Disease Classification System Research Articles

Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach

Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a deep learning (DL) technique for the classification of arrhythmias. The proposed technique makes use of the University of California, Irvine (UCI) repository, which consists of a high-dimensional cardiac arrhythmia dataset of 279 attributes. In this research, our goal was to classify cardiac arrhythmia patients into 16 classes depending on the characteristics of the electrocardiography dataset. The DL approach in the form of long short-term memory (LSTM) is an efficient technique to deal with reduced accuracy due to vanishing and exploding gradients in traditional DL frameworks for big data analysis. The goal of this research was to categorize cardiac arrhythmia patients by developing an efficient intelligent system using the LSTM DL algorithm. This approach to arrhythmia classification includes classification algorithms along with noise removal techniques. Therefore, we utilized principal components analysis (PCA) for noise removal, and LSTM for classification. This hybrid comprehensive arrhythmia classification approach performs better than previous approaches to arrhythmia classification. We attained a highest classification accuracy of 93.5% with the DL based disease classification system, and outperformed the earlier approaches used for cardiac arrhythmia classification.

Application of Machine Learning Techniques in Crop Disease Classification: A Comprehensive Review

Crop diseases are the main threat to agricultural products. Fast, accurate, and automatic detection of diseases can help to overcome this problem. Literature suggests, machine learning techniques are capable of achieving these goals in near real-time. This article presents a comprehensive review of machine learning techniques for crop disease detection and classification. Existing plant disease classification systems are explored and commonly used processing steps are investigated. Analysis of machine learning techniques, accuracy factor, and current state-of-the-art in this domain have been reviewed and presented through this manuscript. The survey resulted in the identification of the strengths and limitations of existing techniques and provides a road map for future research works. These would help researchers to understand and pursue machine learning applications in the field of disease detection and classification

Design and implementation of an efficient rose leaf disease detection and classification using convolutional neural network

Roses are the most planted flowers in world and are grown to make profit and regarded as symbol of love. Diseases are harmful to plants' health, which in turn has adverse impact on the life cycle and quality of flowers. In order to ensure quality and minimum losses to cultivate, it is essential to develop an effective prevention mechanism. This paper has introduced modelling of rose plant disease classification systems based on concept of pre-trained learning mechanism of convolutional neural network. The proposed computational classification model uses multi-level pre-processing scheme as an auxiliary tool for feature learning and accurate disease identification. The modelling of proposed model is carried out on basis of analytical research methodology with prime objective of gaining higher performance. The study outcome shows better performance with an accuracy rate of 97.3% in disease classification. The scope of proposed work is justified based on performance analysis and comparative assessment.

Impaired host response and the presence of Acinetobacter baumannii in the serum microbiome of type-II diabetic patients.

SummaryType II diabetes (T2D) affects over 10% of the US population and is a growing disease worldwide that manifests with numerous comorbidities and defects in inflammation. This dysbiotic host response allows for infection of the host by numerous microorganisms. In the course of T2D disease, individuals can develop chronic infections including foot ulcers and periodontitis, which lead to further complications and opportunistic infections in multiple body sites. In this study, we investigated the serum of healthy subjects and patients with T2D with (T2DP) or without periodontitis for both microbiome signatures in addition to cytokine profiles. Surprisingly, we detected the presence of Acinetobacter baumanii in the serum of 23% individuals with T2D/T2DP tested. In T2DP, IL-1β, TNF-α, MCP-1, IL-6, IL-8, and IFN-γ were significantly elevated in ABC-positive subjects. As an emerging pathogen, A. baumanii infection represents a risk for impaired inflammation and the development of comorbidities in subjects with T2D.

Plant Leaf Disease Classification and Detection System Using Machine Learning

In a developing country like India agriculture plays a noteworthy role. Agricultural intervention in the livelihood of rural India indulges by about 58%. Among the agricultural products, tomato is one of the most used crops. Thus, preventing significant loss in quantity and yield of tomato is majorly dependent on recognition and classification of diseases a tomato plant might possess. Latest and fostering technologies like Image processing is used to rectify such issues using different types of techniques and algorithms. Initially, the leaves of a tomato plant get affected, when plant develops a particular type of disease. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. To remove the noise we are doing the pre-processing and to part the affected or damages area of the leaf, image segmentation is used. The k-nearest neighbors (KNN) algorithm, which is a guided, supervised and advance machine learning algorithm, is implemented to find solutions for both the problems related to classification and regression. During the terminal stage, user is recommended with the treatment. Mostly live plants are adversely affected by the diseases. This paper imparts representation of leaf disease detection employing image processing that can identify drawbacks in tomato plant from images, based on color, bound and texture to give the brisk and reliable results to the farmer.

