and Machine Learning Algorithms: A Cross-Sectional Study.

This study aimed to develop a model to predict fasting blood glucose status using machine learning and data mining, since the early diagnosis and treatment of diabetes can improve outcomes and quality of life. This crosssectional study analyzed data from 3376 adults over 30 years old at 16 comprehensive health service centers in Tehran, Iran who participated in a diabetes screening program. The dataset was balanced using random sampling and the synthetic minority over-sampling technique (SMOTE). The dataset was split into training set (80%) and test set (20%). Shapley values were calculated to select the most important features. Noise analysis was performed by adding Gaussian noise to the numerical features to evaluate the robustness of feature importance. Five different machine learning algorithms, including CatBoost, random forest, XGBoost, logistic regression, and an artificial neural network, were used to model the dataset. Accuracy, sensitivity, specificity, accuracy, the F1-score, and the area under the curve were used to evaluate the model. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important factors for predicting fasting blood glucose status. Though the models achieved similar predictive ability, the CatBoost model performed slightly better overall with 0.737 area under the curve (AUC). A gradient boosted decision tree model accurately identified the most important risk factors related to diabetes. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important risk factors for diabetes, respectively. This model can support planning for diabetes management and prevention.

Massilia litorea sp. nov., Marinobacter salinisoli sp. nov. and Rhodobacter xanthinilyticus sp. nov., isolated from coastal environments.

Three bacterial strains, namely LPB0304T, LPB0319T and LPB0142T, were isolated from coastal environments. The 16S rRNA gene sequences of the three isolates were found to show the highest sequence similarities to Massilia litorea (98.44 %), Marinobacter salinisoli (97.55 %) and Rhodobacter lacus (97.60 %), respectively. The low (<98.7 %) sequence similarities and tree topologies implied the novelty of the three isolates, representing novel genomic species of the genus Massilia, Marinobacter and Rhodobacter. Numerous biochemical and physiological features also supported the distinctiveness of the isolates from previously known species. Based on the phenotypic and phylogenetic data presented in this study, three novel species are suggested with the following names: Massilia litorea sp. nov. (LPB0304T=KACC 21523T=ATCC TSD-216T), Marinobacter salinisoli sp. nov. (LPB0319T=KACC 21522T=ATCC TSD-218T) and Rhodobacter xanthinilyticus sp. nov. (LPB0142T=KACC 18892T=JCM 31567T).

Prediction of the Fatal Acute Complications of Myocardial Infarction via Machine Learning Algorithms.

Myocardial infarction (MI) is a major cause of death, particularly during the first year. The avoidance of potentially fatal outcomes requires expeditious preventative steps. Machine learning (ML) is a subfield of artificial intelligence science that detects the underlying patterns of available big data for modeling them. This study aimed to establish an ML model with numerous features to predict the fatal complications of MI during the first 72 hours of hospital admission. We applied an MI complications database that contains the demographic and clinical records of patients during the 3 days of admission based on 2 output classes: dead due to the known complications of MI and alive. We utilized the recursive feature elimination (RFE) method to apply feature selection. Thus, after applying this method, we reduced the number of features to 50. The performance of 4 common ML classifier algorithms, namely logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost), was evaluated using 8 classification metrics (sensitivity, specificity, precision, false-positive rate, false-negative rate, accuracy, F1-score, and AUC). In this study of 1699 patients with confirmed MI, 15.94% experienced fatal complications, and the rest remained alive. The XGBoost model achieved more desirable results based on the accuracy and F1-score metrics and distinguished patients with fatal complications from surviving ones (AUC=78.65%, sensitivity=94.35%, accuracy=91.47%, and F1-score=95.14%). Cardiogenic shock was the most significant feature influencing the prediction of the XGBoost algorithm. XGBoost algorithms can be a promising model for predicting fatal complications following MI.

X-ray absorption spectroscopy and theoretical investigations of the effect of extended ligands in potassium organic matter interaction.

