Reviewerly/exHarmony huggingface dataset:
Integrated Local and Global Information for Health Risk Prediction Model

Electronic health record (EHR) data has been widely used in health risk prediction models, and it has an important preventive and intervention role in healthcare. Existing approaches typically regard EHR data in a monolayer observational model, and they assume that visits are monotonically decreasing in importance over time. However, in healthcare practice, clinical experts usually focus on diseases and visits that are closely related to the target disease. In addition, the duration of different categories of diseases has a fixed model, as chronic diseases are usually consistently diagnosed during patient visits. To make full use of this disease category information, a hierarchical self-attentive model is proposed that can model patient representations at both the local and global levels. Specifically, a disease duration matrix with multiple times is constructed for disease clustering. We combine the category information to compute dependencies between diseases and disease embeddings. We further explore the pattern of patient health development from a spatio-temporal perspective. Visit embeddings are updated by learning the effects between different visits via a self-attentive mechanism. In addition, the time interval, a special kind of medical event, is introduced to enhance visit sequence temporal modeling. Extensive experiments on two real-world datasets demonstrate the sota performance of the model. At the same time, we demonstrate the plausibility and interpretability of the model through case studies.
Attention-based BRCNN for Chinese Medical Question Answering

Recently, the development of automatic question answering in the field of smart medicine has attracted much attention, especially in the field of Chinese medicine. Many methods have been presented for Chinese medical question answering. However, due to redundant information in sentences, existing Chinese medical question answering methods, like BRCNN, are difficult to choose the best answer for patients’ questions. Aiming at the problem, this paper proposes a BiGRU model based on attention mechanism, and adds it to BRCNN. Specifically, our algorithm first inputs the word vector generated by BERT into the attention module to get the attention of the sentence. Secondly, the attention and word vector of the sentence are input into BiGRU together, and the sequence information of the sentence is obtained through the interaction of attention and word vector. Thirdly, the sequence information of sentences is used as the input of multi-scale CNNs to obtain n-gram information of different sizes of sentences. Finally, the best answer to the question is selected by calculating the cosine similarity between the question and the answer. We conducted some experiments to verify the proposed model by comparing it with the baseline model on the cMedQA and cMedQA2 data sets. The results show that the accuracy of the proposed model is 0.5% and 0.2% higher than that of the baseline model, respectively.
Morphological Guided Causal Constraint Network for Medical Image Multi-Object Segmentation

Multi-objective segmentation (MOS) in medical images is to simultaneously extract multiple regions of interest in the medical images. Due to the unbalanced distribution of samples and the similarity and significant differences between features in medical images, current methods still struggle to achieve satisfactory results. In this context, we propose a novel Morphological Guided Causal Constrain segmentation network (MCCSeg) for medical image multi-object segmentation. We introduced a Causal Constrain Module (CCM) for feature decorrelation by sample reweighting. The morphological guidance module (MG) is designed to extract the boundary features as the prior shape information for enhancing feature representation. Our experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively.
DILM-ICD: A Deep Iterative Learning Model for Automatic ICD Coding

Automatic International Classification of Disease (ICD) coding plays a crucial role in assigning ICD codes to electronic medical records. This task presents a challenging multi-label text classification problem due to the vast number of ICD codes and the imbalanced label distribution. However, accurately predicting all labels simultaneously is extremely difficult for such the large label space. In this paper, we propose a novel model called Deep Iterative Learning Model (DILM-ICD), which uses an iterative learning framework to perform automatic ICD coding task. The iterative learning framework can refine the prediction results by repeating the iteration modules, which simulates the human-like coding process. In addition, we propose a multi-head text-label matching mechanism, which combines the embedding ICD description information to better match the relationship between text and label. The combination of the iterative learning framework with the multi-head text-label matching mechanism enables the model pay attention to lowfrequency ICD codes. DILM-ICD is evaluated on the MIMIC-III-full dataset and MIMIC-III-50 dataset. The experimental results show that DILM-ICD achieves state-of-the-art results across multiple evaluation metrics, which demonstrates the effectiveness of our proposed model.
Semantic-Similarity Collaborative Knowledge Distillation Framework for Whole Slide Image Classification

