arXiv subjects

Yanshan Wang

Publications and source records attributed to Yanshan Wang.

At least 55 records · Page 3Linked to original sources

arXiv2023

From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has captivated the research community, leading to debates about its application in healthcare, mainly due to concerns about transparency and related issues. Meanwhile, concerns about the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied, and decision-makers often fail to consider the significance of generative AI. In this paper, we propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare. We aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI in healthcare.

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arXiv2023

Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important.

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arXiv2023

Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses

A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, where such outcomes are already obvious to the user. To alleviate biases in human decision-making, it is worth considering a broad differential diagnosis, going beyond the most likely options. We introduce a new task, "less likely brainstorming," that asks a model to generate outputs that humans think are relevant but less likely to happen. We explore the task in two settings: a brain MRI interpretation generation setting and an everyday commonsense reasoning setting. We found that a baseline approach of training with less likely hypotheses as targets generates outputs that humans evaluate as either likely or irrelevant nearly half of the time; standard MLE training is not effective. To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans. We compare our method with several state-of-the-art controlled text generation models via automatic and human evaluations and show that our models' capability of generating less likely outputs is improved.

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arXiv2023

ReDWINE: A Clinical Datamart with Text Analytical Capabilities to Facilitate Rehabilitation Research

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients' physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face several challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

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arXiv2023

Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing

Strategy training is a multidisciplinary rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Strategy training has been shown in randomized, controlled clinical trials to be a more feasible and efficacious intervention for promoting independence than traditional rehabilitation approaches. A standardized fidelity assessment is used to measure adherence to treatment principles by examining guided and directed verbal cues in video recordings of rehabilitation sessions. Although the fidelity assessment for detecting guided and directed verbal cues is valid and feasible for single-site studies, it can become labor intensive, time consuming, and expensive in large, multi-site pragmatic trials. To address this challenge to widespread strategy training implementation, we leveraged natural language processing (NLP) techniques to automate the strategy training fidelity assessment, i.e., to automatically identify guided and directed verbal cues from video recordings of rehabilitation sessions. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task. The best performance was achieved by the BERT model with a 0.8075 F1-score. This BERT model was verified on an external validation dataset collected from a separate major regional health system and achieved an F1 score of 0.8259, which shows that the BERT model generalizes well. The findings from this study hold widespread promise in psychology and rehabilitation intervention research and practice.

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arXiv2022

Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks

Clinical Natural Language Processing (NLP) has become an emerging technology in healthcare that leverages a large amount of free-text data in electronic health records (EHRs) to improve patient care, support clinical decisions, and facilitate clinical and translational science research. Recently, deep learning has achieved state-of-the-art performance in many clinical NLP tasks. However, training deep learning models usually requires large annotated datasets, which are normally not publicly available and can be time-consuming to build in clinical domains. Working with smaller annotated datasets is typical in clinical NLP and therefore, ensuring that deep learning models perform well is crucial for the models to be used in real-world applications. A widely adopted approach is fine-tuning existing Pre-trained Language Models (PLMs), but these attempts fall short when the training dataset contains only a few annotated samples. Few-Shot Learning (FSL) has recently been investigated to tackle this problem. Siamese Neural Network (SNN) has been widely utilized as an FSL approach in computer vision, but has not been studied well in NLP. Furthermore, the literature on its applications in clinical domains is scarce. In this paper, we propose two SNN-based FSL approaches for clinical NLP, including Pre-Trained SNN (PT-SNN) and SNN with Second-Order Embeddings (SOE-SNN). We evaluated the proposed approaches on two clinical tasks, namely clinical text classification and clinical named entity recognition. We tested three few-shot settings including 4-shot, 8-shot, and 16-shot learning. Both clinical NLP tasks were benchmarked using three PLMs, including BERT,BioBERT, and BioClinicalBERT. The experimental results verified the effectiveness of the proposed SNN-based FSL approaches in both NLP tasks.