Potassium (K) is an essential nutrient for plant growth, and despite its abundance in soil, most of the K is structurally bound in minerals, limiting its bioavailability and making this soil K reservoir largely inaccessible to plants. Microbial biochemical weathering has been shown to be a promising pathway to sustainably increase plant available K. However, the mechanisms underpinning microbial K uptake, transformation, storage, and sharing are poorly resolved. To better understand the controls on microbial K transformations, we performed K K-edge x-ray absorption near-edge structure (XANES) spectroscopy on K-organic salts, including acetate, citrate, nitrate, oxalate, and tartrate, which are frequently observed as low molecular weight organic acids secreted by soil microbes, as well as humic acid, which acts as a proxy for higher molecular weight organic acids. The organic salts display feature-rich K XANES spectra, each demonstrating numerous unique features spanning ∼13eV range across the absorption edge. In contrast, the spectra for humic acid have one broad, wide feature across the same energy range. We used a combination of time-dependent density functional theory and the Bethe-Salpeter equation based approach within the OCEAN code to simulate the experimental spectra for K-nitrate (KNO3) and K-citrate [K3(C6H5O7)·H2O] to identify the electronic transitions that give rise to some of the outlying and unique spectral features in the organic salts. KNO3 has both the lowest and highest lying energy features, and K3(C6H5O7)·H2O is produced by several soil microbes and is effective at mineral weathering. Our results analyze the K-organic salt bonding in detail to elucidate why the spectral shapes differ and indicate that the K K-edge XANES spectra are associated with the entire ligand despite similar first-shell bonding environments around the K center. The improved understanding of K bonding environments with organic ligands and their use for interpretation of the K-XANES spectra provides an important toolkit to understand how K is transformed by microbial processes and made bioavailable for plant uptake.

Research on Pricing and Replenishment Decision of Vegetable Products Based on Optimization Algorithms

With the improvement of people's living standards, people's requirements for the freshness and variety richness of food are also constantly increasing. Due to the short shelf life of fresh vegetable products, reasonable prediction and formulation of pricing and replenishment strategies have a significant impact on supermarket revenue. This article first cleans the data and uses Excel and Grabbs statistics to determine the missing and abnormal values of the data. The missing values are supplemented by 0, and the abnormal values are determined and eliminated through manual intervention. Conduct descriptive statistics on the preprocessed data to obtain partial data features of categories classified by day and month. Analyze numerical features to determine the distribution pattern of data using monthly classification, and then use SPSS PRO to conduct Spearman correlation analysis to obtain the relationship between vegetable categories. Next, this article quantifies six data indicators, including daily sales volume of each category, average unit price during normal sales, and discount degree. Then, MATLAB is used for regression fitting analysis to obtain the relationship expressions between each indicator and the total sales volume. Excel analysis is used to summarize the data to obtain the available variety information of each item of vegetables in the supermarket from June 24th to 30th, And use Python software to establish a time series prediction model to analyze and process the information of supermarket products, and obtain the demand for individual products on July 1st. Using the single product selected by the supermarket as the 0-1 decision variable, and the total operating profit and supply demand relationship of the supermarket as the dual objective variables, a multiple linear regression model is constructed to obtain the final replenishment policy and pricing decision. The data in this paper is from the National College Students Mathematical Modeling Competition C question.

Study of a bounded interval perks distribution with quantile regression analysis

AbstractIn this article, a novel bounded interval model called the unit‐Perks model is developed by suitably transforming the positive random variable of the Perks distribution. Numerous statistical features of the bounded interval Perks model are being explored based on the expansion of the density function. Eight distinct estimation approaches are being used to estimate the parameters of the unit‐Perks model. A throughout simulation analysis is also included to evaluate the precision of the resulting estimators from eight estimating approaches. Two real bounded interval data sets are being utilized to investigate the practical applicability of the unit‐Perks model. A comparison is also made to determine which method of estimation works better for the given model. According to a comparison of eight different estimation approaches, the maximum likelihood estimation approach outperformed than the other seven estimating approaches. The unit‐perks model is then used to introduce the quantile regression model named as quantile unit‐Perks distribution. Application to real data set for the quantile unit‐Perks distribution is also performed. The quantile residuals are used for the residual analysis of the fitted regression model. On the basis of mathematical, computational, and pictorial evidences, it is concluded that the presented model exhibited greater modeling capabilities.

A novel approach for breast cancer detection using optimized ensemble learning framework and XAI

Breast cancer (BC) is a common and highly lethal ailment. It stands as the second leading contributor to cancer-related deaths in women worldwide. The timely identification of this condition is of utmost importance in mitigating mortality rates. This research paper presents a novel framework for the precise identification of BC, utilising a combination of image and numerical data features with explainable Artificial Intelligence (XAI). The utilisation of the U-NET transfer learning model is employed for image-based prediction. Additionally, an ensemble model is constructed by integrating characteristics from a customised convolutional neural network (CNN) model with an ensemble comprising random forest (RF) and support vector machine (SVM). The experiments aim to evaluate the influence of original features compared to convoluted features. A comparative analysis is carried out to assess the efficacy of various classifiers in accurately detecting BC, utilising the Wisconsin dataset. The model under consideration exhibits promising capabilities in enhancing BC diagnosis, with a remarkable accuracy rate of 99.99%. The present study contributes to the advancement of BC diagnosis by introducing a novel strategy based on machine learning and discussing the interpretation of the variables using XAI. The primary objective of this approach is to get a notable level of precision, hence facilitating the early and reliable identification of BC. Ultimately, the implementation of this approach is expected to enhance patient outcomes.

