Multiple Levels Of Granularity Research Articles

Robust language-based mental health assessments in time and space through social media

In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about “mentally unhealthy days” or feelings of “sadness.” Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physical health. Through the large-scale analysis of social media, robust estimation of population mental health is feasible at finer resolutions. In this study, we created a pipeline that used ~1 billion Tweets from 2 million geo-located users to estimate mental health levels and changes for depression and anxiety, the two leading mental health conditions. Language-based mental health assessments (LBMHAs) had substantially higher levels of reliability across space and time than available survey measures. This work presents reliable assessments of depression and anxiety down to the county-weeks level. Where surveys were available, we found moderate to strong associations between the LBMHAs and survey scores for multiple levels of granularity, from the national level down to weekly county measurements (fixed effects β = 0.34 to 1.82; p < 0.001). LBMHAs demonstrated temporal validity, showing clear absolute increases after a list of major societal events (+23% absolute change for depression assessments). LBMHAs showed improved external validity, evidenced by stronger correlations with measures of health and socioeconomic status than population surveys. This study shows that the careful aggregation of social media data yields spatiotemporal estimates of population mental health that exceed the granularity achievable by existing population surveys, and does so with generally greater reliability and validity.

S[formula omitted]WaveNet: A novel spectral–spatial wave network for hyperspectral image classification

Deep learning has made significant progress in hyperspectral image (HSI) classification, and its powerful ability to automatically learn abstract features is well recognized. Recently, the simple architecture of multi-layer perceptron (MLP) has been extensively employed to extract long-range dependencies of HSI and achieved impressive results. However, existing MLP-based models exhibit insufficient representation of spectral–spatial information in HSI and generally aggregate features with fixed weights, which limits their ability to capture semantic differences. To tackle these challenges, this paper proposes a novel spectral–spatial wave network (S2WaveNet) for HSI classification tasks to enhance the representation capability of spectral–spatial features in ground objects. Specifically, the spectral–spatial wave mixer (S2WaveMixer) block is designed as a key component to represent each HSI input as a wave function with amplitude and phase parts. Thus, it enables a deeper dynamic perception and facilitates the extraction of spectral–spatial feature variations of ground objects. The amplitude represents the original features and the phase term is a complex value changing based on the semantic contents of the input images. Furthermore, the inception unit is introduced into the S2WaveMixer block to consider spectral–spatial information at multiple granularity levels. Experiments conducted on five public datasets demonstrate the superiority of S2WaveNet in classification performance and generalization compared to competitors.

Multi-granularity retrieval of mineral resource geological reports based on multi-feature association

Massive geologic report contains all kinds of multimodal geologic data information (geologic text, geologic maps, geologic tables, etc.), which contain a lot of rich geologic basic knowledge and expert experience knowledge about rocks and minerals, stratigraphic structure, geologic age, geographic location, and so on. Accurate retrieval of specific information from massive geologic data has become an important need for geologic information retrieval. However, the majority of existing research primarily revolves around extracting and associating information at a single granularity to facilitate geological semantic retrieval, which ignores many potential semantic associations, leading to ambiguity and fuzziness in semantic retrieval. To solve these problems, this paper proposes a multi-granularity (document-chapter-paragraph) geological information retrieval framework for accurate semantic retrieval. The framework firstly extracts topic feature information, spatiotemporal feature information, figure and table feature information based on the multi-granularity of geological reports. Then, an improved apriori algorithm is used to mine and visualize the associations among the feature information to discover the semantic associations of the geological reports at multiple levels of granularity. Finally, experiments are designed to validate the application of the proposed multi-granularity information retrieval framework on the accurate retrieval of geological reports. The experimental results show that the proposed multi-granularity information retrieval framework in this paper can dig deeper into underlying geo-semantic information and realize accurate retrieval.

