Healthcare question answering assistance aims to provide customer healthcare information, which widely appears in both Web and mobile Internet. The questions usually require the assistance to have proficient healthcare background knowledge as well as the reasoning ability on the knowledge. Recently a challenge involving complex healthcare reasoning, HeadQA dataset, has been proposed, which contains multiple-choice questions authorized for the public healthcare specialization exam. Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge. These questions are the most challenging for current QA systems, and the current performance of the state-of-the-art method is slightly better than a random guess. In order to solve this challenging task, we present a Multi-step reasoning with Knowledge extraction framework (MurKe). The proposed framework first extracts the healthcare knowledge as supporting documents from the large corpus. In order to find the reasoning chain and choose the correct answer, MurKe iterates between selecting the supporting documents, reformulating the query representation using the supporting documents and getting entailment score for each choice using the entailment model. The reformulation module leverages selected documents for missing evidence, which maintains interpretability. Moreover, we are striving to make full use of off-the-shelf pre-trained models. With less trainable weight, the pre-trained model can easily adapt to healthcare tasks with limited training samples. From the experimental results and ablation study, our system is able to outperform several strong baselines on the HeadQA dataset.
From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
School of Computer Science and Electronic Engineering, University of Essex（埃塞克斯大学计算机科学与电子工程学院）；Harvard Medical School（哈佛医学院）；Boston Children’s Hospital（波士顿儿童医院）；University of Southampton（南安普顿大学）；Athena Research Centre（雅典娜研究中心）
Text-to-SQL Generation for Question Answering on Electronic Medical Records
Virginia Tech（弗吉尼亚理工大学）
电子医疗记录（EMR）包含全面的患者信息，并且通常存储在具有多个表的关系数据库中。从电子病历数据中有效地检索患者信息检索是医学专家的一项具有挑战性的任务。SQL到SQL生成方法通过首先预测关于数据库的给定问题的SQL查询，然后在数据库上执行查询来解决这个问题。然而，大多数现有的方法还没有适应医疗保健领域，由于缺乏医疗保健数据库到SQL数据集的学习模型特定于这个领域。此外，术语缩写的广泛使用和问题中可能的错别字为准确生成相应的SQL查询带来了额外的挑战。在本文中，我们通过开发一个基于深度学习的TRanslate-Edit Model for SQL（TREQS）生成来解决这些挑战，该模型采用广泛使用的序列到序列模型来直接生成给定问题的SQL查询，并使用注意力复制机制和特定于任务的查找表进一步执行所需的编辑。基于广泛使用的公共电子医疗数据库，我们创建了一个新的大规模的SQL对数据集，命名为MIMICSQL，以执行在医疗领域的SQL到SQL的生成任务。一组广泛的实验进行评估我们提出的模型MIMICSQL的性能。定量和定性的实验结果表明，我们所提出的方法在预测条件值的灵活性和效率，其鲁棒性随机问题的缩写和错别字。
Integrating UMLS Knowledge into Large Language Models for Medical Question Answering
National University of Singapore（新加坡国立大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；University of Tokyo（东京大学）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）；Singapore General Hospital（新加坡中央医院）；Zhejiang Lab（之江实验室）；National Institutes of Health（美国国立卫生研究院）；Yale University（耶鲁大学）
Interpretable Question Answering with Knowledge Graphs
Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, USA（威斯康星大学麦迪逊分校电子与计算机工程系）；Department of Computer Science, Banasthali Vidyapeeth, Rajasthan, India（班纳斯塔利大学计算机科学系）；Ansyst Consulting, Gurgaon, India（安西斯特咨询公司）；Huawei Ireland Research Center, Dublin, Ireland（华为爱尔兰研究中心）；Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA（弗吉尼亚理工学院布拉德利电子与计算机工程系）
LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines
Computer Science Institute, DiSIT, University of Piemonte Orientale（皮埃蒙特东方大学计算机科学研究所）；Integrated Laboratory of AI and Medical Informatics, DAIRI, SS. Antonio e Biagio e Cesare Arrigo Hospital（圣安东尼奥、比亚焦与切萨雷·阿里戈医院DAIRI人工智能与医学信息学综合实验室）
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
The Hong Kong Polytechnic University（香港理工大学）；West China Hospital of Sichuan University（四川大学华西医院）；Sichuan Academy of Medical Sciences（四川省医学科学院）；Sichuan Provincial People's Hospital（四川省人民医院）
HealthBranches: Synthesizing Clinically-Grounded Question Answering Datasets via Decision Pathways
University of Calabria（卡拉布里亚大学）；University of Catanzaro（卡坦扎罗大学）；University of Eastern Piedmont（东皮埃蒙特大学）；University of Pisa（比萨大学）；University of Cambridge（剑桥大学）
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
Grado Department of Industrial & Systems Engineering, Virginia Tech（弗吉尼亚理工大学格拉多工业与系统工程系）；Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；School of Mathematics, Physics, and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）；Center for Health Research, University of Southern Queensland（南昆士兰大学健康研究中心）
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Technical University of Munich（慕尼黑工业大学）；Ca’ Foscari University of Venice（威尼斯大学）；USI University of Lugano（瑞士意大利语区大学（卢加诺大学））；NVIDIA（英伟达）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Oxford（牛津大学）
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Georgia Institute of Technology（佐治亚理工学院）；Sutter Health（萨特健康）
准确性和可解释性是成功的预测模型的两个主要特征。通常情况下，必须选择支持复杂的黑盒模型，如递归神经网络（RNN）的准确性，而不是准确性较低但更可解释的传统模型，如逻辑回归。这种权衡在准确性和可解释性都很重要的医学中提出了挑战。我们通过开发用于电子健康记录（EHR）数据的REverse Time AttentIoN模型（RETAIN）来应对这一挑战。RETAIN在保持临床可解释性的同时实现了高准确性，并且基于两级神经注意力模型，该模型检测有影响力的既往访视和这些访视中的重要临床变量（例如关键诊断）。RETAIN通过以相反的时间顺序关注EHR数据来模仿医生的实践，以便最近的临床访问可能会受到更高的关注。RETAIN在一个大型健康系统EHR数据集上进行了测试，在8年的时间里，263K名患者完成了1400万次访问，并证明了与RNN等最先进方法相媲美的预测准确性和计算可扩展性，以及与传统模型相媲美的可解释性。
Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
Carnegie Mellon University（卡内基梅隆大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；GenBio AI（基因生物人工智能公司）；Intel（英特尔公司）
An Interpretable Recommendation Model for Psychometric Data, With an Application to Gerontological Primary Care
Institute of Mathematics；Computer Science, University of S\ ao Paulo S\ ao Carlos Brazil；Department of Gerontology, S\ ao Carlos Federal University S\ ao Carlos Brazil；School of Arts, Sciences；Humanities, University of S\ ao Paulo S\ ao Paulo Brazil；Computer Science, University of S\ ao Paulo；Department of Gerontology, S\ ao Carlos Federal University；Humanities, University of S\ ao Paulo
AI-Powered Dermatological Diagnosis: From Interpretable Models to Clinical Implementation A Comprehensive Framework for Accessible and Trustworthy Skin Disease Detection
Department of Business Analytics/Data Science Engineering University of New Haven（商业分析/数据科学工程系 罗德岛大学）；Department of Healthcare University of New Haven（医疗健康系 罗德岛大学）
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering
Zhejiang University（浙江大学）；University of Illinois Urbana–Champaign Institute（伊利诺伊大学厄巴纳-香槟分校研究院）；Sixth Medical Center of Chinese PLA General Hospital（中国人民解放军总医院第六医学中心）
