Topic Modeling on Clinical Social Work Notes for Exploring Social Determinants of Health Factors

• URL: http://arxiv.org/abs/2212.01462v1
• Date: Fri, 2 Dec 2022 21:54:55 GMT
• Title: Topic Modeling on Clinical Social Work Notes for Exploring Social Determinants of Health Factors
• Authors: Shenghuan Sun, Travis Zack, Madhumita Sushil, Atul J. Butte
• Abstract summary: Clinical notes from social workers might provide a richer source of data on social determinants of health (SDoH) We retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181,644 patients at the University of California, San Francisco. We demonstrated that social work notes contain rich, unique, and otherwise unobtainable information on an individual's SDoH.
• Score: 0.30586855806896046
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Most research studying social determinants of health (SDoH) has focused on physician notes or structured elements of the electronic medical record (EMR). We hypothesize that clinical notes from social workers, whose role is to ameliorate social and economic factors, might provide a richer source of data on SDoH. We sought to perform topic modeling to identify robust topics of discussion within a large cohort of social work notes. We retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181,644 patients at the University of California, San Francisco. We used word frequency analysis and Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion. Word frequency analysis identified both medical and non-medical terms associated with specific ICD10 chapters. The LDA topic modeling analysis extracted 11 topics related to social determinants of health risk factors including financial status, abuse history, social support, risk of death, and mental health. In addition, the topic modeling approach captured the variation between different types of social work notes and across patients with different types of diseases or conditions. We demonstrated that social work notes contain rich, unique, and otherwise unobtainable information on an individual's SDoH.

Related papers

• Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework [0.0]
  Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID.
  arXiv  Detail & Related papers  (2025-03-14T20:13:08Z)
• Social Genome: Grounded Social Reasoning Abilities of Multimodal Models [61.88413918026431]
  Social Genome is the first benchmark for fine-grained, grounded social reasoning abilities of multimodal models. It contains 272 videos of interactions and 1,486 human-annotated reasoning traces related to inferences about these interactions. Social Genome is also the first modeling challenge to study external knowledge in social reasoning.
  arXiv  Detail & Related papers  (2025-02-21T00:05:40Z)
• Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare [47.23120247002356]
  Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG.
  arXiv  Detail & Related papers  (2024-11-29T20:35:01Z)
• Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods [17.83326146480516]
  Social determinants of health (SDoH) play a critical role in shaping health outcomes. We present a novel annotated corpus, the Pediatric Social History Corpus (PedSHAC) We evaluate the automatic extraction of detailed SDoH representations using fine-tuned and in-context learning methods.
  arXiv  Detail & Related papers  (2024-03-31T23:37:18Z)
• Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
  Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs. We propose a computational approach to model users' latent susceptibility levels.
  arXiv  Detail & Related papers  (2023-11-16T07:22:56Z)
• Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting [64.80538055623842]
  sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give. We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
  arXiv  Detail & Related papers  (2023-09-13T15:42:06Z)
• Discovering Mental Health Research Topics with Topic Modeling [13.651763262606782]
  This study aims to identify general trends in the field and pinpoint high-impact research topics by analyzing a large dataset of mental health research papers. Our dataset comprises 96,676 research papers pertaining to mental health, enabling us to examine the relationships between different topics using their abstracts. To enhance our analysis, we also generated word clouds to provide a comprehensive overview of the machine learning models applied in mental health research.
  arXiv  Detail & Related papers  (2023-08-25T05:25:05Z)
• Agent-Based Model: Simulating a Virus Expansion Based on the Acceptance of Containment Measures [65.62256987706128]
  Compartmental epidemiological models categorize individuals based on their disease status. We propose an ABM architecture that combines an adapted SEIRD model with a decision-making model for citizens. We illustrate the designed model by examining the progression of SARS-CoV-2 infections in A Coruna, Spain.
  arXiv  Detail & Related papers  (2023-07-28T08:01:05Z)
• Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes [0.27998963147546146]
  We developed and employed a Bidirectional Representations from Transformers (BERT) based approach to predict the prescription of targeted cancer therapy. We conducted a feature importance analysis to pinpoint the specific social circumstances that impact cancer therapy selection. Our findings indicate that significant disparities exist among breast cancer patients receiving different types of therapies based on social determinants of health.
  arXiv  Detail & Related papers  (2023-06-16T14:40:39Z)
• An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts [0.0]
  We construct and release a new annotated dataset with human-labelled explanations and classification of Interpersonal Risk Factors (IRF) affecting mental disturbance on social media. We establish baseline models on our dataset facilitating future research directions to develop real-time personalized AI models by detecting patterns of TBe and PBu in emotional spectrum of user's historical social media profile.
  arXiv  Detail & Related papers  (2023-05-30T04:08:40Z)
• Wearable Sensor-based Multimodal Physiological Responses of Socially Anxious Individuals across Social Contexts [7.85990334927929]
  We present results using passively collected data from a within-subject experiment that assessed physiological response across different social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, but that there is considerable variability in physiological response patterns even among these distinguishable contexts.
  arXiv  Detail & Related papers  (2023-04-03T18:34:54Z)
• Aggression and "hate speech" in communication of media users: analysis of control capabilities [50.591267188664666]
  Authors studied the possibilities of mutual influence of users in new media. They found a high level of aggression and hate speech when discussing an urgent social problem - measures for COVID-19 fighting. Results can be useful for developing media content in a modern digital environment.
  arXiv  Detail & Related papers  (2022-08-25T15:53:32Z)
• Muti-view Mouse Social Behaviour Recognition with Deep Graphical Model [124.26611454540813]
  Social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Because of the potential to create rich descriptions of mouse social behaviors, the use of multi-view video recordings for rodent observations is increasingly receiving much attention. We propose a novel multiview latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures.
  arXiv  Detail & Related papers  (2020-11-04T18:09:58Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.