Benchmarking Large Language Models in Evidence-Based Medicine.

Evidence-based medicine (EBM) represents a paradigm of providing patient care grounded in the most current and rigorously evaluated research. Recent advances in large language models (LLMs) offer a potential solution to transform EBM by automating labor-intensive tasks and thereby improving the efficiency of clinical decision-making. This study explores integrating LLMs into the key stages in EBM, evaluating their ability across evidence retrieval (PICO extraction, biomedical question answering), synthesis (summarizing randomized controlled trials), and dissemination (medical text simplification). We conducted a comparative analysis of seven LLMs, including both proprietary and open-source models, as well as those fine-tuned on medical corpora. Specifically, we benchmarked the performance of various LLMs on each EBM task under zero-shot settings as baselines, and employed prompting techniques, including in-context learning, chain-of-thought reasoning, and knowledge-guided prompting to enhance their capabilities. Our extensive experiments revealed the strengths of LLMs, such as remarkable understanding capabilities even in zero-shot settings, strong summarization skills, and effective knowledge transfer via prompting. Promoting strategies such as knowledge-guided prompting proved highly effective (e.g., improving the performance of GPT-4 by 13.10% over zero-shot in PICO extraction). However, the experiments also showed limitations, with LLM performance falling well below state-of-the-art baselines like PubMedBERT in handling named entity recognition tasks. Moreover, human evaluation revealed persisting challenges with factual inconsistencies and domain inaccuracies, underscoring the need for rigorous quality control before clinical application. This study provides insights into enhancing EBM using LLMs while highlighting critical areas for further research. The code is publicly available on Github.

NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks.

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to have high prognostic value in HCC, its application in clinical prognosis prediction requires specialized knowledge and complex calculations, which poses challenges for clinicians. It would be of interest to develop a model to help clinicians make full use of micronecrosis to assess patient survival. METHODS: To address these challenges, we propose a HCC prognosis prediction model that integrates pathological micronecrosis information through Graph Convolutional Neural Networks (GCN). This approach enables GCN to utilize micronecrosis, which has been shown to be highly correlated with prognosis, thereby significantly enhancing prognostic stratification quality. We developed our model using 3622 slides from 752 patients with primary HCC from the FAH-ZJUMS dataset and conducted internal and external validations on the FAH-ZJUMS and TCGA-LIHC datasets, respectively. RESULTS: Our method outperformed the baseline by 8.18% in internal validation and 9.02% in external validations. Overall, this paper presents a deep learning research paradigm that integrates HCC micronecrosis, enhancing both the accuracy and interpretability of prognostic predictions, with potential applicability to other pathological prognostic markers. CONCLUSIONS: This study proposes a composite GCN prognostic model that integrates information on HCC micronecrosis, collecting large dataset of HCC histopathological images. This approach could assist clinicians in analyzing HCC patient survival and precisely locating and visualizing necrotic tissues that affect prognosis. Following the research paradigm outlined in this paper, other prognostic biomarker integration models with GCN could be developed, significantly enhancing the predictive performance and interpretability of prognostic model.

Attention-enhanced multiscale feature fusion network for pancreas and tumor segmentation.

BACKGROUND: Accurate pancreas and pancreatic tumor segmentation from abdominal scans is crucial for diagnosing and treating pancreatic diseases. Automated and reliable segmentation algorithms are highly desirable in both clinical practice and research. PURPOSE: Segmenting the pancreas and tumors is challenging due to their low contrast, irregular morphologies, and variable anatomical locations. Additionally, the substantial difference in size between the pancreas and small tumors makes this task difficult. This paper proposes an attention-enhanced multiscale feature fusion network (AMFF-Net) to address these issues via 3D attention and multiscale context fusion methods. METHODS: First, to prevent missed segmentation of tumors, we design the residual depthwise attention modules (RDAMs) to extract global features by expanding receptive fields of shallow layers in the encoder. Second, hybrid transformer modules (HTMs) are proposed to model deep semantic features and suppress irrelevant regions while highlighting critical anatomical characteristics. Additionally, the multiscale feature fusion module (MFFM) fuses adjacent top and bottom scale semantic features to address the size imbalance issue. RESULTS: The proposed AMFF-Net was evaluated on the public MSD dataset, achieving 82.12% DSC for pancreas and 57.00% for tumors. It also demonstrated effective segmentation performance on the NIH and private datasets, outperforming previous State-Of-The-Art (SOTA) methods. Ablation studies verify the effectiveness of RDAMs, HTMs, and MFFM. CONCLUSIONS: We propose an effective deep learning network for pancreas and tumor segmentation from abdominal CT scans. The proposed modules can better leverage global dependencies and semantic information and achieve significantly higher accuracy than the previous SOTA methods.

