IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics最新文献

The imputation of missing values (IMV) in electronic health records tabular data is crucial to enable machine learning for patient-specific predictive modeling. While IMV methods are developed in biostatistics and recently in machine learning, deep learning-based solutions have shown limited success in learning tabular data. This paper proposes a novel attention-based missing value imputation framework that learns to reconstruct data with missing values leveraging between-feature (self-attention) or between-sample attentions. We adopt data manipulation methods used in contrastive learning to improve the generalization of the trained imputation model. The proposed self-attention imputation method outperforms state-of-the-art statistical and machine learning-based (decision-tree) imputation methods, reducing the normalized root mean squared error by 18.4% to 74.7% on five tabular data sets and 52.6% to 82.6% on two electronic health records data sets. The proposed attention-based missing value imputation method shows superior performance across a wide range of missingness (10% to 50%) when the values are missing completely at random.

In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical Efficacy (CE), often fall short in capturing the semantic intricacies of clinical contexts or overemphasize clinical details, undermining report clarity. To overcome these issues, our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs), like GPT-3.5 and GPT-4. Utilizing In-Context Instruction Learning (ICIL) and Chain of Thought (CoT) reasoning, our approach aligns LLM evaluations with radiologist standards, enabling detailed comparisons between human and AI-generated reports. This is further enhanced by a Regression model that aggregates sentence evaluation scores. Experimental results show that our "Detailed GPT-4 (5-shot)" model achieves a correlation that is 0.48, outperforming the METEOR metric by 0.19, while our "Regressed GPT-4" model shows even greater alignment(0.64) with expert evaluations, exceeding the best existing metric by a 0.35 margin. Moreover, the robustness of our explanations has been validated through a thorough iterative strategy. We plan to publicly release annotations from radiology experts, setting a new standard for accuracy in future assessments. This underscores the potential of our approach in enhancing the quality assessment of AI-driven medical reports.

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms. To assist in the diagnosis and phenotyping of delirium, we formed an expert panel to categorize diverse delirium symptoms, composed annotation guidelines, created a delirium corpus with diverse delirium symptoms, and developed NLP methods to extract delirium symptoms from clinical notes. We compared 5 state-of-the-art transformer models including 2 models (BERT and RoBERTa) from the general domain and 3 models (BERT_MIMIC, RoBERTa_MIMIC, and GatorTron) from the clinical domain. GatorTron achieved the best strict and lenient F1 scores of 0.8055 and 0.8759, respectively. We conducted an error analysis to identify challenges in annotating delirium symptoms and developing NLP systems. To the best of our knowledge, this is the first large language model-based delirium symptom extraction system. Our study lays the foundation for the future development of computable phenotypes and diagnosis methods for delirium.

A problem extension of the longest common substring (LCS) between two texts is the enumeration of all LCSs given a minimum length $k$ (ALCS- $k$ ), along with their positions in each text. In bioinformatics, an efficient solution to the ALCS- $k$ for very long texts -genomes or metagenomes- can provide useful insights to discover genetic signatures responsible for biological mechanisms. The ALCS- $k$ problem has two additional requirements compared to the LCS problem: one is the minimum length $k$ , and the other is that all common strings longer than $k$ must be reported. We present an efficient, two-stage ALCS- $k$ algorithm exploiting the spectrum of text substrings of length $k$ ( $k$ -mers). Our approach yields a worst-case time complexity loglinear in the number of $k$ -mers for the first stage, and an average-case loglinear in the number of common $k$ -mers for the second stage (several orders of magnitudes smaller than the total $k$ -mer spectrum). The space complexity is linear in the first phase (disk-based), and on average linear in the second phase (disk- and memory-based). Tests performed on genomes for different organisms (including viruses, bacteria and animal chromosomes) show that run times are consistent with our theoretical estimates; further, comparisons with MUMmer4 show an asymptotic advantage with divergent genomes.

Many studies have examined the impact of exercise and other physical activities in influencing the health outcomes of individuals. These physical activities entail an intricate sequence and series of physical anatomy, physiological movement, movement of the anatomy, etc. To better understand how these components interact with one another and their downstream impact on health outcomes, there needs to be an information model that conceptualizes all entities involved. In this study, we introduced our early development of an ontology model to computationally describe human physical activities and the various entities that compose each activity. We developed an open-sourced biomedical ontology called the Kinetic Human Movement Ontology that reused OBO Foundry terminologies and encoded in OWL2. We applied this ontology in modeling and linking a specific Tai Chi movement. The contribution of this work could enable modeling of information relating to human physical activity, like exercise, and lead towards information standardization of human movement for analysis. Future work will include expanding our ontology to include more expressive information and completely modeling entire sets of movement from human physical activity.

