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Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors. To further the development of clinical AI systems, the Diagnostic Reasoning Benchmark (DR.BENCH) was introduced as a comprehensive generative AI framework, comprised of six tasks representing key components in clinical reasoning. We present a comparative analysis of in-domain versus out-of-domain language models as well as multi-task versus single task training with a focus on the problem summarization task in DR.BENCH (Gao et al., 2023). We demonstrate that a multi-task, clinically trained language model outperforms its general domain counterpart by a large margin, establishing a new state-of-the-art performance, with a ROUGE-L score of 28.55. This research underscores the value of domain-specific training for optimizing clinical diagnostic reasoning tasks.

ACL 2023

Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning

multi-task training

large language models

summarization

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.

Context-aware Medication Event Extraction from Unstructured Text

In the medical field, there are many clinical texts such as electronic medical records, and research on Japanese natural language processing using these texts has been conducted. One such research involves Recognizing Textual
Entailment (RTE) in clinical texts using a semantic analysis and logical inference system, ccg2lambda. However, it is difficult for existing inference systems to correctly determine the entailment relations, if the input sentence contains medical domain specific paraphrases such as disease names. In this study, we pro-
pose a method to supplement the equivalence relations of disease names as axioms by identifying candidates for paraphrases that lack in theorem proving. Candidates of paraphrases are identified by using a model for the NER task for disease names and a disease name dictionary. We also construct an inference test set that requires knowledge injection of disease names and evaluate our inference system. Experiments showed that our inference system was able to correctly infer for 106 out of 149 inference test sets.

Knowledge Injection for Disease Names in Logical Inference between Japanese Clinical Texts

This work introduces a novel three-class annotation scheme for text-based dementia classification in patients, based on their recorded visit interactions. Multiple models were developed utilising BERT, RoBERTa and DistilBERT. Two approaches were employed to improve the representation of dementia samples: oversampling the underrepresented data points in the original Pitt dataset and combining the Pitt with the Holland and Kempler datasets. The DistilBERT models trained on either an oversampled Pitt dataset or the combined dataset performed best in classifying the dementia class. Specifically, the model trained on the oversampled Pitt dataset and the one trained on the combined dataset obtained state-of-the-art performance with 98.8% overall accuracy and 98.6% macro-averaged F1-score, respectively. The models' outputs were manually inspected through saliency highlighting, using Local Interpretable Model-agnostic Explanations (LIME), to provide a better understanding of its predictions.

Training Models on Oversampled Data and a Novel Multi-class Annotation Scheme for Dementia Detection

Recent advances in large language models (LLMs) have generated significant interest in their application across various domains including healthcare. However, there is limited data on their safety and performance in real-world scenarios. This study uses data collected using an autonomous telemedicine clinical assistant. The assistant asks symptom-based questions to elicit patient concerns and allows patients to ask questions about their post-operative recovery. We utilise real-world postoperative questions posed to the assistant by a cohort of 120 patients to examine the safety and appropriateness of responses generated by a recent popular LLM by OpenAI, ChatGPT. We demonstrate that LLMs have the potential to helpfully address routine patient queries following routine surgery. However, important limitations around the safety of today’s models exist which must be considered.

Can Large Language Models Safely Address Patient Questions Following Cataract Surgery?

We present our work on building large scale sequence-to-sequence models for generating clinical note from patient-doctor conversation. This is formulated as an abstractive summarization task for which we use encoder-decoder transformer model with pointer-generator. We discuss various modeling enhancements to this baseline model which include using subword and multiword tokenization scheme, prefixing the targets with a chain-of-clinical-facts, and training with contrastive loss that is defined over various candidate summaries. We also use flash attention during training and query chunked attention during inference to be able to process long input and output sequences and to improve computational efficiency. Experiments are conducted on a dataset containing about 900K encounters from around 1800 healthcare providers covering 27 specialties. The results are broken down into primary care and non-primary care specialties. Consistent accuracy improvements are observed across both of these categories.

Large Scale Sequence-to-Sequence Models for Clinical Note Generation from Patient-Doctor Conversations

We propose a method to automate orthodontic diagnosis with natural language processing. It is worthwhile to assist dentists with such technology to prevent errors by inexperienced dentists and to reduce the workload of experienced ones. However, text length and style inconsistencies in medical findings make an automated orthodontic diagnosis with deep-learning models difficult. In this study, we improve the performance of automatic diagnosis utilizing short summaries of medical findings written in a consistent style by experienced dentists. Experimental results on 970 Japanese medical findings show that summarization consistently improves the performance of various machine learning models for automated orthodontic diagnosis. Although BERT is the model that gains the most performance with the proposed method, the convolutional neural network achieved the best performance.