A NOVEL OPSONIC EXTRACELLULAR CIRP INHIBITOR MOP3 ALLEVIATES GUT ISCHEMIA/REPERFUSION INJURY.

INTRODUCTION: Gut ischemia and reperfusion (I/R) injury promotes the release of damage-associated molecular patterns (DAMPs) such as extracellular cold-inducible RNA-binding protein (eCIRP). Gut I/R often leads to acute lung injury (ALI), a major contributor to mortality. Milk fat globule-epidermal growth factor-factor VIII-derived oligopeptide-3 (MOP3) is a novel peptide that attenuates sepsis by opsonizing eCIRP and facilitating its phagocytic clearance. We hypothesized that MOP3 reduces inflammation, mitigates gut and lung injury, and improves survival in gut I/R injury. METHODS: Phagocytosis of FITC-labeled eCIRP by intestinal epithelial cells was determined by confocal microscopy, and the cell supernatant was evaluated for cytokine expression by ELISA. Adult C57BL/6 mice underwent 60 min of gut ischemia via superior mesenteric artery occlusion followed by reperfusion. Mice were treated with MOP3 or vehicle via retro-orbital injection at the time of reperfusion. At 4 h post-I/R, blood, gut, and lungs were harvested for further assay. In additional mice, 36 h survival was assessed. Plasma levels of injury and inflammatory markers were measured with colorimetry and ELISA, respectively. Tissue mRNA expression was measured with qPCR. Myeloperoxidase (MPO), TUNEL, histologic injury, and ZO-1 immunohistochemistry assessments were performed. RESULTS: MOP3 significantly increased eCIRP phagocytosis by intestinal epithelial cells (p < 0.01) and decreased IL-6 release (p < 0.001). Gut I/R caused elevated plasma eCIRP levels. MOP3 treatment significantly reduced plasma levels of IL-1ß (p < 0.01), IL-6 (p < 0.05), and lactate dehydrogenase (p < 0.05) along with a significant decrease in gut (p < 0.05) and lung (p < 0.001) injury scores as well as gut cell death (p < 0.05). Moreover, MOP3 reduced pulmonary levels of chemokines and the granulocyte activation marker MPO after gut I/R. Mechanistically, ZO-1 expression in the gut was decreased following gut I/R injury, while MOP3 significantly reversed the decrease in ZO-1 mRNA expression (p < 0.001). Finally, mice treated with MOP3 exhibited a significant decrease in mortality (p < 0.05). CONCLUSIONS: Treatment with MOP3 effectively mitigates organ injury induced by gut I/R. This beneficial effect is attributed to the facilitation of eCIRP clearance, directing the potential of MOP3 as an innovative therapeutic approach for this critical and often fatal condition.

Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

BACKGROUND: The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. OBJECTIVE: This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. METHODS: The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. RESULTS: The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. CONCLUSIONS: The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

BMAL2 promotes eCIRP-induced macrophage endotoxin tolerance.

