4,332 research outputs found
Plug-and-Play Medical Dialogue System
Medical dialogue systems aim to provide accurate answers to patients,
necessitating specific domain knowledge. Recent advancements in Large Language
Models (LLMs) have demonstrated their exceptional capabilities in the medical
Q&A domain, indicating a rich understanding of common sense. However, LLMs are
insufficient for direct diagnosis due to the absence of diagnostic strategies.
The conventional approach to address this challenge involves expensive
fine-tuning of LLMs. Alternatively, a more appealing solution is the
development of a plugin that empowers LLMs to perform medical conversation
tasks. Drawing inspiration from in-context learning, we propose PlugMed, a
Plug-and-Play Medical Dialogue System that facilitates appropriate dialogue
actions by LLMs through two modules: the prompt generation (PG) module and the
response ranking (RR) module. The PG module is designed to capture dialogue
information from both global and local perspectives. It selects suitable
prompts by assessing their similarity to the entire dialogue history and recent
utterances grouped by patient symptoms, respectively. Additionally, the RR
module incorporates fine-tuned SLMs as response filters and selects appropriate
responses generated by LLMs. Moreover, we devise a novel evaluation method
based on intent and medical entities matching to assess the efficacy of
dialogue strategies in medical conversations more effectively. Experimental
evaluations conducted on three unlabeled medical dialogue datasets, including
both automatic and manual assessments, demonstrate that our model surpasses the
strong fine-tuning baselines.Comment: 9 pages, 3 figures, Possible submission to Emnlp or AAA
A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction
This paper focuses on term-status pair extraction from medical dialogues
(MD-TSPE), which is essential in diagnosis dialogue systems and the automatic
scribe of electronic medical records (EMRs). In the past few years, works on
MD-TSPE have attracted increasing research attention, especially after the
remarkable progress made by generative methods. However, these generative
methods output a whole sequence consisting of term-status pairs in one stage
and ignore integrating prior knowledge, which demands a deeper understanding to
model the relationship between terms and infer the status of each term. This
paper presents a knowledge-enhanced two-stage generative framework (KTGF) to
address the above challenges. Using task-specific prompts, we employ a single
model to complete the MD-TSPE through two phases in a unified generative form:
we generate all terms the first and then generate the status of each generated
term. In this way, the relationship between terms can be learned more
effectively from the sequence containing only terms in the first phase, and our
designed knowledge-enhanced prompt in the second phase can leverage the
category and status candidates of the generated term for status generation.
Furthermore, our proposed special status "not mentioned" makes more terms
available and enriches the training data in the second phase, which is critical
in the low-resource setting. The experiments on the Chunyu and CMDD datasets
show that the proposed method achieves superior results compared to the
state-of-the-art models in the full training and low-resource settings.Comment: Published in Machine Intelligence Researc
Medical Dialogue Generation via Dual Flow Modeling
Medical dialogue systems (MDS) aim to provide patients with medical services,
such as diagnosis and prescription. Since most patients cannot precisely
describe their symptoms, dialogue understanding is challenging for MDS.
Previous studies mainly addressed this by extracting the mentioned medical
entities as critical dialogue history information. In this work, we argue that
it is also essential to capture the transitions of the medical entities and the
doctor's dialogue acts in each turn, as they help the understanding of how the
dialogue flows and enhance the prediction of the entities and dialogue acts to
be adopted in the following turn. Correspondingly, we propose a Dual Flow
enhanced Medical (DFMed) dialogue generation framework. It extracts the medical
entities and dialogue acts used in the dialogue history and models their
transitions with an entity-centric graph flow and a sequential act flow,
respectively. We employ two sequential models to encode them and devise an
interweaving component to enhance their interactions. Experiments on two
datasets demonstrate that our method exceeds baselines in both automatic and
manual evaluations.Comment: Accepted as Findings of ACL 202
CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation
The development of conversational agents to interact with patients and
deliver clinical advice has attracted the interest of many researchers,
particularly in light of the COVID-19 pandemic. The training of an end-to-end
neural based dialog system, on the other hand, is hampered by a lack of
multi-turn medical dialog corpus. We make the very first attempt to release a
high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease
named CDialog, with over 1K conversations collected from the online medical
counselling websites. We annotate each utterance of the conversation with seven
different categories of medical entities, including diseases, symptoms, medical
tests, medical history, remedies, medications and other aspects as additional
labels. Finally, we propose a novel neural medical dialog system based on the
CDialog dataset to advance future research on developing automated medical
dialog systems. We use pre-trained language models for dialogue generation,
incorporating annotated medical entities, to generate a virtual doctor's
response that addresses the patient's query. Experimental results show that the
proposed dialog models perform comparably better when supplemented with entity
information and hence can improve the response quality
A Novel Approach to Equating English Teachers’ and Chinese Teachers’ Ratings of Behaviours Characterised by Attention Deficit Hyperactivity Disorder
The diagnosis and treatment of ADHD rely on accurately identifying and interpreting symptoms. However, different raters may have different perceptions of ADHD symptoms, which can significantly impact ADHD diagnosis and prevalence rates. This study presented a novel way to compare ADHD symptom ratings between children from China and England while considering raters' differences.
