Medication errors most commonly occur at the ordering or prescribing stage, potentially leading to medical complications and poor health outcomes. While it is possible to catch these errors using different techniques; the focus of this work is on textual and contextual analysis of prescription information to detect and prevent potential medication errors. In this paper, we demonstrate how to use BERT-based contextual language models to detect anomalies in written or spoken text based on a data set extracted from real-world medical data of thousands of patient records. The proposed models are able to learn patterns of text dependency and predict erroneous output based on contextual information such as patient data. The experimental results yield accuracy up to 96.63% for text input and up to 79.55% for speech input, which is satisfactory for most real-world applications.
Systematic Comparative Analysis of Large Pretrained Language Models on Contextualized Medication Event Extraction
Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT Center)（大数据智能卓越研究中心（CREDIT中心））；Department of Electrical and Computer Engineering（电气与计算机工程系）；Prairie View A&M University（普拉提维大学）；Texas A&M University System（德克萨斯A&M大学系统）
Ensemble BERT for Medication Event Classification on Electronic Health Records (EHRs)
Center of Excellence in Research and Education for Big Military Data Intelligence (CREDIT Center)（大数据智能研究与教育中心）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Prairie View A&M University（普拉姆维尤农业与机械大学）；Texas A&M University System（德克萨斯农工大学系统）
The clinical named entity recognition (CNER) task seeks to locate and classify clinical terminologies into predefined categories, such as diagnostic procedure, disease disorder, severity, medication, medication dosage, and sign symptom. CNER facilitates the study of side-effect on medications including identification of novel phenomena and human-focused information extraction. Existing approaches in extracting the entities of interests focus on using static word embeddings to represent each word. However, one word can have different interpretations that depend on the context of the sentences. …
PACE-RAG: Patient-Aware Contextual and Evidence-Constrained RAG for Clinical Drug Recommendation
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Department of Neurology, Seoul National University Hospital（首尔国立大学医院神经科）；Asan Medical Center, University of Ulsan, College of Medicine（釜山大学医学院阿桑医院）；Haeundae Paik Hospital, Inje University（庆尚大学医院海undae医院）
East China University of Science and Technology（华东理工大学）；Unisound（云知声）；Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine（上海中医药大学附属曙光医院）；DS Information Technology Co.,Ltd（DS信息技术有限公司）
Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which could lead to severe damages to both patients and hospital services. One of such error is the mismatches between diagnoses and prescriptions, which we address as 'medication anomaly' in the paper, and clinicians used to manually identify and correct them. With the development of machine learning techniques, researchers are able to train specific model for the task, but the process still requires expert knowledge to construct proper features, and few semantic …
Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties
Singapore General Hospital（新加坡中央医院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore National Eye Centre（新加坡全国眼科中心）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore Health Services（新加坡保健服务）
Large language models management of medications: three performance analyses
University of Colorado School of Medicine（科罗拉多大学医学院）；University of Georgia（佐治亚大学）；University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences（科罗拉多大学斯卡格斯药学院）；Mayo Clinic（梅奥诊所）；University of Georgia College of Pharmacy（佐治亚大学药学院）；Cleveland Clinic（克利夫兰诊所）
Errors in medication intake among elderly people are very common. One of the main causes for this is their loss of ability to retain information. The high amount of medicine intake required by the advanced age is another limiting factor. Thence, the design of an interactive aid system, preferably using natural language, to help the older population with medication is in demand. A chatbot based on a subtractive cluster algorithm, included in unsupervised learned models, is the chosen solution since the processing of natural languages is a necessary step in view to construct a chatbot able to an…
Grammatical error detection (GED) in non-native writing requires systems to identify a wide range of errors in text written by language learners. Error detection as a purely supervised task can be challenging, as GED datasets are limited in size and the label distributions are highly imbalanced. Contextualized word representations offer a possible solution, as they can efficiently capture compositional information in language and can be optimized on large amounts of unsupervised data. In this paper, we perform a systematic comparison of ELMo, BERT and Flair embeddings (Peters et al., 2017; Dev…