Background: The disruption of the circadian clock is associated with inflammatory and immunological disorders. BMAL2, a critical circadian protein, forms a dimer with CLOCK, activating transcription. Extracellular cold-inducible RNA-binding protein (eCIRP), released during sepsis, can induce macrophage endotoxin tolerance. We hypothesized that eCIRP induces BMAL2 expression and promotes macrophage endotoxin tolerance through triggering receptor expressed on myeloid cells-1 (TREM-1). Methods: C57BL/6 wild-type (WT) male mice were subjected to sepsis by cecal ligation and puncture (CLP). Serum levels of eCIRP 20 h post-CLP were assessed by ELISA. Peritoneal macrophages (PerM) were treated with recombinant mouse (rm) CIRP (eCIRP) at various doses for 24 h. The cells were then stimulated with LPS for 5 h. The levels of TNF-α and IL-6 in the culture supernatants were assessed by ELISA. PerM were treated with eCIRP for 24 h, and the expression of PD-L1, IL-10, STAT3, TREM-1 and circadian genes such as BMAL2, CRY1, and PER2 was assessed by qPCR. Effect of TREM-1 on eCIRP-induced PerM endotoxin tolerance and PD-L1, IL-10, and STAT3 expression was determined by qPCR using PerM from TREM-1-/- mice. Circadian gene expression profiles in eCIRP-treated macrophages were determined by PCR array and confirmed by qPCR. Induction of BMAL2 activation in bone marrow-derived macrophages was performed by transfection of BMAL2 CRISPR activation plasmid. The interaction of BMAL2 in the PD-L1 promoter was determined by computational modeling and confirmed by the BIAcore assay. Results: Serum levels of eCIRP were increased in septic mice compared to sham mice. Macrophages pre-treated with eCIRP exhibited reduced TNFα and IL-6 release upon LPS challenge, indicating macrophage endotoxin tolerance. Additionally, eCIRP increased the expression of PD-L1, IL-10, and STAT3, markers of immune tolerance. Interestingly, TREM-1 deficiency reversed eCIRP-induced macrophage endotoxin tolerance and significantly decreased PD-L1, IL-10, and STAT3 expression. PCR array screening of circadian clock genes in peritoneal macrophages treated with eCIRP revealed the elevated expression of BMAL2, CRY1, and PER2. In eCIRP-treated macrophages, TREM-1 deficiency prevented the upregulation of these circadian genes. In macrophages, inducible BMAL2 expression correlated with increased PD-L1 expression. In septic human patients, blood monocytes exhibited increased expression of BMAL2 and PD-L1 in comparison to healthy subjects. Computational modeling and BIAcore assay identified a putative binding region of BMAL2 in the PD-L1 promoter, suggesting BMAL2 positively regulates PD-L1 expression in macrophages. Conclusion: eCIRP upregulates BMAL2 expression via TREM-1, leading to macrophage endotoxin tolerance in sepsis. Targeting eCIRP to maintain circadian rhythm may correct endotoxin tolerance and enhance host resistance to bacterial infection.

pDenoiser: A Personalized Speech Enhancement Neural Network for Pre-hospital Emergency Medical Services.

Pre-hospital emergency medical service (EMS) tasks often come with complex and diverse noise interferences, posing challenges in implementing ASR-based medical technologies and hindering efficient and accurate telephonic communication. Among the different types of noise distortion, interfering speech is especially annoying. To address these issues, our aim is to develop a technology capable of extracting the intended speech content of the target physician from noisy and mixed audio during EMS tasks. In this work, we propose a monoaural personalized speech enhancement (PSE) method called pDenoiser, which is a real-time neural network that operates in the time domain. By leveraging the prior vocalization cues of emergency physicians, pDenoiser selectively enhances target speech components while suppressing noise and nontarget speech components, thereby improving speech quality and speech recognition accuracy under noisy conditions. We demonstrate the potential value of our approach through evaluations on both public general-domain test sets and our self-collected real-world EMS test sets. The experimental results are promising, as our model effectively promotes both speech quality and ASR performance under various conditions and outperforms related methods across multiple evaluation metrics. Our methodology will hopefully elevate EMS efficiency and fortify security against nontarget speech during EMS tasks.

Harnessing extracellular cold-inducible RNA binding protein by PS-OMe miR130: A promising shield against hemorrhage-induced lung injury.

INTRODUCTION: Hemorrhagic shock (HS) poses a life-threatening condition with the lungs being one of the most susceptible organs to its deleterious effects. Extracellular cold-inducible RNA binding protein has emerged as a pivotal mediator of inflammation, and its release has been observed as a case of HS-induced tissue injury. Previous studies unveiled a promising engineered microRNA, designated PS-OMe miR130, which inhibits extracellular cold-inducible RNA binding protein, thereby safeguarding vital organs. In this study, we hypothesized that PS-OMe miR130 serves as a protective shield against HS-induced lung injury by curtailing the overzealous inflammatory immune response. METHODS: Hemorrhagic shock was induced in male C57BL6 mice by withdrawing blood via a femoral artery cannula to a mean arterial pressure of 30 mm Hg for 90 minutes. The mice were resuscitated with twice the shed blood volume with Ringer's lactate solution. They were then treated intravenously with either phosphate-buffered saline (vehicle) or 62.5 nmol PS-OMe miR130. At 4 hours later, blood and lungs were harvested. RESULTS: Following PS-OMe miR130 treatment in HS mice, a substantial decrease was observed in serum injury markers including aspartate aminotransferase, alanine transaminase, lactate dehydrogenase, and blood urea nitrogen. Serum interleukin (IL)-6 exhibited a similar reduction. In lung tissues, PS-OMe miR130 led to a significant decrease in the messenger RNA expressions of pro-inflammatory cytokines (IL-6, IL-1ß, and tumor necrosis factor α), chemokines (keratinocyte-derived chemokine and macrophage inflammatory protein 2), and an endothelial injury marker, E-selectin. PS-OMe miR130 also produced substantial inhibition of lung myeloperoxidase activity and resulted in a marked reduction in lung injury as evidenced by histological evaluation. This was further confirmed by the observation that PS-OMe miR130 significantly reduced the presence of lymphocyte antigen 6 family member G-positive neutrophils and terminal deoxynucleotidyl transferase dUTP nick end labeling-positive apoptotic cells. CONCLUSION: PS-OMe miR130 emerges as a potent safeguard against HS-induced lung injury by effectively inhibiting pro-inflammation and injuries, offering a promising therapeutic strategy in such critical clinical condition.