Combining clinical features and MEST-C score in IgA nephropathy may be a better determinant of kidney survival.

Introduction. IgA nephropathy (IgAN) is a heterogeneous disease with highly variable clinical and histopathological features. We investigated the effects of Oxford classification and clinical features on renal survival in patients with IgAN.Methods. This retrospective observational study conducted from 2013 to 2017. Ninety-seven patients who were followed up more than six months were examined.Results. A total of 97 patients (68% male and median age 40 years) were enrolled in this study. 13% of patients developed end stage renal disease (ESRD) within the median of 37 months of follow-up. Need for renal replacement therapy at the time of diagnosis, serum creatinine level of higher than 1.97 mg/dl, serum albumin level less than 3.5 gr/dl, 24-hour urine protein level of higher than > 3.5 g/day, the percentage of glomerulosclerosis higher than 53%, T2 score and total MEST-C score higher than two were found to be significant predictors of development of ESRD. None of the clinical or histopathological features were found to be significant predictor of steroid treatment sensitivity except T1-2 scores.Conclusion. We think that IgA nephropathy is a heterogeneous disease that requires clinical and histopathological features to be evaluated together, but not individually, to determine renal survival.What is new. Iga nephropathy is a heterogeneous disease and modern pathologic classification systems is not enough to predict to prognosis. Histopathological features to be evaluated with clinical features, but not individually, to determine renal survival. Also glucocorticoid treatment response seems to be independent from clinical and histopathological features except T1-2 score.

Rice plant disease classification using color features: a machine learning paradigm

In traditional practices, detection of rice plant diseases by experts is a subjective matter whereas by testing in the laboratory is time-consuming. As a consequence, it causes reduction on agricultural production and economic loss to farmers. To overcome this, there is a demand to develop fast and effective systems to detect and classify rice plant diseases. Therefore, the development of image-based automated systems for classification of rice plant diseases is an interesting growing research area in the agriculture domain. Color is one of the important features to classify rice plant diseases. In this study, we have presented an image-based rice plant disease classification approach using color features only. We have explored 14 different color spaces and extracted four features from each color channel leading to 172 features. Moreover, the performance of seven different classifiers have been compared and demonstrated that a highest classification accuracy of 94.65% has been achieved using support vector machine (SVM) classifier. Training and testing of models were performed on the dataset that consists of 619 images. This dataset was collected from the real agriculture field that belongs to four classes: (a) Bacterial Leaf Blight (BLB), (b) Rice Blast (RB), (c) Sheath Blight (SB) and (d) Healthy Leave (HL). The encouraging results of this paper show that color features can play an important role in developing rice plant disease classification system and enable the farmers to take preventive measures resulting in better product quality and quantity.

Phenotypically Similar Rare Disease Identification from an Integrative Knowledge Graph for Data Harmonization: Preliminary Study.