Lightweight Large Language Model for Medication Enquiry: Med-Pal
Singapore Health Services（新加坡保健服务集团）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore General Hospital（新加坡中央医院）；University of California San Francisco（加利福尼亚大学旧金山分校）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；MOHH Holdings (Singapore) Private Limited（新加坡卫生部控股（私人）有限公司）；SingHealth Duke-NUS Family Medicine Academic Clinical Programme（新加坡保健服务集团-杜克-新加坡国立大学家庭医学学术临床项目）
Improving Extraction of Clinical Event Contextual Properties from Electronic Health Records: A Comparative Study
King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯 NHS 基金会信托）；King’s College Hospital NHS Foundation Trust（国王学院医院 NHS 基金会信托）；University College London（伦敦大学学院）；Health Data Research UK London（英国健康数据研究伦敦中心）
Medication counseling with large language models: balancing flexibility and rigidity
Department of Engineering and Information Technology Åbo Akademi University（工程与信息科技系阿博阿卡迪米大学）；Experience Lab Åbo Akademi University（经验实验室阿博阿卡迪米大学）；Department of Natural and Health Sciences Åbo Akademi University（自然与健康科学系阿博阿卡迪米大学）；Department of Caring and Ethics University of Stavanger（护理与伦理系斯塔万格大学）
End-to-end modeling (E2E) of automatic speech recognition (ASR) blends all the components of a traditional speech recognition system into a unified model. Although it simplifies training and decoding pipelines, the unified model is hard to adapt when mismatch exists between training and test data. In this work, we focus on contextual speech recognition, which is particularly challenging for E2E models because it introduces significant mismatch between training and test data. To improve the performance in the presence of complex contextual information, we propose to use class-based language mod…
Robust state tracking for task-oriented dialogue systems currently remains restricted to a few popular languages. This paper shows that given a large-scale dialogue data set in one language, we can automatically produce an effective semantic parser for other languages using machine translation. We propose automatic translation of dialogue datasets with alignment to ensure faithful translation of slot values and eliminate costly human supervision used in previous benchmarks. We also propose a new contextual semantic parsing model, which encodes the formal slots and values, and only the last age…
It's challenging to customize transducer-based automatic speech recognition (ASR) system with context information which is dynamic and unavailable during model training. In this work, we introduce a light-weight contextual spelling correction model to correct context-related recognition errors in transducer-based ASR systems. We incorporate the context information into the spelling correction model with a shared context encoder and use a filtering algorithm to handle large-size context lists. Experiments show that the model improves baseline ASR model performance with about 50% relative word e…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
End-to-end (E2E) models have achieved promising results on multiple speech recognition benchmarks, and shown the potential to become the mainstream. However, the unified structure and the E2E training hamper injecting contextual information into them for contextual biasing. Though contextual LAS (CLAS) gives an excellent all-neural solution, the degree of biasing to given context information is not explicitly controllable. In this paper, we focus on incorporating context information into the continuous integrate-and-fire (CIF) based model that supports contextual biasing in a more controllable…
The awareness and mitigation of biases are of fundamental importance for the fair and transparent use of contextual language models, yet they crucially depend on the accurate detection of biases as a precursor. Consequently, numerous bias detection methods have been proposed, which vary in their approach, the considered type of bias, and the data used for evaluation. However, while most detection methods are derived from the word embedding association test for static word embeddings, the reported results are heterogeneous, inconsistent, and ultimately inconclusive. To address this issue, we co…
LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction
Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology（哈尔滨工业大学社会计算与信息检索研究中心）；State Key Laboratory of Cognitive Intelligence, iFLYTEK Research（讯飞研究院认知智能国家重点实验室）
Information Retrieval Lab（信息检索实验室）；Georgetown University（乔治城大学）
Offensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes three English sub-tasks: identifying the presence of offensive language (Sub-task A), identifying the presence of target in offensive language (Sub-task B), and identifying the categories of the target (Sub-task C). Our experiments explore using a domain-tuned contextualized language model (namely, BERT) for this task. We also experiment with different components and configurations (e.g., a multi-view SVM) stacked upon B…