Bi2Te3-Based Thermoelectric Modules for Efficient and Reliable Low-Grade Heat Recovery.

Bismuth-telluride-based alloy has long been considered as the most promising candidate for low-grade waste heat power generation. However, optimizing the thermoelectric performance of n-type Bi2Te3 is more challenging than that of p-type counterparts due to its greater sensitivity to texture, and thus limits the advancement of thermoelectric modules. Herein, the thermoelectric performance of n-type Bi2Te3 is enhanced by incorporating a small amount of CuGaTe2, resulting in a peak ZT of 1.25 and a distinguished average ZT of 1.02 (300-500 K). The decomposed Cu+ strengthens interlayer interaction, while Ga+ optimizes carrier concentration within an appropriate range. Simultaneously, the emerged numerous defects, such as small-angle grain boundaries, twin boundaries, and dislocations, significantly suppresses the lattice thermal conductivity. Based on the size optimization by finite element modelling, the constructed thermoelectric module yields a high conversion efficiency of 6.9% and output power density of 0.31 W cm-2 under a temperature gradient of 200 K. Even more crucially, the efficiency and output power little loss after subjecting the module to 40 thermal cycles lasting for 6 days. This study demonstrates the efficient and reliable Bi2Te3-based thermoelectric modules for broad applications in low-grade heat harvest.

GCN-Based Risk Prediction for Necrosis Slide of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world which ranks fourth in cancer deaths. Primary pathological necrosis is an effective prognostic indicator for hepatocellular carcinoma. We propose a GCN-based approach that mimics the pathologist's perspective for global assessment of necrosis tissue distribution to analyze patient survival. Specifically, we introduced a graph convolutional neural network to construct a spatial map with necrotic tissue and tumor tissue as graph nodes, aiming to mine the contextual information between necrotic tissue in pathological sections. We used 1381 slides from 303 patients from the First Affiliated Hospital of Zhejiang University School to train the model and used TCGA-LIHC for external validation. The C-index of our method outperforms the baseline by about 4.45%, which proves that the information about the spatial distribution of necrosis learned by GCN is meaningful for guiding patient prognosis.

Gestational diabetes and risk of perinatal depression in low- and middle-income countries: a meta-analysis.

Background: The relationship between gestational diabetes (GDM) and the risk of depression has been thoroughly investigated in high-income countries on their financial basis, while it is largely unexplored in low- and middle- income countries. This meta-analysis aims to assess how GDM influences the risk of perinatal depression by searching multiple electronic databases for studies measuring the odds ratios between them in low- and middle-income countries. Methods: Two independent reviewers searched multiple electronic databases for studies that investigated GDM and perinatal mental disorders on August 31, 2023. Pooled odds ratios (ORs) and confidence intervals (CIs) were calculated using the random effect model. Subgroup analyses were further conducted based on the type of study design and country income level. Results: In total, 16 observational studies met the inclusion criteria. Only the number of studies on depression (n=10) satisfied the conditions to conduct a meta-analysis, showing the relationship between mental illness and GDM has been overlooked in low- and middle-income countries. Evidence shows an elevated risk of perinatal depression in women with GDM (pooled OR 1.92; 95% CI 1.24, 2.97; 10 studies). The increased risk of perinatal depression in patients with GDM was not significantly different between cross-sectional and prospective design. Country income level is a significant factor that adversely influences the risk of perinatal depression in GDM patients. Conclusion: Our findings suggested that women with GDM are vulnerable to perinatal depressive symptoms, and a deeper understanding of potential risk factors and mechanisms may help inform strategies aimed at prevention of exposure to these complications during pregnancy.

A NOVEL OLIGONUCLEOTIDE MRNA MIMIC ATTENUATES HEMORRHAGE-INDUCED ACUTE LUNG INJURY.