Urban Functional Zone Classification Using Light-Detection-and-Ranging Point Clouds, Aerial Images, and Point-of-Interest Data

Urban Functional Zones (UFZs) serve as the fundamental units of cities, making the classification and recognition of UFZs of paramount importance for urban planning and development. These differences between UFZs not only encompass geographical landscape disparities but also incorporate socio-economic information. Therefore, it is essential to extract high-precision two-dimensional (2D) and three-dimensional (3D) Urban Morphological Parameters (UMPs) and integrate socio-economic data for UFZ classification. In this study, we conducted UFZ classification using airborne LiDAR point clouds, aerial images, and point-of-interest (POI) data. Initially, we fused LiDAR and image data to obtain high-precision land cover distributions, building height models, and canopy height models, which served as accurate data sources for extracting 2D and 3D UMPs. Subsequently, we segmented city blocks based on road network data and extracted 2D UMPs, 3D UMPs, and POI Kernel Density Features (KDFs) for each city block. We designed six classification experiments based on features from single and multiple data sources. K-Nearest Neighbors (KNNs), random forest (RF), and eXtreme Gradient Boosting (XGBoost) were employed to classify UFZs. Furthermore, to address the potential data redundancy stemming from numerous input features, we implemented a feature optimization experiment. The results indicate that the experiment, which combined POI KDFs and 2D and 3D UMPs, achieved the highest classification accuracy. Three classifiers consistently exhibited superior performance, manifesting a substantial improvement in the best Overall Accuracy (OA) that ranged between 8.31% and 17.1% when compared to experiments that relied on single data sources. Among these, XGBoost outperformed the others with an OA of 84.56% and a kappa coefficient of 0.82. By conducting feature optimization on all 107 input features, the classification accuracy of all three classifiers exceeded 80%. Specifically, the OA for KNN improved by 10.46%. XGBoost maintained its leading performance, achieving an OA of 86.22% and a kappa coefficient of 0.84. An analysis of the variable importance proportion of 24 optimized features revealed the following order: 2D UMPs (46.46%) &gt; 3D UMPs (32.51%) &gt; POI KDFs (21.04%). This suggests that 2D UMPs contributed the most to classification, while a ranking of feature importance positions 3D UMPs in the lead, followed by 2D UMPs and POI KDFs. This highlights the critical role of 3D UMPs in classification, but it also emphasizes that the socio-economic information reflected by POI KDFs was essential for UFZ classification. Our research outcomes provide valuable insights for the rational planning and development of various UFZs in medium-sized cities, contributing to the overall functionality and quality of life for residents.

Modeling Spatial Anisotropic Relationships Using Gradient-Based Geographically Weighted Regression

Distance and direction play crucial roles in modeling the spatial nonstationarity relationship. Because Euclidean distance ignores the effect of direction, several modified geographically weighted regression (GWR) attempts have been made to model anisotropic relationships using various non-Euclidean distance metrics. These methods, however, adopt uniform parameters to define the non-Euclidean metrics over the whole study area, neglecting the varying numerical features existing in different regions. As a result, they fail to accurately depict spatial anisotropic relationships between variables. To address this issue, we propose a novel method called gradient-based geographically weighted regression (GGWR) that integrates the gradient of spatial relationships into GWR. Additionally, we introduce an l 0-norm regularization technique to achieve the parameter estimation of GGWR. Both simulated and actual data sets were used to validate the proposed method, and the experimental results demonstrate that the gradient field of the spatial relationship obtained by GGWR can effectively characterize the direction and intensity of variable relationships at various locations. Moreover, GGWR outperforms other models, including GWR, directional geographically weighted regression, and Minkowski distance-based geographically weighted regression, in terms of fitting accuracy, coefficient estimation accuracy, and interpretation of coefficient symbols. These findings indicate that the GGWR can be a valuable tool for modeling spatial anisotropic relationships by leveraging the spatial relationship gradient field.

