Medical Question Answering System Research Articles

A Liver Cancer Question-Answering System Based on Next-Generation Intelligence and the Large Model Med-PaLM 2

Chronic diseases are the "number one killer" threatening human life and health. In recent years, the incidence of chronic diseases has been rising, and the trend is younger, and the prevention and control situation is very serious. This study introduces a Liver Cancer Question-Answering System (LCQAS) that leverages next-generation artificial intelligence and the large model Med-PaLM 2 The Med-PaLM 2 medical question answering system, powered by next-generation intelligence and large language models, represents a cutting-edge tool in the healthcare domain. Leveraging advanced artificial intelligence techniques and the capabilities of large language models like Med-PaLM 2, this system aims to provide accurate and comprehensive responses to medical inquiries and queries. With its intuitive interface and contextually relevant answers, LCQAS represents a significant advancement in medical information retrieval for liver cancer management.

Design of Intelligent Question Answering System for Hospital Online Triage based on Knowledge Graph

At present, people pay more and more attention to medical and health issues, and more and more Internet users are eager to obtain medical related knowledge from the Internet. However, despite the large amount of medical data spread on the Internet, it is difficult for ordinary Internet users to effectively obtain and use these data through search engines. To solve this problem, this paper designs and implements a medical question answering system based on knowledge map, so that users can quickly obtain the relevant knowledge in the medical field they want by asking questions, and realize the effective use of medical data on the Internet. At present, there are few question answering systems in the medical and health field, so the medical question answering system designed in this paper has a broad application prospect. The medical knowledge map constructed in the system can also be used in more application scenarios such as medical chat robots in the future, further promoting the process of intelligence in the medical field.

MAGE: Multi-scale Context-aware Interaction based on Multi-granularity Embedding for Chinese Medical Question Answer Matching

Background and ObjectiveThe Chinese medical question answer matching (cMedQAM) task is the essential branch of the medical question answering system. Its goal is to accurately choose the correct response from a pool of candidate answers. The relatively effective methods are deep neural network-based and attention-based to obtain rich question-and-answer representations. However, those methods overlook the crucial characteristics of Chinese characters: glyphs and pinyin. Furthermore, they lose the local semantic information of the phrase by generating attention information using only relevant medical keywords. To address this challenge, we propose the multi-scale context-aware interaction approach based on multi-granularity embedding (MAGE) in this paper. MethodsWe adapted ChineseBERT, which integrates Chinese characters glyphs and pinyin information into the language model and fine-tunes the medical corpus. It solves the common phenomenon of homonyms in Chinese. Moreover, we proposed a context-aware interactive module to correctly align question and answer sequences and infer semantic relationships. Finally, we utilized the multi-view fusion method to combine local semantic features and attention representation. ResultsWe conducted validation experiments on the three publicly available datasets, namely cMedQA V1.0, cMedQA V2.0, and cEpilepsyQA. The proposed multi-scale context-aware interaction approach based on the multi-granularity embedding method is validated by top-1 accuracy. On cMedQA V1.0, cMedQA V2.0, and cEpilepsyQA, the top-1 accuracy on the test dataset was improved by 74.1%, 82.7%, and 60.9%, respectively. Experimental results on the three datasets demonstrate that our MAGE achieves superior performance over state-of-the-art methods for the Chinese medical question answer matching tasks. ConclusionsThe experiment results indicate that the proposed model can improve the accuracy of the Chinese medical question answer matching task. Therefore, it may be considered a potential intelligent assistant tool for the future Chinese medical answer question system.

Improving Question Analysis for Arabic Question Answering in the Medical Domain

Question analysis is a basic module in a question answering (QA) system, and its quality affects the performance of QA system. In this paper, we address the problem of Arabic question analysis in the medical domain where several specific challenges are met. The major challenging issue in processing Arabic medical question is the need for ambiguity resolution. Nevertheless, this issue has not been well studied in related works. Our question analysis uses dictionaries and transducers to analyze any medical question, factoid or complex. This module detects important elements of the question, including: different words in the question that identify what the user wants to ask for, and the nature of the expected answer. To identify well these elements a step of disambiguation is applied. Then, the words used in the question will be extended by adding new words that connect semantically to those in the question. Experimentation of the question analysis module of our Arabic medical question answering system show interesting results.