BackgroundAlthough many efforts have been made to develop comprehensive disease resources that capture rare disease information for the purpose of clinical decision making and education, there is no standardized protocol for defining and harmonizing rare diseases across multiple resources. This introduces data redundancy and inconsistency that may ultimately increase confusion and difficulty for the wide use of these resources. To overcome such encumbrances, we report our preliminary study to identify phenotypical similarity among genetic and rare diseases (GARD) that are presenting similar clinical manifestations, and support further data harmonization.ObjectiveTo support rare disease data harmonization, we aim to systematically identify phenotypically similar GARD diseases from a disease-oriented integrative knowledge graph and determine their similarity types.MethodsWe identified phenotypically similar GARD diseases programmatically with 2 methods: (1) We measured disease similarity by comparing disease mappings between GARD and other rare disease resources, incorporating manual assessment; 2) we derived clinical manifestations presenting among sibling diseases from disease classifications and prioritized the identified similar diseases based on their phenotypes and genotypes.ResultsFor disease similarity comparison, approximately 87% (341/392) identified, phenotypically similar disease pairs were validated; 80% (271/392) of these disease pairs were accurately identified as phenotypically similar based on similarity score. The evaluation result shows a high precision (94%) and a satisfactory quality (86% F measure). By deriving phenotypical similarity from Monarch Disease Ontology (MONDO) and Orphanet disease classification trees, we identified a total of 360 disease pairs with at least 1 shared clinical phenotype and gene, which were applied for prioritizing clinical relevance. A total of 662 phenotypically similar disease pairs were identified and will be applied for GARD data harmonization.ConclusionsWe successfully identified phenotypically similar rare diseases among the GARD diseases via 2 approaches, disease mapping comparison and phenotypical similarity derivation from disease classification systems. The results will not only direct GARD data harmonization in expanding translational science research but will also accelerate data transparency and consistency across different disease resources and terminologies, helping to build a robust and up-to-date knowledge resource on rare diseases.

Development of a Severity Classification System for Sickle Cell Disease.

PurposeThere is no well-accepted classification system of overall sickle cell disease (SCD) severity. We sought to develop a system that could be tested as a clinical outcome predictor.Patients and MethodsUsing validated methodology (RAND/UCLA modified Delphi panel), 10 multi-disciplinary expert clinicians collaboratively developed 180 simplified patient histories and rated each on multiple axes (estimated clinician follow-up frequency, risk of complications or death, quality of life, overall disease severity). Using ratings on overall disease severity, we developed a 3-level severity classification system ranging from Class I (least severe) to Class III (most severe).ResultsThe system defines patients as Class I who are 8–40 years with no end organ damage, no chronic pain, and ≤4 unscheduled acute care visits due to vaso-occlusive crises (VOC) in the last year. Patients <8 or >40 years with no end organ damage, no chronic pain, and <2 unscheduled acute care visits are also considered Class I. Patients any age with ≥5 unscheduled acute care visits and/or with severe damage to bone, retina, heart, lung, kidney, or brain are classified as Class III (except patients ≥25 years with severe retinopathy, no chronic pain, and 0–1 unscheduled acute care visits, who are considered Class II). Patients not meeting these Class I or III definitions are classified as Class II.ConclusionThis system consolidates patient characteristics into homogenous groups with respect to disease state to support clinical decision-making. The system is consistent with existing literature that increased unscheduled acute care visits and organ damage translate into clinically significant patient morbidity. Studies to further validate this system are planned.

Evidence-based, personalised and minimally invasive treatment for periodontitis patients - the new EFP S3-level clinical treatment guidelines.

The 2017 international classification system for periodontal diseases characterises periodontitis patients based upon disease extent, severity and past experience, and in stage IV includes a treatment complexity component. In addition, taking into account well-established risk factors, it aims to estimate the risk for future attachment loss. This classification system draws upon current understanding of disease pathobiology and reflects the complex, multifactorial nature of the periodontitis. It also acknowledges individual patients' risk profiles.Classification and diagnosis are distinct but linked entities, and the inclusion of established risk factors in the system helps signpost the clinician and patient towards a more personalised approach to care provision. The European Federation of Periodontology (EFP) has recently developed an S3-level clinical treatment guideline for stages I to III periodontitis, based upon a rigorous standardised process involving 15 systematic reviews of current evidence and their synthesis by a representative group of experts and stakeholders. The aim is to guide the practitioner through the complex plethora of periodontal management options and to facilitate pragmatic decision-making in full knowledge of the evidence base.Here, we present the structured, stepwise treatment protocols developed by the EFP guideline group and highlight selected specific clinical recommendations. The adapted guidelines for the UK healthcare system will be published in early 2021.