Enhancing supply chain resilience: A machine learning approach for predicting product availability dates under disruption

The COVID-19 pandemic and ongoing political and regional conflicts have a highly detrimental impact on the global supply chain and cause significant delays in logistics operations and international shipments. One of the most pressing concerns is the uncertainty surrounding the availability dates of products, which is critical information for companies to generate effective logistics and shipment plans. Therefore, accurately predicting availability dates plays a pivotal role in executing successful logistics operations, ultimately minimizing total transportation and inventory costs. We investigate the prediction of product availability dates for General Electric (GE) Gas Power’s inbound shipments for gas and steam turbine service and manufacturing operations, utilizing both numerical and categorical features. We evaluate several regression models, including Simple Regression, Lasso Regression, Ridge Regression, Elastic Net, Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network models. Based on real-world data, our experiments demonstrate that the tree-based algorithms (i.e., RF and GBM) provide the best generalization error and outperforms all other regression models tested. We anticipate that our prediction models will assist companies in managing supply chain disruptions and reducing supply chain risks on a broader scale.

PRA-Pred: Structure-based prediction of protein-RNA binding affinity

Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity. Further, we observed that these factors are influenced by the type of RNA strand and the function of the protein in a protein-RNA complex. Multiple regression equations were developed for different classes of complexes to perform the prediction of the binding affinity between the protein and RNA. We evaluated the models using the jack-knife test and achieved an overall correlation 0.77 between the experimental and predicted binding affinities with a mean absolute error of 1.02 kcal/mol. Furthermore, we introduced a web server, PRA-Pred, intended for the prediction of protein-RNA binding affinity, and it is freely accessible through https://web.iitm.ac.in/bioinfo2/prapred/. We propose that our approach could function as a potential resource for investigating protein-RNA recognitions and developing therapeutic strategies.

Automatic feature engineering for catalyst design using small data without prior knowledge of target catalysis

The empirical aspect of descriptor design in catalyst informatics, particularly when confronted with limited data, necessitates adequate prior knowledge for delving into unknown territories, thus presenting a logical contradiction. This study introduces a technique for automatic feature engineering (AFE) that works on small catalyst datasets, without reliance on specific assumptions or pre-existing knowledge about the target catalysis when designing descriptors and building machine-learning models. This technique generates numerous features through mathematical operations on general physicochemical features of catalytic components and extracts relevant features for the desired catalysis, essentially screening numerous hypotheses on a machine. AFE yields reasonable regression results for three types of heterogeneous catalysis: oxidative coupling of methane (OCM), conversion of ethanol to butadiene, and three-way catalysis, where only the training set is swapped. Moreover, through the application of active learning that combines AFE and high-throughput experimentation for OCM, we successfully visualize the machine’s process of acquiring precise recognition of the catalyst design. Thus, AFE is a versatile technique for data-driven catalysis research and a key step towards fully automated catalyst discoveries.

Written discourse in diagnosis for acquired neurogenic communication disorders: current evidence and future directions.

We aimed to perform the first review of research focusing on written discourse performance in people with acquired neurogenic communication disorders. In studies from 2000 onward, we specifically sought to determine: (1) the differences between patient populations and control groups, (2) the differences between different patient populations, (3) longitudinal differences between patient populations, and (4) modality differences between spoken and written discourse performance. We completed a thorough search on MEDLINE, Embase, Cochrane, APAPsycinfo, Web of Science, and Scopus databases. We identified studies that focus on written discourse performance in people with aphasia, primary progressive aphasia, mild cognitive impairment, and Alzheimer's disease. Nineteen studies were identified from the review of literature, some of which addressed more than one of our review questions. Fifteen studies included a comparison between clinical populations and controls. Six studies compared different characteristics of patient populations. Three studies reported changes over time in progressive disorders. Six studies targeted different modalities of discourse. Differences in linguistic features by patient populations are not yet clear due to the limited number of studies and different measures and tasks used across the studies. Nevertheless, there is substantial evidence of numerous linguistic features in acquired neurogenic communication disorders that depart from those of healthy controls. Compared to the controls, people with aphasia tend to produce fewer words, and syntactically simpler utterances compared to the controls. People with Alzheimer's disease produce less information content, and this feature increases over time, as reported in longitudinal studies. Our review imparts additional information that written and spoken discourse provide unique insights into the cognitive and linguistic deficits experienced by people with aphasia, Alzheimer's disease, mild cognitive impairment and primary progressive aphasia and provide targets for treatment to improve written communication in these groups.