Social factors like family background, education level, financial status, and stress can impact public health outcomes, such as suicidal ideation. However, the analysis of social factors for suicide prevention has been limited by the lack of up-to-date suicide reporting data, variations in reporting practices, and small sample sizes. In this study, we analyzed 172,629 suicide incidents from 2014 to 2020 utilizing the National Violent Death Reporting System Restricted Access Database (NVDRS-RAD). Logistic regression models were developed to examine the relationships between demographics and suicide-related circumstances. Trends over time were assessed, and Latent Dirichlet Allocation (LDA) was used to identify common suicide-related social factors. Mental health, interpersonal relationships, mental health treatment and disclosure, and school/work-related stressors were identified as the main themes of suicide-related social factors. This study also identified systemic disparities across various population groups, particularly concerning Black individuals, young people aged under 24, healthcare practitioners, and those with limited education backgrounds, which shed light on potential directions for demographic-specific suicidal interventions.

Multivariate clinical time series data, such as those contained in Electronic Health Records (EHR), often exhibit high levels of irregularity, notably, many missing values and varying time intervals. Existing methods usually construct deep neural network architectures that combine recurrent neural networks and time decay mechanisms to model variable correlations, impute missing values, and capture the impact of varying time intervals. The complete data matrices thus obtained from the imputation task are used for downstream risk prediction tasks. This study aims to achieve more desirable imputation and prediction accuracy by performing both tasks simultaneously. We present a new multi-task deep neural network that incorporates the imputation task as an auxiliary task while performing risk prediction tasks. We validate the method on clinical time series imputation and in-hospital mortality prediction tasks using two publicly available EHR databases. The experimental results show that our method outperforms state-of-the-art imputation-prediction methods by significant margins. The results also empirically demonstrate that the incorporation of time decay mechanisms is a critical factor for superior imputation and prediction performance. The novel deep imputation-prediction network proposed in this study provides more accurate imputation and prediction results with EHR data. Future work should focus on developing more effective time decay mechanisms for simultaneously enhancing the imputation and prediction performance of multi-task learning models.

Data synthesis can address important data availability challenges in biomedical informatics. Quantitative evaluation of generative models may help understand their applications to synthesizing biomedical data. This poster paper examines state-of-the-art generative models used in medical imaging, such as StyleGAN and DDPM models, and evaluates their performance in learning data manifolds and in the visible features of generated samples. Results show that existing generative models have much to improve based on the studied measures.

Predictive analytics using Electronic Health Records (EHRs) have become an active research area in recent years, especially with the development of deep learning techniques. A popular EHR data analysis paradigm in deep learning is patient representation learning, which aims to learn a condensed mathematical representation of individual patients. However, EHR data are often inherently irregular, i.e., data entries were captured at different times as well as with different contents due to the individualized needs of each patient. Most of the work focused on the provision of deep neural networks with attention mechanisms that generate complete patient representations that can be readily used for downstream prediction tasks. However, such approaches fail to take patient similarity into account, which is generally used in clinical reasoning scenarios. This study presents a new Contrastive Graph Similarity Network for similarity calculation among patients in large EHR datasets. Particularly, we apply graph-based similarity analysis that explicitly extracts the clinical characteristics of each patient and aggregates the information of similar patients to generate rich patient representations. Experimental results on real-world EHR databases demonstrate the effectiveness and superiority of our method for the task of vital signs imputation and ICU patient deterioration prediction.

Deep neural networks have been increasingly integrated in healthcare applications to enable accurate predicative analyses. Sharing trained deep models not only facilitates knowledge integration in collaborative research efforts but also enables equitable access to computational intelligence. However, recent studies have shown that an adversary may leverage a shared model to learn the participation of a target individual in the training set. In this work, we investigate privacy-protecting model sharing for survival studies. Specifically, we pose three research questions. (1) Do deep survival models leak membership information? (2) How effective is differential privacy in defending against membership inference in deep survival analyses? (3) Are there other effects of differential privacy on deep survival analyses? Our study assesses the membership leakage in emerging deep survival models and develops differentially private training procedures to provide rigorous privacy protection. The experimental results show that deep survival models leak membership information and our approach effectively reduces membership inference risks. The results also show that differential privacy introduces a limited performance loss, and may improve the model robustness in the presence of noisy data, compared to non-private models.