Pathological whole slide images (WSIs) are of great importance for clinical diagnosis, and the classification of WSIs is often regarded as a multiple instance learning (MIL) problem. However, most MIL frameworks only use bag labels to train their models, neglecting the potential information from negative bags, which is a waste of existing labeled information. In this paper, we propose a semantic-similarity collaborative knowledge distillation framework (SSC) for WSI classification, which differs from other methods by leveraging the latent information of the data to the fullest extent and considering both semantic space and similarity space, then transferring this knowledge to a lightweight network through knowledge distillation, achieving end-to-end fast inference. Specifically, our proposed method includes three branches: the teacher, student, and regular branches. The teacher branch explores the similarity space and includes a similarity-based classifier that outputs classification results based on the similarity space mapped from negative bags. The output of the similarity-based classifier is fused with that of the semantic-based classifier and serves as the soft pseudo-label for the student branch. To improve the quality of the labels, we use views under different augmentation modes as inputs for the three branches and encourage weakly augmented features to be as similar as possible to strongly augmented features through consistency regularization. Moreover, the teacher, student, and regular branches share the weights of the classifier and projection head, thus strengthening their knowledge exchange. The proposed method exhibits excellent performance on Camelyon16.
Adaptive Semi-Supervised Learning from Stronger Augmented Transformations of Medical Texts

Semi-supervised learning (SSL) is a promising approach to dealing with the problem of insufficient medical labeled data. Recent methods grouped into paradigms of consistency regularization and pseudo-labeling have outstanding performances in processing images but achieve limited improvements in processing medical texts. This paper proposes MedMatch, a novel semi-supervised learning (SSL) method addressing the challenges of medical text processing due to its discrete nature and lack of quality text augmentation. MedMatch can detect abnormal model states from anomalous data, reducing their impact while improving consistency regularization effectiveness. It also optimizes low entropy from pseudo-label learning. We introduce robust medical text augmentation pipelines to increase text view divergence, enhancing model learning. Comparative experiments show MedMatch significantly outperforms previous SSL methods on two major medical benchmarks.
Machine Learning techniques application to predict the quality of life degree in breast cancer patients

Breast cancer is among the leading causes of mortality in women of all ages worldwide. Prevention and timely diagnosis can make the difference between life and death for most patients, and it is for this reason that today there is a great deal of focus on raising public awareness about the possibility of using the various techniques of preventive screening available today. In cases where surgery is necessary, beyond the purely medical aspects, it is important to take into account also the psychological aspects, which are sometimes underestimated with negative consequences for the patient. However, it is very difficult to determine a priori what the final outcome of the intervention will be, especially with regard to the level of personal satisfaction of patients. The aim of this work is to identify a possible correlation between the commonly used measurements of breast morphology, necessary for the assessment of any surgical procedure, and the personal satisfaction level of the patients, based on the results of anonymous questionnaires, through the use of machine learning techniques.
Research on Named Entity Recognition in Traditional Chinese Medicine Herbal Texts

Objective To address the issues in named entity recognition (NER) in the field of traditional Chinese medicine (TCM), this study proposes a method for identifying entities in TCM herbal literature; Methods We identify and describe the types of knowledge entities and entity relationships involved in herbal literature. We apply the BIO sequence labeling method to generate a training corpus dataset and use our self-developed CNLP text annotation system for text annotation. The Bert model is employed for recognizing named entities; Results The Bert model achieved entity recognition results for various entities in TCM herbal literature with precision (P) of 71.49%, recall (R) of 72.33%, and F1 score of 71.91%; Conclusion The Bert model demonstrates a certain level of applicability in recognizing various entities in TCM herbal literature. This model is helpful in extracting valuable structured information from a large volume of text data.
Latent Topic Extraction as a Source of Labeling in Natural Language Processing

Supervised machine learning algorithms depend on accurate labeling of target data to develop models that can derive relationships between input data and the target data. One major hindrance for developing supervised machine learning models capable of predicting the correct target label of unseen data rests on the quality of the data used to train the models, which often depends on having a subject matter expert (SME) create a labeled dataset to train the model on. Given the scarcity of such experts in many fields, the time needed to analyze data for labeling, and subjective differences among experts, ways to reduce the complexity associated with creating meaningful datasets are needed. In this work, we explore the use of two unsupervised topic modeling algorithms, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) as potential methods for reducing the complexities in the labeling process. Specifically, we obtained COVID patient message data labeled by a SME and compared the overlap in topics designated as COVID versus not by the two algorithms to those of the SME. For each of the topic modeling algorithms, we found a strong degree of overlap in the COVID vs. non-COVID patient message labels with that of the SME, suggesting that the methodology could be used to provide synergies for developing labeled data sets used for clinically meaningful models.
Transfer Learning Classification Algorithm by Exploiting Multi-source Adaptation and Similarity of Omic Data