Development of a Plant Disease Classification System using an Improved Counter Propagation Neural Network

Development of a Plant Disease Classification System using an Improved Counter Propagation Neural Network

Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN)

Jambi Province is a producer of palm oil as a mainstay of commodities. However, the limited insight of farmers in Jambi to oil palm pests and diseases affects oil palm productivity. Meanwhile, knowing the types of pests and diseases in oil palm requires an expert, but access restrictions are a problem. This study offers a diagnosis of oil palm disease using the most popular concept in the field of artificial intelligence today. This method is deep learning. Various recent studies using CNN, say the results of image recognition accuracy are very good. The data used in this study came from oil palm image data from the Jambi Provincial Plantation Office. After the oil palm disease image data is trained, the training data model will be stored for the process of testing the oil palm disease diagnosis. The test evaluation is stored as a configuration matrix. So that it can be assessed how successful the system is to diagnose diseases in oil palm plants. From the testing, there were 2490 images of oil palm labeled with 11 disease categories. The highest accuracy results were 0.89 and the lowest was 0.83, and the average accuracy was 0.87. This shows that the results of the classification of oil palm images with CNN are quite good. These results can indicate the development of an automatic and mobile oil palm disease classification system to help farmers.

An MRI brain disease classification system using PDFB-CT and GLCM with kernel-SVM for medical decision support

The automatic binary classification of normal and abnormal subjects using magnetic resonance (MR) brain images has made remarkable progress in recent years. This automation method plays a central role in the early evaluation of degenerative brain diseases in patients. In recent years, various new robust classification methods have been proposed based on families of the wavelet transform (WT). These transforms are good at capturing edge points but lack smoothness along the contour of an image. Therefore, instead of using WT in our experiment, we used a pyramid directional filter bank contourlet transform (PDFB-CT). The key characteristic of this transform is that it is likely to manage 2D singularities efficiently, i.e., edges, unlike the wavelets, which deal with point singularities exclusively, and it also efficiently implements a wavelet-like structure using iterative filter banks. Moreover, we passed the outcome obtained from the PDFB-CT to the gray-level co-occurrence matrix (GLCM) to obtain 22 texture features from each MR brain image. Furthermore, we passed these extracted features to random tree embedding (RTE) to transform low-dimensional features to high-dimensional features before passing them to probabilistic principal component analysis (PPCA) for dimensionality reduction. Here, the multi-kernel support vector machine classifier with a grid search CV (to find optimal hyperparameters) method was used to perform binary classification of abnormal and normal images. As a result, our proposed system achieved an area under the receiver operating characteristic (AU-ROC) curve and classification accuracy of 100% for abnormal vs. normal group classification using a ten-fold stratified cross-validation technique. This experiment result exemplifies the significance of our proposed method compared with recently published state-of-the-art techniques, and hence our proposed method can be effectively used by a physician as a support tool for examining a patient’s brain.

Interobserver agreement of the anatomic and physiological classification system for adult congenital heart disease

Consensus definitions for AP categories were developed with input from 4 experts. Research assistants (RAs) assigned AnatC/PhyS for patients in the Boston ACHD Biobank, a prospectively enrolled cohort of ambulatory ACHD patients ≥18 years old seen between 2012 and 2019. Two (of 4) expert reviewers then independently assigned AnatC/PhyS for 41 patients. Interrater reliability was assessed with linearly weighted kappa (κω) for agreement between (1) experts and (2) an RA and an expert. Experts examined disagreements and identified sources of variability and areas requiring clarification. Interexpert agreement for AnatC was excellent, with agreement on 38/41 (92.7%) cases and κω 0.88 [0.75, 1.01]. Agreement for PhyS was less robust, with consensus on 24/41 cases (59.5%), κω 0.57 [0.39, 0.75]. Expert-RA agreement was lower for AnatC (κω 0.77 [0.60, 0.95]), whereas PhyS was similar to interexpert agreement (κω 0.53 [0.34, 0.72]). There was ambiguity in the definitions of (1) arrhythmia status, (2) cyanotic CHD, and (3) valve disease. Although AnatC can be assessed reliably, that is not true for the PhyS part of the AP classification proposed in the 2018 American College of Cardiology/American Heart Association guidelines. Reliability of PhyS would be strengthened by more precise definitions readily interpretable in clinical practice.

Skin Disease Classification using Neural Network.