JPPF: Multi-task Fusion for Consistent Panoptic-Part Segmentation

Part-aware panoptic segmentation is a problem of computer vision that aims to provide a semantic understanding of the scene at multiple levels of granularity. More precisely, semantic areas, object instances, and semantic parts are predicted simultaneously. In this paper, we present our joint panoptic part fusion (JPPF) that combines the three individual segmentations effectively to obtain a panoptic-part segmentation. Two aspects are of utmost importance for this: first, a unified model for the three problems is desired that allows for mutually improved and consistent representation learning. Second, balancing the combination so that it gives equal importance to all individual results during fusion. Our proposed JPPF is parameter-free and dynamically balances its input. The method is evaluated and compared on the Cityscapes panoptic parts (CPP) and Pascal panoptic parts (PPP) datasets in terms of PartPQ and Part-Whole Quality (PWQ). In extensive experiments, we verify the importance of our fair fusion, highlight its most significant impact for areas that can be further segmented into parts, and demonstrate the generalization capabilities of our design without fine-tuning on 5 additional datasets.

GUESS: GradUally Enriching SyntheSis for Text-Driven Human Motion Generation.

In this paper, we propose a novel cascaded diffusion-based generative framework for text-driven human motion synthesis, which exploits a strategy named GradUally Enriching SyntheSis (GUESS as its abbreviation). The strategy sets up generation objectives by grouping body joints of detailed skeletons in close semantic proximity together and then replacing each of such joint group with a single body-part node. Such an operation recursively abstracts a human pose to coarser and coarser skeletons at multiple granularity levels. Notably, we further integrate GUESS with the proposed dynamic multi-condition fusion mechanism to dynamically balance the cooperative effects of the given textual condition and synthesized coarse motion prompt in different generation stages. Extensive experiments on large-scale datasets verify that GUESS outperforms existing state-of-the-art methods by large margins in terms of accuracy, realisticness, and diversity. Please refer to the supplemental demo video for more visualizations.

IGMG: Instance-guided multi-granularity for domain generalizable person re-identification

Learning generalized feature embedding is crucial for various computer vision tasks, including domain generalizable person re-identification (ReID). ReID aims to develop deep learning-based feature embeddings that can effectively recognize individuals in both trained (source) domains and unseen target domains. However, many state-of-the-art ReID methods suffer from overfitting as they train and test within the same source domain. To address this issue, we investigate the potential of multi-granularity approaches in mitigating domain shift challenges in person re-identification. Specifically, we propose a novel framework called instance-guided multi-granularity (IGMG), which leverages style-free features through non-parametric Instance Normalization (IN) at multiple granularity levels. While high-level abstract concepts are often not shared across different classes, low- and mid-level features can offer more shareable information to enhance the model’s generalization capabilities. By incorporating this concept, our framework can dynamically eliminate style variations across various levels of abstraction. As a result, it enables the model to capture fine-grained details and high-level semantics, leading to enhanced robustness against changes in data distribution. To validate the effectiveness of our approach, we conduct extensive experiments on multiple benchmark ReID datasets. The results consistently demonstrate that our proposed framework exhibits strong generalization capabilities, performing consistently well on unseen target domains. The code is available at https://github.com/mdamranhossenbhuiyan/IGMG/.

ChatReview: A ChatGPT-enabled natural language processing framework to study domain-specific user reviews

Intelligent search engines including pre-trained generative transformers (GPT) have revolutionized the user search experience. Several fields including e-commerce, education, and hospitality are increasingly exploring GPT tools to study user reviews and gain critical insights to improve their service quality. However, massive user-review data and imprecise prompt engineering lead to biased, irrelevant, and impersonal search results. In addition, exposing user data to these search engines may pose privacy issues. Motivated by these factors, we present ChatReview, a ChatGPT-enabled natural language processing (NLP) framework that effectively studies domain-specific user reviews to offer relevant and personalized search results at multiple levels of granularity. The framework accomplishes this task using four phases including data collection, tokenization, query construction, and response generation. The data collection phase involves gathering domain-specific user reviews from public and private repositories. In the tokenization phase, ChatReview applies sentiment analysis to extract keywords and categorize them into various sentiment classes. This process creates a token repository that best describes the user sentiments for a given user-review data. In the query construction phase, the framework uses the token repository and domain knowledge to construct three types of ChatGPT prompts including explicit, implicit, and creative. In the response generation phase, ChatReview pipelines these prompts into ChatGPT to generate search results at varying levels of granularity. We analyze our framework using three real-world domains including education, local restaurants, and hospitality. We assert that our framework simplifies prompt engineering for general users to produce effective results while minimizing the exposure of sensitive user data to search engines. We also present a one-of-a-kind Large Language Model (LLM) peer assessment of the ChatReview framework. Specifically, we employ Google’s Bard to objectively and qualitatively analyze the various ChatReview outputs. Our Bard-based analyses yield over 90% satisfaction, establishing ChatReview as a viable survey analysis tool.

