Retrieving and ranking short medical questions with two stages neural matching model

• URL: http://arxiv.org/abs/2012.01254v1
• Date: Mon, 16 Nov 2020 07:00:35 GMT
• Title: Retrieving and ranking short medical questions with two stages neural matching model
• Authors: Xiang Li, Xinyu Fu, Zheng Lu, Ruibin Bai, Uwe Aickelin, Peiming Ge, Gong Liu
• Abstract summary: 80 percent of internet users have asked health-related questions online. Those representative questions and answers in medical fields are valuable raw data sources for medical data mining. We propose a novel two-stage framework for the semantic matching of query-level medical questions.
• Score: 3.8020157990268206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services. Online medical services become increasingly popular and active. According to US data in 2018, 80 percent of internet users have asked health-related questions online. Numerous data is generated in unprecedented speed and scale. Those representative questions and answers in medical fields are valuable raw data sources for medical data mining. Automated machine interpretation on those sheer amount of data gives an opportunity to assist doctors to answer frequently asked medical-related questions from the perspective of information retrieval and machine learning approaches. In this work, we propose a novel two-stage framework for the semantic matching of query-level medical questions.

Related papers

• Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation [46.42604861624895]
  We propose a new task of multi-modal medical concern summary generation. Nonverbal cues, such as patients' gestures and facial expressions, aid in accurately identifying patients' concerns. We construct the first multi-modal medical concern summary generation corpus.
  arXiv  Detail & Related papers  (2024-01-10T12:56:47Z)
• Med-Flamingo: a Multimodal Medical Few-shot Learner [58.85676013818811]
  We propose Med-Flamingo, a multimodal few-shot learner adapted to the medical domain. Based on OpenFlamingo-9B, we continue pre-training on paired and interleaved medical image-text data from publications and textbooks. We conduct the first human evaluation for generative medical VQA where physicians review the problems and blinded generations in an interactive app.
  arXiv  Detail & Related papers  (2023-07-27T20:36:02Z)
• Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision [53.692793122749414]
  We introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document.
  arXiv  Detail & Related papers  (2022-09-30T08:20:32Z)
• LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs [35.55690461944328]
  This paper presents a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely "LingYi" Our system utilizes automated medical procedures including medical triage, consultation, image-text drug recommendation and record.
  arXiv  Detail & Related papers  (2022-04-20T04:41:26Z)
• Medical Visual Question Answering: A Survey [55.53205317089564]
  Medical Visual Question Answering(VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer.
  arXiv  Detail & Related papers  (2021-11-19T05:55:15Z)
• Knowledge-Aware Neural Networks for Medical Forum Question Classification [13.22396257705293]
  We develop a medical knowledge-aware BERT-based model (MedBERT) that gives more weightage to medical concept-bearing words. We also contribute a multi-label dataset for the Medical Forum Question Classification (MFQC) task.
  arXiv  Detail & Related papers  (2021-09-27T15:57:21Z)
• Auto Response Generation in Online Medical Chat Services [0.0]
  We develop a smart auto-response generation mechanism for medical conversations that helps doctors respond to consultation requests efficiently. We explore over 900,000 anonymous, historical online messages between doctors and patients collected over nine months.
  arXiv  Detail & Related papers  (2021-04-26T17:45:10Z)
• Medical Information Retrieval and Interpretation: A Question-Answer based Interaction Model [7.990816079551592]
  Internet has become a very powerful platform where diverse medical information are expressed daily. Current search engines and recommendation systems still lack real time interactions that may provide more precise result generation. This paper proposes an intelligent and interactive system tied up with the vast medical big data repository on the web.
  arXiv  Detail & Related papers  (2021-01-24T07:01:06Z)
• MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation [86.38736781043109]
  We build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG. We propose two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset.
  arXiv  Detail & Related papers  (2020-10-15T03:34:33Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• Automated Question Answer medical model based on Deep Learning Technology [0.43748379918040853]
  This research will train an end-to-end model using the framework of RNN and the encoder-decoder to generate sensible and useful answers to a small set of medical/health issues. The proposed model was trained and evaluated using data from various online services, such as WebMD, HealthTap, eHealthForums, and iCliniq.
  arXiv  Detail & Related papers  (2020-05-21T01:40:01Z)

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.