In this study, a novel and fully automatic skin disease classification approach is proposed using statistical feature extraction and Artificial Neural Network (ANN) based classification using first and second order statistical moments, the entropy of different color channels and texture-based features. The basic aim of our study is to develop an automated system for skin disease classification that can help a general physician to automatically detect the lesion and classify it to disease types. The performance of the proposed approach is corroborated by extensive experiments performed on a dataset of 588 images containing 6907 lesion regions. The results show that the proposed methodology can be effectively used to construct a skin disease classification system. Our proposed method is designed for a specific skin tone. Future investigation is needed to analyze the impact of different skin tones on the performance of lesions detection and classification system.

Classification of Periodontal Diseases – Old is Gold or New is Bold? A Survey Study in Egypt

Background: A consensus was reached for a new classification system for periodontal and peri-implant diseases and conditions and was proposed by a group of world experts in 2017. Since then, there have been ongoing debates among periodontists regarding the application of the new classification. This study aims to shed light on the current understanding of the new classification among Egyptian periodontists. Methods: This cross-sectional survey study was performed using an anonymous manually distributed questionnaire in various universities in Egypt. The validated questionnaire included 15 questions – 13 multiple choice questions with a Likert scale and two open ended questions, in addition to demographic data of the participant. Qualitative data were presented as frequencies and percentages, and binary and ordinal logistic regression analyses were performed. Results: The clarity of the new classification was the only significant predictor for satisfaction. An increase in clarity scores was directly associated with an increase in satisfaction scores (odds ratio = 5.521, 95% CI = 2.198 – 8.844, P-value = 0.001). Only 24.2% of the participants actually applied the new classification. There was a high approval rate on the introduction of “health on reduced periodontium” and the classification of peri-implant conditions. However, there was a huge dissatisfaction with applying the staging and grading system as well as the omission of aggressive periodontitis. Conclusions: This survey has identified important gaps between theory and practice and bridging these gaps by revising the controversial points would help develop a clearer, simpler system for clinicians to improve patients’ oral health.

Development and Validation of A Male Anterior Urethral Stricture Classification System

To develop and validate a clinical classification system for urethral stricture disease (USD) based on the retrograde urethrogram (RUG), physical exam, and stricture-specific patient history. Three elements were chosen to be included in the classification system: 1) Length of urethral stricture (L); 2) Stricture segment/location (S); 3) Stricture Etiology (E) (LSE classification system). Each element was divided into clinically relevant sub-categories. A three-step development and validation process then ensued, culminating in an in-person Trauma and Urologic Reconstruction Network of Surgeons (TURNS) meeting, at which the final classification system was unanimously agreed upon by attendees based on interrater reliability data obtained from the classifying of 22 clinical vignettes. A final validation step involved retrospectively classifying cases in the TURNS database to determine if classification influenced surgical technique and was associated with presumed stricture etiology. The final LSE classification system was found to have an interrater reliability of 0.79 (individual components 0.76, 0.70 and 0.93 respectfully). Retrospective classification of the 2162 TURNS strictures revealed the segment (S) to be strongly associated with urethroplasty type (p = 0.0005) and stricture etiology (E) (p = 0.0005). We developed and validated a novel, easy to use, urethral stricture classification system. The system's ability to aid in directing treatments, predict treatment outcomes, and facilitate collaborative research efforts will require further study.

Prognostic Reliability of a New Classification System for Blount's Disease.

ObjectiveWe conducted this study to evaluate the reproducibility of a new classification system for Blount's disease and assess its correlation with established radiological measures used to evaluate the severity of this disorder.Materials and MethodsThis is a retrospective review of children with Blount’s disease that were younger than 10 years of age. Recurrence was defined as the need for a second corrective surgery. Radiographs immediately pre-surgery and at final follow-up were used to measure mechanical axis (MA), tibial metaphyseal-diaphyseal angle (TMDA), epiphyseal-metaphyseal angle (EMA), lateral distal femoral angle (LDFA), and medial proximal tibial angle (MPTA). Patients were stratified according to the new classification (Type A, B, or C).ResultsSixty-five limbs from 16 males and 24 females met our inclusion criteria. The average follow-up was 4.2 years. Twelve patients (with 22 Type-A extremities) underwent bracing with a success rate of 54%. Thirty-four patients (53 extremities) underwent surgical correction. The recurrence rate was 35.8%. Group C had a recurrence rate of 62%, higher than that of Group B (33%), and Group A (23%) (P = 0.026). In addition, irrespective of reoperation, patients in Group C had the least change in the MA (62%, P = 0.046) and the most severe values of MPTA and TMDA initially and after the operation (P < 0.05).ConclusionThe new classification system for Blount’s disease holds validity for predicting recurrence. The severity of the grades is correlated with the TMDA, MPTA, and varus reversibility. This can aid physicians and families in making an informed decision and setting treatment goals.

