Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health records.

Type 2 diabetes mellitus (T2DM) is a prevalent health challenge faced by countries worldwide. In this study, we propose a novel large language multimodal models (LLMMs) framework incorporating multimodal data from clinical notes and laboratory results for diabetes risk prediction. We collected five years of electronic health records (EHRs) dating from 2017 to 2021 from a Taiwan hospital database. This dataset included 1,420,596 clinical notes, 387,392 laboratory results, and more than 1505 laboratory test items. Our method combined a text embedding encoder and multi-head attention layer to learn laboratory values, and utilized a deep neural network (DNN) module to merge blood features with chronic disease semantics into a latent space. In our experiments, we observed that integrating clinical notes with predictions based on textual laboratory values significantly enhanced the predictive capability of the unimodal model in the early detection of T2DM. Moreover, we achieved an area greater than 0.70 under the receiver operating characteristic curve (AUC) for new-onset T2DM prediction, demonstrating the effectiveness of leveraging textual laboratory data for training and inference in LLMs and improving the accuracy of new-onset diabetes prediction.

al-BERT: a semi-supervised denoising technique for disease prediction.

BACKGROUND: Medical records are a valuable source for understanding patient health conditions. Doctors often use these records to assess health without solely depending on time-consuming and complex examinations. However, these records may not always be directly relevant to a patient's current health issue. For instance, information about common colds may not be relevant to a more specific health condition. While experienced doctors can effectively navigate through unnecessary details in medical records, this excess information presents a challenge for machine learning models in predicting diseases electronically. To address this, we have developed 'al-BERT', a new disease prediction model that leverages the BERT framework. This model is designed to identify crucial information from medical records and use it to predict diseases. 'al-BERT' operates on the principle that the structure of sentences in diagnostic records is similar to regular linguistic patterns. However, just as stuttering in speech can introduce 'noise' or irrelevant information, similar issues can arise in written records, complicating model training. To overcome this, 'al-BERT' incorporates a semi-supervised layer that filters out irrelevant data from patient visitation records. This process aims to refine the data, resulting in more reliable indicators for disease correlations and enhancing the model's predictive accuracy and utility in medical diagnostics. METHOD: To discern noise diseases within patient records, especially those resembling influenza-like illnesses, our approach employs a customized semi-supervised learning algorithm equipped with a focused attention mechanism. This mechanism is specifically calibrated to enhance the model's sensitivity to chronic conditions while concurrently distilling salient features from patient records, thereby augmenting the predictive accuracy and utility of the model in clinical settings. We evaluate the performance of al-BERT using real-world health insurance data provided by Taiwan's National Health Insurance. RESULT: In our study, we evaluated our model against two others: one based on BERT that uses complete disease records, and another variant that includes extra filtering techniques. Our findings show that models incorporating filtering mechanisms typically perform better than those using the entire, unfiltered dataset. Our approach resulted in improved outcomes across several key measures: AUC-ROC (an indicator of a model's ability to distinguish between classes), precision (the accuracy of positive predictions), recall (the model's ability to find all relevant cases), and overall accuracy. Most notably, our model showed a 15% improvement in recall compared to the current best-performing method in the field of disease prediction. CONCLUSION: The conducted ablation study affirms the advantages of our attention mechanism and underscores the crucial role of the selection module within al-BERT.

Notch2-induced COX-2 expression enhancing gastric cancer progression.

Gastric carcinoma is one of the most common and mortal types of malignancy worldwide. To date, the mechanisms controlling its aggressiveness are not yet fully understood. Notch signal pathway can function as either an oncogene or a tumor suppressor in tumorigenesis. Four members (Notch1-4) of Notch receptors were found in mammals and each exhibits distinct roles in tumor progression. Previous study showed that the activated Notch1 receptor promoted gastric cancer progression through cyclooxygenase-2 (COX-2). This study addressed whether Notch2 signal pathway is also involved in gastric cancer progression. Constitutive expression of Notch2 intracellular domain (N2IC), the activated form of Notch2 receptor, promoted both cell proliferation and xenografted tumor growth of human stomach adenocarcinoma SC-M1 cells. The colony formation, migration, invasion, and wound-healing abilities of SC-M1 cells were enhanced by N2IC expression, whereas these abilities were suppressed by Notch2 knockdown. Similarly, Notch2 knockdown inhibited cancer progressions of AGS and AZ521 gastric cancer cells. Expression of N2IC also caused epithelial-mesenchymal transition in SC-M1 cells. Furthermore, N2IC bound to COX-2 promoter and induced COX-2 expression through a CBF1-dependent manner in SC-M1 cells. The ability of N2IC to enhance tumor progression in SC-M1 cells was suppressed by knockdown of COX-2 or treatment with NS-398, a COX-2 inhibitor. Moreover, the suppression of tumor progression by Notch2 knockdown in SC-M1 cells was reversed by exogenous COX-2 or its major enzymatic product PGE(2) . Taken together, this study is the first to demonstrate that the Notch2-COX-2 signaling axis plays an important role in controlling gastric cancer progression.