The study developed a series of cartoon animations to measure the raters' leniency toward Children’s ADHD symptoms. The Many-facet Rasch Model was then applied to adjust the children's ADHD symptom ratings according to their raters' leniency. The study was conducted in Year 2 classrooms in schools in China and England, and participating teachers were asked to rate cartoon characters' ADHD behaviours according to their tolerance. They were also asked to rate 10 children selected randomly from their class about ADHD symptoms.
The study found that Chinese teachers were more lenient with children's ADHD behaviours than their English colleagues. Moreover, after adjusting for raters' leniency, Chinese children's ratings increased significantly, while English children's ratings decreased significantly. The study also found that Chinese children's ratings of ADHD behaviours were significantly higher than those of English children. Additionally, the Inter-rater Agreement was low among Chinese teachers. The findings highlight the significant impact of raters' differences on ADHD symptom ratings and the importance of equating teachers' ratings of children's ADHD symptoms to produce a relatively fair comparison between countries. The study's use of cartoon animations offers many advantages over text and videotape vignettes for cross-cultural studies. Moreover, the findings suggest that multi-informants are necessary for a single setting for diagnosing ADHD in children.
In conclusion, this study provides valuable insights into the impact of rater differences on ADHD symptom ratings and the importance of considering these differences when comparing prevalence rates between countries. Future research should explore ways to improve inter-rater agreement among raters and investigate other factors that may affect ADHD diagnosis and treatment
You Do Not Have to Get through This Alone: Interpersonal Emotion Regulation and Psychosocial Resources during the COVID-19 Pandemic across Four Countries
While experiencing the unpredictable events of the COVID-19 pandemic, we are likely to turn to people in order to regulate our emotions. In this research, we investigate how this interpersonal emotion regulation is connected to affective symptoms, above and beyond intrapersonal emotion regulation. Furthermore, we explore whether perceived psychosocial resources moderate these associations, i.e., if individuals reporting healthier social connections benefit differently from interpersonal emotion regulation. N = 1401 participants from the USA, UK, Germany, and Switzerland completed an online survey that included text samples. Affective symptoms (depression, adjustment disorder, fear of COVID-19) were examined based on self-reported as well as language-based indicators. As psychosocial resources, we examined social support, loneliness, attachment style, and trust. We defined latent variables for adaptive and maladaptive interpersonal emotion regulation and analyzed how they were associated with affective symptoms controlling for intrapersonal emotion regulation. Further, we analyzed how they interacted with psychosocial resources. Maladaptive interpersonal emotion regulation strategies were associated with affective symptoms. With lower psychosocial resources, the associations between interpersonal emotion regulation and depressive symptoms were more pronounced. The results highlight that maladaptive interpersonal emotion regulation is associated with worse mental health. These effects are not buffered by more psychosocial resources and are stronger for people with low psychosocial resources
Multi-centre classification of functional neurological disorders based on resting-state functional connectivity.
BACKGROUND
Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a "rule-in" procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting.
METHODS
This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation).
RESULTS
FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%).
CONCLUSIONS
The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms
Multi-centre classification of functional neurological disorders based on resting-state functional connectivity.
Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a "rule-in" procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting.
This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation).
FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%).
The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms
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