The sputum microbiome, airway inflammation, and mortality in chronic obstructive pulmonary disease

The sputum microbiome has a potential role in disease phenotyping and risk stratification in chronic obstructive pulmonary disease (COPD), but few large longitudinal cohort studies exist. Our aim was to investigate the COPD sputum microbiome and its association with inflammatory phenotypes and mortality. 16S ribosomal RNA gene sequencing was performed on sputum from 253 clinically stable COPD patients (4-year median follow-up). Samples were classified as Proteobacteria or Firmicutes (phylum level) and Haemophilus or Streptococcus (genus level) dominant. Alpha diversity was measured by using Shannon-Wiener diversity and Berger-Parker dominance indices. Survival was modeled by using Cox proportional hazards regression. Asubset of 78 patients had label-free liquid chromatography with tandem mass spectrometry performed, with partial least square discriminant analysis integrating clinical, microbiome, and proteomics data. Proteobacteria dominance and lower diversity was associated with more severe COPD according to the Global Initiative for Chronic Obstructive Lung Disease classification system (P= .0015), more frequent exacerbations (P= .0042), blood eosinophil level less than or equal to 100 cells/μL (P< .0001), and lower FEV1 (P= .026). Blood eosinophil counts showed a positive relationship with percent of Firmicutes and Streptococcus and a negative association with percent Proteobacteria and Haemophilus. Proteobacteria dominance was associated with increased mortality compared with Firmicutes-dominated orbalanced microbiome profiles (hazard ratio= 2.58; 95% CI= 1.43-4.66; P= .0017 and hazard ratio=7.47; 95% CI=1.02-54.86; P= .048, respectively). Integrated omics analysis showed significant associations between Proteobacteria dominance and the neutrophil activation pathway in sputum. The sputum microbiome is associated with clinical and inflammatory phenotypes in COPD. Reduced microbiome diversity, associated with Proteobacteria (predominantly Haemophilus) dominance, is associated with neutrophil-associated protein profiles and an increased risk of mortality.

Relationship of rare disorder latent clusters to anxiety and depression symptoms.

A single rare disorder or disease (RD) affects fewer than 200,000 Americans. Combined, RDs affect 30 million Americans. Collectively, people with RDs have higher levels of anxiety and depression compared with those with prevalent diseases. Previous attempts to study psychological distress within individual RDs or disease classification systems are limited by low power, generalizability, and predictive utility. The present study used mixture modeling to identify meaningful RD clusters associated with anxiety and depression. One-thousand and 218 participants with 232 RDs were surveyed on RD characteristics and anxiety and depression symptoms. Mixture modeling identified symptom clusters based on RD characteristics (i.e., age of symptom onset, disease course, fatigue, pain, physical function, duration of symptoms, and visibility). The following six clusters were identified (most frequently represented RD in parentheses): stable (cutaneous T cell lymphoma), late onset and visible (spinocerebellar ataxia), moderate symptoms (spinocerebellar ataxia), invisible and tiring (idiopathic hypersomnia), severe symptoms (spinocerebellar ataxia), and early onset with severe symptoms (Ehlers-Danlos syndrome). There was a significant relationship between cluster membership and anxiety and depression symptoms. Clusters in order from least to most distress were: stable; late onset and visible; moderate symptoms; invisible and tiring; severe symptoms; and early onset with very severe symptoms. The last two clusters had clinically significantly more distress than the stable cluster. Researchers, clinicians, RD organizations, and policymakers could make wide-reaching impacts by prioritizing funding, research, and interventions for people likely to fall in a cluster at high risk for distress. (PsycINFO Database Record (c) 2020 APA, all rights reserved).