Automated Orthodontic Diagnosis from a Summary of Medical Findings

Recent advancements in natural language processing (NLP) have been driven by large language models (LLMs), thereby revolutionizing the field. Our study investigates the impact of diverse pre-training strategies on the performance of Turkish clinical language models in a multi-label classification task involving radiology reports, with a focus on overcoming language resource limitations. Additionally, for the first time, we evaluated the simultaneous pre-training approach by utilizing limited clinical task data. We developed four models: TurkRadBERT-task v1, TurkRadBERT-task v2, TurkRadBERT-sim v1, and TurkRadBERT-sim v2. Our results revealed superior performance from BERTurk and TurkRadBERT-task v1, both of which leverage a broad general-domain corpus. Although task-adaptive pre-training is capable of identifying domain-specific patterns, it may be prone to overfitting because of the constraints of the task-specific corpus. Our findings highlight the importance of domain-specific vocabulary during pre-training to improve performance. They also affirmed that a combination of general domain knowledge and task-specific fine-tuning is crucial for optimal performance across various categories. This study offers key insights for future research on pre-training techniques in the clinical domain, particularly for low-resource languages.

Harnessing the Power of BERT in the Turkish Clinical Domain: Pretraining Approaches for Limited Data Scenarios

Over the last years, an increasing number of publicly available, semantically annotated medical corpora have been released for the German language. While their annotations cover comparable semantic classes, the synergies of such efforts have not been explored yet. This is due to substantial differences in the data schemas (syntax) and annotated entities (semantics), which hinder the creation of common meta-datasets. For instance, it is unclear whether named entity recognition (NER) taggers trained on one or more of such datasets are useful to detect entities in any of the other datasets. In this work, we create harmonized versions of German medical corpora using the BigBIO framework, and make them available to the community. Using these as a meta-dataset, we perform a series of cross-corpus evaluation experiments on two settings of aligned labels. These consist in fine-tuning various pre-trained Transformers on different combinations of training sets, and testing them against each dataset separately. We find that a) trained NER models generalize poorly, with F1 scores dropping approx. 20 pp. on unseen test data, and b) current pre-trained Transformer models for the German language do not systematically alleviate this issue. However, our results suggest that models benefit from additional training corpora in most cases, even if these belong to different medical fields or text genres.

A Meta-dataset of German Medical Corpora: Harmonization of Annotations and Cross-corpus NER Evaluation

Post-stroke speech and language deficits (aphasia) significantly impact patients’ quality of life. Many with mild symptoms remain undiagnosed, and the majority do not receive the intensive doses of therapy recommended, due to healthcare costs and/or inadequate services. Automatic Speech Recognition (ASR) may help overcome these difficulties by improving diagnostic rates and providing feedback during tailored therapy. However, its performance is often unsatisfactory due to the high variability in speech errors and scarcity of training datasets. This study assessed the performance of Whisper, a recently released end-to-end model, in patients with post-stroke aphasia (PWA). We tuned its hyperparameters to achieve the lowest word error rate (WER) on aphasic speech. WER was significantly higher in PWA compared to age-matched controls (10.3% vs 38.5%, p<0,001 ). We demonstrated that worse WER was related to the more severe aphasia as measured by expressive (overt naming, and spontaneous speech production) and receptive (written and spoken comprehension) language assessments. Stroke lesion size did not affect the performance of Whisper. Linear mixed models accounting for demographic factors, therapy duration, and time since stroke, confirmed worse Whisper performance with left hemispheric frontal lesions.
We discuss the implications of these findings for how future ASR can be improved in PWA.

Uncovering the Potential for a Weakly Supervised End-to-End Model in Recognising Speech from Patient with Post-Stroke Aphasia

We explore temporal dependency graph (TDG) parsing in the clinical domain. We leverage existing annotations on the THYME dataset to semi-automatically construct a TDG corpus. Then we propose a new natural language inference (NLI) approach to TDG parsing, and evaluate it both on general domain TDGs from wikinews and the newly constructed clinical TDG corpus. We achieve competitive performance on general domain TDGs with a much simpler model than prior work. On the clinical TDGs, our method establishes the first result of TDG parsing on clinical data with 0.79/0.88 micro/macro F1.

Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning

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Context-aware Medication Event Extraction from Unstructured Text

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Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning

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Context-aware Medication Event Extraction from Unstructured Text

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PRESENTATIONS

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COMPANY

RESOURCES

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