Unsupervised embedding of trajectories captures the latent structure of scientific migration.

Human migration and mobility drives major societal phenomena including epidemics, economies, innovation, and the diffusion of ideas. Although human mobility and migration have been heavily constrained by geographic distance throughout the history, advances, and globalization are making other factors such as language and culture increasingly more important. Advances in neural embedding models, originally designed for natural language, provide an opportunity to tame this complexity and open new avenues for the study of migration. Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation. The resulting representation provides a functional distance between locations, as well as a "digital double" that can be distributed, re-used, and itself interrogated to understand the many dimensions of migration. We show that the unique power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility. Focusing on the case of scientific migration, we apply word2vec to a database of three million migration trajectories of scientists derived from the affiliations listed on their publication records. Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration, such as cultural, linguistic, and prestige relationships at multiple levels of granularity. Our results provide a theoretical foundation and methodological framework for using neural embeddings to represent and understand migration both within and beyond science.

A Machine Learning Pipeline and Application for Automatic Classification of Clinical Documents

Healthcare industry has many associated services including research on various trends or patterns in diseases and patients’ life style. With the emergence of Artificial Intelligence (AI), it is made possible that problems in healthcare domain can be solved by using Machine Learning (ML) techniques. One such problem considered in this paper is known as clinical document classification. Existing methods in this area lack a systematic approach in filtering out false positives. In this paper we proposed a ML framework that considers pipelining of ML models at multiple levels. In the first level, clinical documents that do not have any content related to smoking are discarded. In the second level, the documents that talk about known smoking cases are retained. In the third level clinical document are classified into two categories such as currently smoking and past smokers. We proposed an algorithm known as Learning based Clinical Document Classification (LbCDC). This algorithm makes use of three models in pipeline in order to perform classification of clinical documents at multiple levels of granularity. Our experimental results revealed that the proposed system is efficient in clinical document classification.

Actor-Driven Decomposition of Microservices through Multi-level Scalability Assessment

The microservices architectural style has gained widespread acceptance. However, designing applications according to this style is still challenging. Common difficulties concern finding clear boundaries that guide decomposition while ensuring performance and scalability. With the aim of providing software architects and engineers with a systematic methodology, we introduce a novel actor-driven decomposition strategy to complement the domain-driven design and overcome some of its limitations by reaching a finer modularization yet enforcing performance and scalability improvements. The methodology uses a multi-level scalability assessment framework that supports decision-making over iterative steps. At each iteration, architecture alternatives are quantitatively evaluated at multiple granularity levels. The assessment helps architects to understand the extent to which architecture alternatives increase or decrease performance and scalability. We applied the methodology to drive further decomposition of the core microservices of a real data-intensive smart mobility application and an existing open-source benchmark in the e-commerce domain. The results of an in-depth evaluation show that the approach can effectively support engineers in ( i ) decomposing monoliths or coarse-grained microservices into more scalable microservices and ( ii ) comparing among alternative architectures to guide decision-making for their deployment in modern infrastructures that orchestrate lightweight virtualized execution units.

Automated identification of unstandardized medication data: a scalable and flexible data standardization pipeline using RxNorm on GEMINI multicenter hospital data.