Multi-source transfer learning has received widespread concern due to outstanding predictive performance. But the limitations imposed by the initial outputs of the source classifiers on the decision values of the target samples, as well as the significant differences between the source and target domains, negative transfer may occur. A novel algorithm called Multi-Source Adaption and Similarity Learning (MSASL) is proposed, which utilizes the predicted probabilities of each target source classifier and the learned correlation probabilities to calculate the classification probabilities of target domain samples. It can dynamically learn the correlations between different source and target domains in a common subspace and discard some irrelevant source domains by measuring the domain differences. Simulation experiments have demonstrated the effectiveness of the method.
Enabling Large Language Models to Think Twice When Its Answer Is Unreliable: A Case Study In Cancer Screening

To enhance the accuracy and reliability of the response of large language models (LLMs) in professional domain-specific question answering, this paper proposes an approach to enhance the capabilities of LLMs by prompting them to "think twice." This methodology encourages a second-pass processing, allowing for deeper analysis and more refined outputs. We developed a prototype-logic-inspired query optimizer (PQO) to enhance the response of LLM. It refines initial queries using few-shot learning and self-reflection, leading to more insightful questions. Our experimental results show that the proposed approach obtains empirically validated answers.
REAF: ROI Extraction and Adaptive Fusion for Breast Cancer Diagnosis in Ultrasound Images

Breast cancer leads to the top cause of death among women worldwide. Early detection of breast cancer is the most effective way to reduce the risk of death and increase the likelihood of survival. Breast ultrasound imaging is an essential test for early detection but its accurate evaluation is difficult due to the limited number of experienced sonographers. So the computer-aided system for breast cancer diagnosis on ultrasound images is vitally necessary. Recent breast cancer diagnosis methods are mostly developed by improving or transferring visual deep models to breast cancer diagnosis. These methods ignored the characteristics of breast cancer on ultrasound images. Some methods fuse the pre-defined region of interest (ROI) to adapt the characteristics of ultrasound images but cannot diagnose those samples whose ROIs are not defined. In this paper, we propose a novel framework, ROI Extraction and Adaptive Fusion (REAF), for breast cancer diagnosis on ultrasound images. To make the framework be applied to the samples without pre-defined ROIs as well as incorporate the characteristics of breast cancer on ultrasound images, the proposed REAF firstly extracts the ROI of breast tumor areas as a confidence degree map. Then we propose an adaptive fusion method to fuse the extracted ROI into the downstream diagnostic model. In addition, we propose a pair augmentation method to increase the variety of samples for enhancing the generalization of the proposed REAF. The experimental results demonstrate that the REAF surpasses state-of-the-art visual classification models and breast cancer methods. For the evaluation of the REAF, the AUC reaches 95.52%, the accuracy reaches 88.97%, the specificity reaches 92.86%, the precision reaches 89.43%, the recall reaches 86.82% and the f1-score reaches 88.05%.
Radiology Report Generation via Structured Knowledge-Enhanced Multi-modal Attention and Contrastive Learning

The automated generation of radiology reports has attracted significant attention in the field of bioinformatics. Currently, the main limitations of this task include insufficient utilization of prior medical knowledge, lack of efficient knowledge fusion algorithms, and less distinctiveness between different generated reports. To address these issues, we propose a novel algorithm for radiology report generation, which includes Structured Knowledge-Enhanced Multi-modal Attention (SKEMA) and Dual-Branch Contrastive Learning (DBCL) for the first time. SKEMA aims to effectively bridge the gap between visual and prior knowledge by leveraging the high-order adjacency matrix of the knowledge graph to weightedly fuse image features and knowledge features. We enhance both features through masking, and use the original features and augmented features as positive and negative samples in the dual-branch contrastive learning (DBCL). DBCL increases the differences between positive and negative samples to avoid generating templated results, and enhances the robustness of the model. Finally, we conducted experiments to demonstrate the effectiveness of our model on two public radiology datasets, IU-Xray and MIMIC-CXR. Our model outperformed previous baseline methods on both datasets and achieved excellent evaluation scores.
Biomedical Named Entity Recognition Through Deep Reinforcement Learning