A medical question answering system using large language models and knowledge graphs

A medical question answering system using large language models and knowledge graphs

Answering medical questions in Chinese using automatically mined knowledge and deep neural networks: an end-to-end solution

BackgroundMedical information has rapidly increased on the internet and has become one of the main targets of search engine use. However, medical information on the internet is subject to the problems of quality and accessibility, so ordinary users are unable to obtain answers to their medical questions conveniently. As a solution, researchers build medical question answering (QA) systems. However, research on medical QA in the Chinese language lags behind work on English-based systems. This lag is mainly due to the difficulty of constructing a high-quality knowledge base and the underutilization of medical corpora in the Chinese language.ResultsThis study developed an end-to-end solution to implement a medical QA system for the Chinese language with low cost and time. First, we created a high-quality medical knowledge graph from hospital data (electronic health/medical records) in a nearly automatic manner that trained a supervised model based on data labeled using bootstrapping techniques. Then, we designed a QA system based on a memory-based neural network and attention mechanism. Finally, we trained the system to generate answers from the knowledge base and a QA corpus on the internet.ConclusionsBootstrapping and deep neural network techniques can construct a knowledge graph from electronic health/medical records with satisfactory precision and coverage. Our proposed context bridge mechanisms perform training with a variety of language features. Our QA system can achieve state-of-the-art quality in answering medical questions with constrained topics. As we evaluated, complex Chinese language processing techniques, such as segmentation and parsing, were not necessary for practice and complex architectures were not necessary to build the QA system. Lastly, we created an application using our method for internet QA usage.

A bio- medical Question Answering system for the Malayalam language using word embedding and Bidirectional Encoder Representation from Transformers.

Conversational search is the dominant intent of Question Answering, which is achieved through different NLP techniques, Deep Learning models. The advent of Transformer models has been a breakthrough in Natural Language Processing applications, which has attained a benchmark on state of NLP tasks such as question answering. Here we propose a semantic Malayalam Question Answering system that automatically answers the queries related to health issues. The Biomedical Question-Answering, especially in the Malayalam language, is a tedious and challenging task. The proposed model uses a neural network-based Bidirectional Encoder Representation from Transformers (BERT), to implement the question answering system. In this study, we investigate how to train and fine-tune a BERT model for Question-Answering. The system has been tested with our own annotated Malayalam SQUAD form health dataset. In comparing the result with our previous works - Word embedding and RNN based model, identified we find that our BERT model is more accurate than the previous models and achieves an F1 score of 86%.

Evaluation System Framework of Artificial Intelligence Applications in Medical Diagnosis and Treatment

Current medical artificial intelligence applications and products are confronted the dilemma of lacking standardized practical evaluation guidelines and management methods. This research adopts the Donabedian medical quality management classic model and the DeLone & Mclean benefit evaluation model to develop a basic framework of the medical artificial intelligence application evaluation system for auxiliary diagnosis and treatment and to design the corresponding evaluation procedures. This paper illustrates the overall project framework, followed by a detailed structure and construction process of the model proposed for fast and accurate medical evaluations, NHCKG. NHCKG provides a holistic view of diseases, medical regulations and evaluations. The proposed NHCKG can provide partial solutions to significant challenges faced by knowledge graphs and natural language processing (NLP). Based on the knowledge graph, the structure of a related medical question answering system is elaborated along with its advantages and disadvantages. The evaluation process can take this procedure as a blueprint for exploring standardized and practical evaluation of medical artificial intelligence applications. The process aims to build a concrete measurement basis for medical artificial intelligence products and to promote the healthy and stable development of the medical artificial intelligence industry.