Patient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity. The GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2090155 pharmacy orders from 245258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with efficient manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes. Manual coding was performed for 1948817 pharmacy orders and GEMINI-RxNorm successfully returned 1941389 (99.6%) orders. Recall was greater than 0.985 in all 13 drug classes, and the F1-score and precision remained above 0.90 in all drug classes, facilitating efficient manual review to achieve 100% precision. GEMINI-RxNorm saved time substantially compared with manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 to 5 s and reducing the number of rows needed to be reviewed by up to 99.99%. GEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.

Machine learning-based natural language processing to extract PD-L1 expression levels from clinical notes.

Introduction: PD-L1 expression is used to determine oncology patients' response to and eligibility for immunologic treatments; however, PD-L1 expression status often only exists in unstructured clinical notes, limiting ability to use it in population-level studies. Methods: We developed and evaluated a machine learning based natural language processing (NLP) tool to extract PD-L1 expression values from the nationwide Veterans Affairs electronic health record system. Results: The model demonstrated strong evaluation performance across multiple levels of label granularity. Mean precision of the overall PD-L1 positive label was 0.859 (sd, 0.039), recall 0.994 (sd, 0.013), and F1 0.921 (0.024). When a numeric PD-L1 value was identified, the mean absolute error of the value was 0.537 on a scale of 0 to 100. Conclusion: We presented an accurate NLP method for deriving PD-L1 status from clinical notes. By reducing the time and manual effort needed to review medical records, our work will enable future population-level studies in cancer immunotherapy.

Tri-granularity attribute reduction of three-way concept lattices

A real-world dataset often generates a huge concept lattice that may be difficult to understand and impractical to use. The principle of granular computing is that we can understand and use a complex whole at multiple levels of granularity. In light of this, we propose a unified tri-granularity model of various types of concept lattices, which allows us to examine a huge concept lattice at three levels of granularity (i.e., the elementary granularity, local granularity and global granularity). Attribute reduction plays a fundamental role in three-way concept analysis, it simplifies the expression of three-way concepts and thus contributes to a better perception of the knowledge in three-way concept lattices. The tri-granularity model suggests an opportunity to investigate the tri-granularity attribute reduction of three-way concept lattices. The existing research on attribute reduction works at the global granularity but pays little attention to the local granularity, or, even less, to the elementary granularity. Driven by these issues, we supply definitions and methods of local granularity and elementary granularity attribute reduction of three-way concept lattices. These newly proposed two levels of attribute reduction with the existing global granularity attribute reduction together provide a framework for the tri-granularity attribute reduction of three-way concept lattices. We further analyze the relationships among the three levels of attribute reduction. Moreover, the efficacy of our suggested approach is illustrated by an example via the trisections induced by three-way concepts. Finally, we design two tri-granularity attribute reduction algorithms whose effectiveness is further examined by numerical experiments.

TrinityRCL: Multi-Granular and Code-Level Root Cause Localization Using Multiple Types of Telemetry Data in Microservice Systems

The microservice architecture has been commonly adopted by large scale software systems exemplified by a wide range of online services. Service monitoring through anomaly detection and root cause analysis (RCA) is crucial for these microservice systems to provide stable and continued services. However, compared with monolithic systems, software systems based on the layered microservice architecture are inherently complex and commonly involve entities at different levels of granularity. Therefore, for effective service monitoring, these systems have a special requirement of multi-granular RCA. Furthermore, as a large proportion of anomalies in microservice systems pertain to problematic code, to timely troubleshoot these anomalies, these systems have another special requirement of RCA at the finest code-level. Microservice systems rely on telemetry data to perform service monitoring and RCA of service anomalies. The majority of existing RCA approaches are only based on a single type of telemetry data and as a result can only support uni-granular RCA at either application-level or service-level. Although there are attempts to combine metric and tracing data in RCA, their objective is to improve RCA's efficiency or accuracy rather than to support multi-granular RCA. In this paper, we propose a new RCA solution <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrinityRCL</i> that is able to localize the root causes of anomalies at multiple levels of granularity including application-level, service-level, host-level, and metric-level, with the unique capability of code-level localization by harnessing all three types of telemetry data to construct a causal graph representing the intricate, dynamic, and nondeterministic relationships among the various entities related to the anomalies. By implementing and deploying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrinityRCL</i> in a real production environment, we evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrinityRCL</i> against two baseline methods and the results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrinityRCL</i> has a significant performance advantage in terms of accuracy at the same level of granularity with comparable efficiency and is particularly effective to support large-scale systems with massive telemetry data.