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arXiv2022

Toward Improving Health Literacy in Patient Education Materials with Neural Machine Translation Models

Health literacy is the central focus of Healthy People 2030, the fifth iteration of the U.S. national goals and objectives. People with low health literacy usually have trouble understanding health information, following post-visit instructions, and using prescriptions, which results in worse health outcomes and serious health disparities. In this study, we propose to leverage natural language processing techniques to improve health literacy in patient education materials by automatically translating illiterate languages in a given sentence. We scraped patient education materials from four online health information websites: MedlinePlus.gov, Drugs.com, Mayoclinic.org and Reddit.com. We trained and tested the state-of-the-art neural machine translation (NMT) models on a silver standard training dataset and a gold standard testing dataset, respectively. The experimental results showed that the Bidirectional Long Short-Term Memory (BiLSTM) NMT model outperformed Bidirectional Encoder Representations from Transformers (BERT)-based NMT models. We also verified the effectiveness of NMT models in translating health illiterate languages by comparing the ratio of health illiterate language in the sentence. The proposed NMT models were able to identify the correct complicated words and simplify into layman language while at the same time the models suffer from sentence completeness, fluency, readability, and have difficulty in translating certain medical terms.

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arXiv2022

Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes

Semantic textual similarity (STS) in the clinical domain helps improve diagnostic efficiency and produce concise texts for downstream data mining tasks. However, given the high degree of domain knowledge involved in clinic text, it remains challenging for general language models to infer implicit medical relationships behind clinical sentences and output similarities correctly. In this paper, we present a graph-augmented cyclic learning framework for similarity estimation in the clinical domain. The framework can be conveniently implemented on a state-of-art backbone language model, and improve its performance by leveraging domain knowledge through co-training with an auxiliary graph convolution network (GCN) based network. We report the success of introducing domain knowledge in GCN and the co-training framework by improving the Bio-clinical BERT baseline by 16.3% and 27.9%, respectively.

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arXiv2022

HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing

Deep learning algorithms are dependent on the availability of large-scale annotated clinical text datasets. The lack of such publicly available datasets is the biggest bottleneck for the development of clinical Natural Language Processing(NLP) systems. Zero-Shot Learning(ZSL) refers to the use of deep learning models to classify instances from new classes of which no training data have been seen before. Prompt-based learning is an emerging ZSL technique where we define task-based templates for NLP tasks. We developed a novel prompt-based clinical NLP framework called HealthPrompt and applied the paradigm of prompt-based learning on clinical texts. In this technique, rather than fine-tuning a Pre-trained Language Model(PLM), the task definitions are tuned by defining a prompt template. We performed an in-depth analysis of HealthPrompt on six different PLMs in a no-data setting. Our experiments prove that prompts effectively capture the context of clinical texts and perform remarkably well without any training data.

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arXiv2021

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive Model

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arXiv2021

Neural Language Models with Distant Supervision to Identify Major Depressive Disorder from Clinical Notes

Major depressive disorder (MDD) is a prevalent psychiatric disorder that is associated with significant healthcare burden worldwide. Phenotyping of MDD can help early diagnosis and consequently may have significant advantages in patient management. In prior research MDD phenotypes have been extracted from structured Electronic Health Records (EHR) or using Electroencephalographic (EEG) data with traditional machine learning models to predict MDD phenotypes. However, MDD phenotypic information is also documented in free-text EHR data, such as clinical notes. While clinical notes may provide more accurate phenotyping information, natural language processing (NLP) algorithms must be developed to abstract such information. Recent advancements in NLP resulted in state-of-the-art neural language models, such as Bidirectional Encoder Representations for Transformers (BERT) model, which is a transformer-based model that can be pre-trained from a corpus of unsupervised text data and then fine-tuned on specific tasks. However, such neural language models have been underutilized in clinical NLP tasks due to the lack of large training datasets. In the literature, researchers have utilized the distant supervision paradigm to train machine learning models on clinical text classification tasks to mitigate the issue of lacking annotated training data. It is still unknown whether the paradigm is effective for neural language models. In this paper, we propose to leverage the neural language models in a distant supervision paradigm to identify MDD phenotypes from clinical notes. The experimental results indicate that our proposed approach is effective in identifying MDD phenotypes and that the Bio- Clinical BERT, a specific BERT model for clinical data, achieved the best performance in comparison with conventional machine learning models.