Biomedical Named Entity Recognition (BioNER) is a crucial task in extracting entities from biomedical literature. It plays a key role as the initial step in various biomedical information extraction tasks. Pre-training models have gained popularity in BioNER. However, these models require a large number of parameters, which poses a barrier for many researchers due to the hardware resource requirements. To address this challenge, we propose REIN-NER, a novel reinforcement learning-based approach for BioNER. REIN-NER employs a two-stage training mechanism: Firstly, the basic named recognition model (BaseNER) is pre-trained to capture the mapping knowledge from input words to the corresponding labels. Secondly, after initializing the agent model (AgentNER) with the BaseNER, the reinforcement training is carried out to further obtain the dependency knowledge between the output labels. Both BaseNER and AgentNER are built with lightweight Bi-LSTM networks, which significantly reduce parameter sizes compared to pre-training models. Remarkably, REIN-NER achieves superior performance with F-scores of 84.04% and 93.34% on the CDR-disease and CDR-chemical corpora, respectively, outperforming BERT-based models. Our work presents a pioneering exploration of reinforcement learning in biomedical named entity recognition and demonstrates its effectiveness through state-of-the-art results.
Enhancing Classification Tasks through Domain Adaptation Strategies

Domain adaptation (DA) is a technique that uses the knowledge from similar data sets to enhance the generalizability of a model, which proves to be effective in addressing the issue of limited training data. However, due to the complexity of medical data, most advances have occurred in the natural domain and not in the medical field. Furthermore, the majority of advancements are derived from conventional DA datasets, which may introduce result bias. This article presents a comprehensive new analysis of four widely used DA algorithms, tested on more realistic medical data sets to assess the potential of these DA techniques. The examination covers an evaluation of their model performance, discrepancies in data distribution, and the interpretability of the models. For example, the Deep subdomain adaptation network (DSAN) achieves a high level of accuracy on the COVID-19 dataset (89.9%) using Resnet34. Furthermore, the interpretability of the DSAN’s prediction using the skin cancer dataset is implausible. In conclusion, we offer perspectives on the outcomes obtained. Our codes are available at https://github.com/AIPMLab/Domain_Adaptation.
Auto-Augmentation Contrastive Learning for Wearable-based Human Activity Recognition

For low-semantic sensor signals from human activity recognition (HAR), contrastive learning (CL) is essential to implement novel applications or generic models without manual annotation, which is a high-performance self-supervised learning (SSL) method. However, CL relies heavily on data augmentation for pairwise comparisons. Especially for low semantic data in the HAR area, conducting good performance augmentation strategies in pretext tasks still rely on manual attempts lacking generalizability and flexibility. To reduce the augmentation burden, we propose an end-to-end auto-augmentation contrastive learning (AutoCL) method for wearable-based HAR. AutoCL is based on a Siamese network architecture that shares the parameters of the backbone and with a generator embedded to learn auto-augmentation. AutoCL trains the generator based on the representation in the latent space to overcome the disturbances caused by noise and redundant information in raw sensor data. The architecture empirical study indicates the effectiveness of this design. Furthermore, we propose a stop-gradient design and correlation reduction strategy in AutoCL to enhance encoder representation learning. Extensive experiments based on four wide-used HAR datasets demonstrate that the proposed AutoCL method significantly improves recognition accuracy compared with other SOTA methods.
A Closer Look at Weak Supervision’s Limitations in WSI Recurrence Score Prediction

Histological examination remains the gold standard for breast cancer diagnosis, prognosis assessment and treatment guidance. Commercial molecular signature test, ONCOTYPEDX <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> is routinely used for luminal breast cancers to predict the probabilities of response to chemotherapy and disease recurrence. We attempted to predict RS using digital pathology and Weakly Supervised (WS) attention-based models like CLAM (Clustering-constrained Attention Multiple Instance Learning) [1] and TransMIL (Transformer based Correlated Multiple Instance Learning) [2] on our in-house dataset. In tissue samples, the malignant component is haphazardly admixed with the nonmalignant component in variable proportions. This represents a challenge for the WS attention-based models to identify high-valued diagnostic/prognostic areas within whole slide images (WSIs). To address this, we propose an interactive approach with a human in the middle (supervised) by creating a user-friendly Graphical User Interface (GUI) that allows a pathologist to provide feedback to heatmaps generated by any WS attention-based model. We incorporate the feedback from the GUI as expected scores and penalize its difference with the attention scores in the successive training process (current scores). We observe an improvement in RS prediction after the pathologist’s feedback - a 5% rise in AUC and 4% in accuracy for CLAM and a 4.5% increase in AUC and 3% in accuracy for TransMIL. We analyze the generated heatmaps and notice an improvement in cosine similarity between the expected scores and attention scores before and after the feedback - 5% and 10% increase for CLAM and TransMIL, respectively. The implementation of the proposed approach and the dataset is available for download <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Our adaptive, interactive feedback system harmonizes attention scores with expert intuition and instills higher confidence in the system’s predictions. This study establishes a potent synergy between AI and pathologists.