Taicheng Guo, Hai Wang, Chaochun Liu, Mohsen Golalikhani, Xin Chen, Xiangliang Zhang, Chandan K. Reddy. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The medical crowdsourced question answering (Q&A) websites are booming in recent years, and an increasingly large amount of patients and doctors are involved. The valuable information from these medical crowdsourced Q&A websites can benefit patients, doctors and the society. One key to unleash the power of these Q&A websites is to extract medical knowledge from the noisy question-answer pairs and filter out unrelated or even incorrect information. Facing the daunting scale of information generated on medical Q&A websites everyday, it is unrealistic to fulfill this task via supervised method due to the expensive annotation cost. In this paper, we propose a Medical Knowledge Extraction (MKE) system that can automatically provide high-quality knowledge triples extracted from the noisy question-answer pairs, and at the same time, estimate expertise for the doctors who give answers on these Q&A websites. The MKE system is built upon a truth discovery framework, where we jointly estimate trustworthiness of answers and doctor expertise from the data without any supervision. We further tackle three unique challenges in the medical knowledge extraction task, namely representation of noisy input, multiple linked truths, and the long-tail phenomenon in the data. The MKE system is applied to real-world datasets crawled fromxywy.com, one of the most popular medical crowdsourced Q&A websites. Both quantitative evaluation and case studies demonstrate that the proposed MKE system can successfully provide useful medical knowledge and accurate doctor expertise. We further demonstrate a real-world application,Ask A Doctor, which can automatically give patients suggestions to their questions.

Text classification is one of the indispensable tasks for natural language processing, which has many real applications. However, existing methods for text classification still cannot well effectively capture long-range and local-pattern features within texts due to the huge variation of text expression. Motivated by this, we propose skip-gram convolution to extract non-consecutive local n-gram patterns, which provide much more comprehensive information for varying text expressions, and help us to understand the human text better. We also employ the recurrent neural network to extract the long-range features from localized level to sequential and global level via the chain-like architecture. To demonstrate the effectiveness of our deep skip-gram networks, we conduct comprehensive experiments on eight large-scale datasets that are widely used for the text classification task. Experimental results show that our deep skip-gram networks can outperform most of competing state-of-the-art methods, especially significant on more complex and challenging datasets. Moreover, our model is very robust and can be generalized very well on different datasets, even without tuning the hyper-parameters for specific dataset.

In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant). Considering the 3D nature of lung CT data and the compactness of dual path networks (DPN), two deep 3D DPN are designed for nodule detection and classification respectively. Specifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is designed for nodule detection with 3D dual path blocks and a U-net-like encoder-decoder structure to effectively learn nodule features. For nodule classification, gradient boosting machine (GBM) with 3D dual path network features is proposed. The nodule classification subnetwork was validated on a public dataset from LIDC-IDRI, on which it achieved better performance than state-of-the-art approaches and surpassed the performance of experienced doctors based on image modality. Within the DeepLung system, candidate nodules are detected first by the nodule detection subnetwork, and nodule diagnosis is conducted by the classification subnetwork. Extensive experimental results demonstrate that DeepLung has performance comparable to experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset.

Nowadays, increasingly more people are receiving medical diagnoses from healthcare-related question answering platforms as people can get diagnoses quickly and conveniently. However, such diagnoses from non-expert crowdsourcing users are noisy or even wrong due to the lack of medical domain knowledge, which can cause serious consequences. To unleash the power of crowdsourcing on healthcare question answering, it is important to identify trustworthy answers and filter out noisy ones from user-generated data. Truth discovery methods estimate user reliability degrees and infer trustworthy information simultaneously, and thus these methods can be adopted to discover trustworthy diagnoses from crowdsourced answers. However, existing truth discovery methods do not take into account the rich semantic meanings of the answers. In the light of this challenge, we propose a method to automatically capture the semantic meanings of answers, where answers are represented as real-valued vectors in the semantic space. To learn such vector representations from noisy user-generated data, we tightly combine the truth discovery and vector learning processes. In this way, the learned vector representations enable truth discovery method to model the semantic relations among answers, and the information trustworthiness inferred by truth discovery can help the procedure of vector representation learning. To demonstrate the effectiveness of the proposed method, we collect a large-scale real-world dataset that involves 219,527 medical diagnosis questions and 23,657 non-expert users. Experimental results show that the proposed method improves the accuracy of identified trustworthy answers due to the successful consideration of answers' semantic meanings. Further, we demonstrate the fast convergence and good scalability of the proposed method, which makes it practical for real-world applications.

Given a health-related question (such as "I have a bad stomach ache. What should I do?"), a medical self-diagnosis Android inquires further information from the user, diagnoses the disease, and ultimately recommend best solutions. One practical challenge to build such an Android is to ask correct questions and obtain most relevant information, in order to correctly pinpoint the most likely causes of health conditions. In this paper, we tackle this challenge, named "relevant symptom question generation": Given a limited set of patient described symptoms in the initial question (e.g., "stomach ache"), what are the most critical symptoms to further ask the patient, in order to correctly diagnose their potential problems? We propose an augmented long short-term memory (LSTM) framework, where the network architecture can naturally incorporate the inputs from embedding vectors of patient described symptoms and an initial disease hypothesis given by a predictive model. Then the proposed framework generates the most important symptom questions. The generation process essentially models the conditional probability to observe a new and undisclosed symptom, given a set of symptoms from a patient as well as an initial disease hypothesis. Experimental results show that the proposed model obtains improvements over alternative methods by over 30% (both precision and mean ordinal distance).