Research on Medical Question Answering System Based on Knowledge Graph

To meet the high-efficiency question answering needs of existing patients and doctors, this system integrates medical professional knowledge, knowledge graphs, and question answering systems that conduct man-machine dialogue through natural language. This system locates the medical field, uses crawler technology to use vertical medical websites as data sources, and uses diseases as the core entity to construct a knowledge graph containing 44,000 knowledge entities of 7 types and 300,000 entities of 11 kinds. It is stored in the Neo4j graph database, using rule-based matching methods and string-matching algorithms to construct a domain lexicon to classify and query questions. This system has specific practical value in the medical field knowledge graph and question answering system.

A Persian Medical Question Answering System

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three main modules: question processing, document retrieval, and answer extraction. For the question processing module, a sequential architecture is designed which retrieves the main concept of a question by using different components. In these components, rule-based methods, natural language processing, and dictionary-based techniques are used. In the document retrieval module, the documents are indexed and searched using the Lucene library. The retrieved documents are ranked using similarity detection algorithms and the highest-ranked document is selected to be used by the answer extraction module. This module is responsible for extracting the most relevant section of the text in the retrieved document. During this research, different customized language processing tools such as part of speech tagger and lemmatizer are also developed for Persian. Evaluation results show that this system performs well for answering different questions about diseases and drugs. The accuracy of the system for 500 sample questions is 83.6%.

Multi-task Character-Level Attentional Networks for Medical Concept Normalization

Recognizing standard medical concepts in the colloquial text is significant for kinds of applications such as the medical question answering system. Recently, word-level neural network methods, which can learn complex informal expression features, achieved remarkable performance on this task. However, they have two main limitations: (1) Existing word-level methods cannot learn character structure features inside words and suffer from “Out-of-vocabulary” (OOV) words, which are common in noisy colloquial text. (2) Since these methods handle the normalization task as a classification issue, concept phrases are represented by category labels. Hence the word morphological information inside the concept is lost. In this work, we present a multi-task character-level attentional network model for medical concept normalization. Specifically, the character-level encoding scheme of our model can alleviate the OOV word problem. The attention mechanism can effectively exploit the word morphological information through multi-task training. It generates higher attention weights on domain-related positions in the text sequence, helping the downstream convolution focus on the characters that are related to medical concepts. To test our model, we first introduce a labeled Chinese dataset (overall 314,991 records) for this task. Other two real-world English datasets are also used. Our model outperforms state-of-the-art methods on all three datasets. Besides, by adding four types noises to the datasets, we validate the robustness of our model against common noises in the colloquial text.

Case Based Reasioning (CBR) for Medical Question Answering System

In this research, the researchers implement a medical Question Answering System (QAS), a complaint system in the form of sentences or paragraphs of questions about the complaint (illness) suffered by a person. Afterwards, the system will give answer to the questions with answers in the form of diagnosis based on the system knowledge. The system in this study has knowledge of the system obtained based on Case Based Reasoning (CBR) method from the previous cases stored in the database. When there is a new case, the system will perform a matching process using CBR and Sorenson Coefficient calculations to find out which the previous cases have the highest percentage of matches with the new case. Then the selected previous cases will be taken and given to the new case. Testing is processed by using 2 types of testing, expert validation testing with result of 28 data of appropriate test from 30 test data and accuracy testing resulting of 93,33% from the appropriate test data.

An Algorithm Research on Potential Problem Reactor in Medical Online Question Answering Community

Objective: Based on the online question-and-answer community of Internet medicine, a weighted user-personal model is proposed in viewof the pain point of the users' queries in the medical question-and-answer community which can not be effectively answered in time. Predictswhether the recommended user can respond timely and effectively to the problem to improve the accuracy of the proposed results. .Methods: Using the user's historical activity data in the community, a model of user's personal domain was proposed based on query-likelanguage model to describe the degree of user's concern in different field.Results: In the experiment based on Baidu medical question-answering system data, compared with the existing algorithms, the potentialproblem respondents in this paper have better recommendation effect.

Impact of a GRADE-based medical question answering system on physician behaviour: a randomised controlled trial

Physicians are frequently faced with questions related to their patients’ care that they cannot answer. A vast number of randomised trials have tested a wide variety of behaviour-changing strategies designed...