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arXiv2021

Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision

Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle factor changes and interventions. Analyzing electronic health records (EHR) of patients with AD can help us better understand lifestyle's effect on AD. However, lifestyle information is typically stored in clinical narratives. Thus, the objective of the study was to demonstrate the feasibility of natural language processing (NLP) models to classify lifestyle factors (e.g., physical activity and excessive diet) from clinical texts. We automatically generated labels for the training data by using a rule-based NLP algorithm. We conducted weak supervision for pre-trained Bidirectional Encoder Representations from Transformers (BERT) models on the weakly labeled training corpus. These models include the BERT base model, PubMedBERT(abstracts + full text), PubMedBERT(only abstracts), Unified Medical Language System (UMLS) BERT, Bio BERT, and Bio-clinical BERT. We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle factors for AD. These models were compared on the developed Gold Standard Corpus (GSC) on the two case studies. The PubmedBERT(Abs) model achieved the best performance for physical activity, with its precision, recall, and F-1 scores of 0.96, 0.96, and 0.96, respectively. Regarding classifying excessive diet, the Bio BERT model showed the highest performance with perfect precision, recall, and F-1 scores. The proposed approach leveraging weak supervision could significantly increase the sample size, which is required for training the deep learning models. The study also demonstrates the effectiveness of BERT models for extracting lifestyle factors for Alzheimer's disease from clinical notes.

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arXiv2020

Clinical Concept Extraction: a Methodology Review

Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. Objectives In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. Methods Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. Results A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.

cs.IR↗

arXiv2020

A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19

COVID-19 has resulted in an ongoing pandemic and as of 12 June 2020, has caused more than 7.4 million cases and over 418,000 deaths. The highly dynamic and rapidly evolving situation with COVID-19 has made it difficult to access accurate, on-demand information regarding the disease. Online communities, forums, and social media provide potential venues to search for relevant questions and answers, or post questions and seek answers from other members. However, due to the nature of such sites, there are always a limited number of relevant questions and responses to search from, and posted questions are rarely answered immediately. With the advancements in the field of natural language processing, particularly in the domain of language models, it has become possible to design chatbots that can automatically answer consumer questions. However, such models are rarely applied and evaluated in the healthcare domain, to meet the information needs with accurate and up-to-date healthcare data. In this paper, we propose to apply a language model for automatically answering questions related to COVID-19 and qualitatively evaluate the generated responses. We utilized the GPT-2 language model and applied transfer learning to retrain it on the COVID-19 Open Research Dataset (CORD-19) corpus. In order to improve the quality of the generated responses, we applied 4 different approaches, namely tf-idf, BERT, BioBERT, and USE to filter and retain relevant sentences in the responses. In the performance evaluation step, we asked two medical experts to rate the responses. We found that BERT and BioBERT, on average, outperform both tf-idf and USE in relevance-based sentence filtering tasks. Additionally, based on the chatbot, we created a user-friendly interactive web application to be hosted online.

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arXiv2020

How Good is Artificial Intelligence at Automatically Answering Consumer Questions Related to Alzheimer's Disease?

Alzheimer's Disease (AD) is the most common type of dementia, comprising 60-80% of cases. There were an estimated 5.8 million Americans living with Alzheimer's dementia in 2019, and this number will almost double every 20 years. The total lifetime cost of care for someone with dementia is estimated to be $350,174 in 2018, 70% of which is associated with family-provided care. Most family caregivers face emotional, financial and physical difficulties. As a medium to relieve this burden, online communities in social media websites such as Twitter, Reddit, and Yahoo! Answers provide potential venues for caregivers to search relevant questions and answers, or post questions and seek answers from other members. However, there are often a limited number of relevant questions and responses to search from, and posted questions are rarely answered immediately. Due to recent advancement in Artificial Intelligence (AI), particularly Natural Language Processing (NLP), we propose to utilize AI to automatically generate answers to AD-related consumer questions posted by caregivers and evaluate how good AI is at answering those questions. To the best of our knowledge, this is the first study in the literature applying and evaluating AI models designed to automatically answer consumer questions related to AD.