Ontology-aware Prescription Recommendation in Treatment Pathways Using Multi-evidence Healthcare Data

For care of chronic diseases (e.g., depression, diabetes, hypertension), it is critical to identify effective treatment pathways that aim to promptly update the medication following the change of patient state and disease progression. This task is challenging because the optimal treatment pathway for each patient needs to be personalized due to the significant heterogeneity among individuals. Therefore, it is naturally promising to investigate how to use the abundant electronic health records to recommend effective and safe prescriptions. However, prescription recommendation needs to consider multiple aspects of life-critical evidence, such as the information relevance in terms of medical concepts, the health condition in terms of diagnosis history, and the further constraint in terms of side information (e.g., patient demographics and drug side effects). To this end, in this article, we propose a novel prescription recommendation framework named OntoPath to predict the next drug in disease treatment pathways, by building an ontology-aware hierarchical-attention model that integrates multiple medical evidence from domain knowledge guidance, medical history profiling, and side information utilization. Specifically, our method can be characterized from three aspects: (1) by incorporating the longitudinal diagnosis history, we enrich the profiling of patients in terms of comprehensive health conditions, which can largely influence a drug’s outcome on individual patients; (2) using the hierarchical disease and drug ontology structures, we are able to model the domain-specific relevance between patients and drugs at multiple levels of granularity and achieve in-depth collaborative filtering; (3) we introduce a pre-training stage to enhance the discriminativeness of network representations, which helps us obtain a premium model initialization to further boost the final recommendation training. We perform extensive experiments on a large-scale depression cohort with over 37,000 patients from a real-world medical claims database. The quantitative and qualitative results demonstrate the effectiveness of OntoPath through the consistent outperformance over state-of-the-art prescription recommendation baselines and the interpretation of model mechanism in case studies.

Hierarchical Information Enhancing Detector for Remotely Sensed Object Detection

For the remote sensing object detection task, two-stage networks are widely used due to their high accuracy. These networks roughly predict the proposal regions containing potential objects. It is assumed in these methods that the sizes of these regions are close to that of the corresponding real object. However, this assumption is not always true. Consequently, the detector is affected by the size-unfitting proposal regions. In this letter, a hierarchical information enhancing detector (HIE-Det) is advocated to deal with this issue. First, the important semantic reinjection (ISR) module is proposed to mitigate the lack of object semantics caused by the size-unfitting problem. Compared with the normal detectors, the ISR module increases the proportion of information on objects and improves the effectiveness of the detection model. Second, the object boundary enhancing (OBE) module is proposed to improve the robustness of the regression. The OBE module introduces the convolutional branch stacking multigranularity grids for the same proposal region. Multiple granularity levels improve the robustness of the model to the different degrees of proposal size unfitting. Finally, to evaluate the effectiveness of the HIE-Det on multiscale datasets in a balanced and effective manner, we propose the scale-modulating scores (S-scores), i.e., scale-modulating average precision (sAP) and scale-modulating average recall (sAR). Compared with the other comprehensive scores, the S-scores are rid of the sample amounts and give priority to weaker indices. Implementing the proposed HIE-Det, S-scores {sAP, sAR} are, respectively, improved from {17.3%, 29.7%} to {34.8%, 43.2%}, reaching the state-of-the-art performance on the HRRSD dataset. These experiments verify the effectiveness of the proposed HIE-Det.

PRIVEE: A Visual Analytic Workflow for Proactive Privacy Risk Inspection of Open Data.