Idiap Research Institute（伊迪亚普研究所）；Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院）；Brno University of Technology（布尔诺理工大学）；ReplayWell（ReplayWell公司）
Air traffic management and specifically air-traffic control (ATC) rely mostly on voice communications between Air Traffic Controllers (ATCos) and pilots. In most cases, these voice communications follow a well-defined grammar that could be leveraged in Automatic Speech Recognition (ASR) technologies. The callsign used to address an airplane is an essential part of all ATCo-pilot communications. We propose a two-steps approach to add contextual knowledge during semi-supervised training to reduce the ASR system error rates at recognizing the part of the utterance that contains the callsign. Init…
Bytedance AI Lab（字节跳动人工智能实验室）；Chinese Academy of Sciences（中国科学院）
Nowadays, most methods in end-to-end contextual speech recognition bias the recognition process towards contextual knowledge. Since all-neural contextual biasing methods rely on phrase-level contextual modeling and attention-based relevance modeling, they may encounter confusion between similar context-specific phrases, which hurts predictions at the token level. In this work, we focus on mitigating confusion problems with fine-grained contextual knowledge selection (FineCoS). In FineCoS, we introduce fine-grained knowledge to reduce the uncertainty of token predictions. Specifically, we first…
Spelling irregularities, known now as spelling mistakes, have been found for several centuries. As humans, we are able to understand most of the misspelled words based on their location in the sentence, perceived pronunciation, and context. Unlike humans, computer systems do not possess the convenient auto complete functionality of which human brains are capable. While many programs provide spelling correction functionality, many systems do not take context into account. Moreover, Artificial Intelligence systems function in the way they are trained on. With many current Natural Language Proces…
The quality of automatic speech recognition (ASR) is critical to Dialogue Systems as ASR errors propagate to and directly impact downstream tasks such as language understanding (LU). In this paper, we propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with LU to improve the performance of both tasks simultaneously. To measure the effectiveness of this approach we used a public benchmark, the 2nd Dialogue State Tracking (DSTC2) corpus. As a baseline approach, we trained task-specific Statistical Language Models (SLM) and fine-tuned state-of-the…
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；OPPO Research Institute（OPPO研究院）；Peng Cheng Laboratory（鹏城实验室）；Tencent Cloud Xiaowei（腾讯云小微）
Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. Recently, related researches focus on introducing character similarity from confusion set to enhance the CSC models, ignoring the context of characters that contain richer information. To make better use of contextual information, we propose a simple yet effective Curriculum Learning (CL) framework for the CSC task. With the help of our model-agnostic CL framework, existing CSC models will be trained from easy to difficult as humans learn Chinese characters and achieve further performance improvements. Extens…
Goal-oriented conversational interfaces are designed to accomplish specific tasks and typically have interactions that tend to span multiple turns adhering to a pre-defined structure and a goal. However, conventional neural language models (NLM) in Automatic Speech Recognition (ASR) systems are mostly trained sentence-wise with limited context. In this paper, we explore different ways to incorporate context into a LSTM based NLM in order to model long range dependencies and improve speech recognition. Specifically, we use context carry over across multiple turns and use lexical contextual cues…
ALTA Institute（ALTA研究所）；University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）
Targeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to identify when agreement is correct and when it is incorrect. To that end, we propose grammatical error detection as a diagnostic probe to evaluate token-level contextual representations for their knowledge of SVA. We evaluate contextual representations at each layer from five pre-trained English language models: BERT, XLNet, GPT-2, RoBERTa, and ELECTRA. We leverage publi…
Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings. We try to incorporate contextual information from pre-trained language model to leverage annotation and benefit multilingual scenarios. Results show strong potential of Bidirectional Encoder Representations from Transformers (BERT) in grammatical error correction task.
Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院）；Mashang Consumer Finance Co., Ltd.（马上消费金融股份有限公司）