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arXiv2019

Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying novel patterns and relations from EHRs without using human created labels. In this paper, we investigate the application of unsupervised machine learning models in discovering latent disease clusters and patient subgroups based on EHRs. We utilized Latent Dirichlet Allocation (LDA), a generative probabilistic model, and proposed a novel model named Poisson Dirichlet Model (PDM), which extends the LDA approach using a Poisson distribution to model patients' disease diagnoses and to alleviate age and sex factors by considering both observed and expected observations. In the empirical experiments, we evaluated LDA and PDM on three patient cohorts with EHR data retrieved from the Rochester Epidemiology Project (REP), for the discovery of latent disease clusters and patient subgroups. We compared the effectiveness of LDA and PDM in identifying latent disease clusters through the visualization of disease representations learned by two approaches. We also tested the performance of LDA and PDM in differentiating patient subgroups through survival analysis, as well as statistical analysis. The experimental results show that the proposed PDM could effectively identify distinguished disease clusters by alleviating the impact of age and sex, and that LDA could stratify patients into more differentiable subgroups than PDM in terms of p-values. However, the subgroups discovered by PDM might imply the underlying patterns of diseases of greater interest in epidemiology research due to the alleviation of age and sex. Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.

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arXiv2019

CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model

Background: Widespread adoption of electronic health records (EHRs) has enabled secondary use of EHR data for clinical research and healthcare delivery. Natural language processing (NLP) techniques have shown promise in their capability to extract the embedded information in unstructured clinical data, and information retrieval (IR) techniques provide flexible and scalable solutions that can augment the NLP systems for retrieving and ranking relevant records. Methods: In this paper, we present the implementation of Cohort Retrieval Enhanced by Analysis of Text from EHRs (CREATE), a cohort retrieval system that can execute textual cohort selection queries on both structured and unstructured EHR data. CREATE is a proof-of-concept system that leverages a combination of structured queries and IR techniques on NLP results to improve cohort retrieval performance while adopting the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to enhance model portability. The NLP component empowered by cTAKES is used to extract CDM concepts from textual queries. We design a hierarchical index in Elasticsearch to support CDM concept search utilizing IR techniques and frameworks. Results: Our case study on 5 cohort identification queries evaluated using the IR metric, P@5 (Precision at 5) at both the patient-level and document-level, demonstrates that CREATE achieves an average P@5 of 0.90, which outperforms systems using only structured data or only unstructured data with average P@5s of 0.54 and 0.74, respectively.

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arXiv2018

MedSTS: A Resource for Clinical Semantic Textual Similarity

The wide adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely depends on our ability to efficiently extract and consolidate information embedded in clinical text where natural language processing (NLP) techniques are essential. Semantic textual similarity (STS) that measures the semantic similarity between text snippets plays a significant role in many NLP applications. In the general NLP domain, STS shared tasks have made available a huge collection of text snippet pairs with manual annotations in various domains. In the clinical domain, STS can enable us to detect and eliminate redundant information that may lead to a reduction in cognitive burden and an improvement in the clinical decision-making process. This paper elaborates our efforts to assemble a resource for STS in the medical domain, MedSTS. It consists of a total of 174,629 sentence pairs gathered from a clinical corpus at Mayo Clinic. A subset of MedSTS (MedSTS_ann) containing 1,068 sentence pairs was annotated by two medical experts with semantic similarity scores of 0-5 (low to high similarity). We further analyzed the medical concepts in the MedSTS corpus, and tested four STS systems on the MedSTS_ann corpus. In the future, we will organize a shared task by releasing the MedSTS_ann corpus to motivate the community to tackle the real world clinical problems.

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