Open data sets that contain personal information are susceptible to adversarial attacks even when anonymized. By performing low-cost joins on multiple datasets with shared attributes, malicious users of open data portals might get access to information that violates individuals' privacy. However, open data sets are primarily published using a release-and-forget model, whereby data owners and custodians have little to no cognizance of these privacy risks. We address this critical gap by developing a visual analytic solution that enables data defenders to gain awareness about the disclosure risks in local, joinable data neighborhoods. The solution is derived through a design study with data privacy researchers, where we initially play the role of a red team and engage in an ethical data hacking exercise based on privacy attack scenarios. We use this problem and domain characterization to develop a set of visual analytic interventions as a defense mechanism and realize them in PRIVEE, a visual risk inspection workflow that acts as a proactive monitor for data defenders. PRIVEE uses a combination of risk scores and associated interactive visualizations to let data defenders explore vulnerable joins and interpret risks at multiple levels of data granularity. We demonstrate how PRIVEE can help emulate the attack strategies and diagnose disclosure risks through two case studies with data privacy experts.

From classical mendelian randomization to causal networks for systematic integration of multi-omics.

The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting individual components do not consider the underlying relationships. In contrast, causal network models established in the principles of MR offer significant improvements to the classical MR framework for understanding omic data. Integration of these mostly distinct branches of statistics is a recent development, and we here review the current progress. To set the stage for causal network models, we review some recent progress in the classical MR framework. We then explain how to transition from the classical MR framework to causal networks. We discuss the identification of causal networks and evaluate the underlying assumptions. We also introduce some tests for sensitivity analysis and stability assessment of causal networks. We then review practical details to perform real data analysis and identify causal networks and highlight some of the utility of causal networks. The utilities with validated novel findings reveal the full potential of causal networks as a systems approach that will become necessary to integrate large-scale omic data.

Optimal Granularity Selection for Indoor Localization Detection with Wireless IoT Networks

Indoor localization detection acts as an important issue and has wide applications with wireless Internet of Things (IoT) networks. In recent years, the WiFi-based localization by using the latest artificial intelligence methods for improving the detection accuracy has attracted attention of many researchers. Granular computing is a newly emerged computing paradigm in artificial intelligence, which focuses on the structured thinking based on multiple levels of granularity. Thus, we introduce granular computing approaches to the task of wireless indoor localization detection, and a novel heuristic data discretization method is proposed based on the binary ant colony optimization and rough set (BACORS) for the selection of optimal granularity. For BACORS, the global optimal cut point set is searched based on the binary ant colony optimization to simultaneously discretize multiple attributes. Meanwhile, the accuracy of approximation classifications coined from rough sets is used to determine the consistent of multiple attribute data. To validate the effectiveness of BACORS, it is applied to a wireless indoor localization data set, and the experimental results indicate that it has promising performance.

Hierarchical Symptom Components in Early Psychosis.

Quantitative models of psychopathology can empirically guide subclassification of heterogeneous clinical presentations such as psychosis; they are particularly well-equipped to capture the nuanced symptomatology observed in first-episode psychosis. As well, components may be better aligned with biological variables. The current study sought to confirm and extend knowledge of the hierarchical structure of psychosis symptoms in first-episode psychosis. Based on past hierarchical work, we hypothesized that a 4 component level would be most closely associated with longitudinal disability. Participants with early-stage psychosis (N = 370) underwent clinical assessment with the scale for the assessment of positive symptoms (SAPS), scale for assessment of negative symptoms (SANS), and global assessment scale(GAS). A subset was assessed at 6 months (N = 221) and 1 year (N = 207). Hierarchical symptom components were extracted at 12 levels. The predictive utility of the components for global functioning was tested. As predicted, the 4-component model (reality distortion, thought disorder, inexpressivity, apathy/asociality) provided a superior prediction of functioning over other levels of the hierarchy. Baseline apathy/asociality longitudinally predicted functioning beyond the shared variance of the components at 6 months (b = -4.83, t(216) = -5.37, p < .001, R2adj = 0.12) and 1-year (b = -4.49, t(202) = -4.38, p < .001, R2adj = 0.09). The hierarchical structure of psychotic symptomatology and its external validity have been robustly established in independent, longitudinal first-episode psychosis samples. The established model incorporates multiple levels of granularity that can be flexibly applied based on the level that offers the greatest predictive utility for external validators.