ABSTRACT: Hemorrhagic shock (HS) is accompanied by a pronounced activation of the inflammatory response in which acute lung injury (ALI) is one of the most frequent consequences. Among the pivotal orchestrators of this inflammatory cascade, extracellular cold-inducible RNA-binding protein (eCIRP) emerges as a noteworthy focal point, rendering it as a promising target for the management of inflammation and tissue injury. Recently, we have reported that oligonucleotide poly(A) mRNA mimic termed A 12 selectively binds to the RNA binding region of eCIRP and inhibits eCIRP binding to its receptor TLR4. Furthermore, in vivo administration of eCIRP induces lung injury in healthy mice and that mouse deficient in CIRP showed protection from inflammation-associated lung injury. We hypothesize that A 12 inhibits systemic inflammation and ALI in HS. To test the impacts of A 12 on systemic and lung inflammation, extent of inflammatory cellular infiltration and resultant lung damage were evaluated in a mouse model of HS. Male mice were subjected to controlled hemorrhage with a mean arterial pressure of 30 mm Hg for 90 min and then resuscitated with Ringer's lactate solution containing phosphate-buffered saline (vehicle) or A 12 at a dose of 4 nmol/g body weight (treatment). The infusion volume was twice that of the shed blood. At 4 h after resuscitation, mice were euthanized, and blood and lung tissues were harvested. Blood and tissue markers of inflammation and injury were evaluated. Serum markers of injury (lactate dehydrogenase, alanine transaminase, and blood urea nitrogen) and inflammation (TNF-α, IL-6) were increased after HS and A 12 treatment significantly decreased their levels. A 12 treatment also decreased lung levels of TNF-α, MIP-2, and KC mRNA expressions. Lung histological injury score, neutrophil infiltration (Ly6G staining and myeloperoxidase activity), and lung apoptosis were significantly attenuated after A 12 treatment. Our study suggests that the capacity of A 12 in attenuating HS-induced ALI and may provide novel perspectives in developing efficacious pharmaceutics for improving hemorrhage prognosis.

A Noise-Induced Hearing Loss Prediction Model Based on Asymmetric Convolution for Workers Exposed to Complex Industrial Noise.

OBJECTIVES: Current approaches for evaluating noise-induced hearing loss (NIHL), such as the International Standards Organization 1999 (ISO) 1999 prediction model, rely mainly on noise energy and exposure time, thus ignoring the intricate time-frequency characteristics of noise, which also play an important role in NIHL evaluation. In this study, an innovative NIHL prediction model based on temporal and spectral feature extraction using an asymmetric convolution algorithm is proposed. DESIGN: Personal data and individual occupational noise records from 2214 workers across 23 factories in Zhejiang Province, China, were used in this study. In addition to traditional metrics like noise energy and exposure duration, the importance of time-frequency features in NIHL assessment was also emphasized. To capture these features, operations such as random sampling, windowing, short-time Fourier transform, and splicing were performed to create time-frequency spectrograms from noise recordings. Two asymmetric convolution kernels then were used to extract these critical features. These features, combined with personal information (e.g., age, length of service) in various configurations, were used as model inputs. The optimal network structure was selected based on the area under the curve (AUC) from 10-fold cross-validation, alongside the Wilcoxon signed ranks test. The proposed model was compared with the support vector machine (SVM) and ISO 1999 models, and the superiority of the new approach was verified by ablation experiments. RESULTS: The proposed model had an AUC of 0.7768 ± 0.0223 (mean ± SD), outperforming both the SVM model (AUC: 0.7504 ± 0.0273) and the ISO 1999 model (AUC: 0.5094 ± 0.0071). Wilcoxon signed ranks tests confirmed the significant improvement of the proposed model ( p = 0.0025 compared with ISO 1999, and p = 0.00142 compared with SVM). CONCLUSIONS: This study introduced a new NIHL prediction method that provides deeper insights into industrial noise exposure data. The results demonstrated the superior performance of the new model over ISO 1999 and SVM models. By combining time-frequency features and personal information, the proposed approach bridged the gap between conventional noise assessment and machine learning-based methods, effectively improving the ability to protect workers' hearing.

A Personal Healthcare Knowledge Graph Framework for Diagnosis of Pelvic Masses Diseases.

A Personal Health Knowledge Graph (PHKG) facilitates the efficient integration of potential diagnostic clues from patients' electronic health records with medical knowledge, establishing diagnostic reasoning paths and ensuring accurate, individually interpretable results in the diagnosis of pelvic masses.

Design of Household Cognitive Level Assessment System Based on Grip Force and Finger Force Distribution.

In this paper we designed a household cognitive level assessment system based on finger force distribution. The system evaluates the user's current cognitive level according to the degree of matching between the characteristics of user's grip force and finger force distribution data and the characteristics in the database. The system based on finger force distribution will greatly reduce the space and economic cost of household cognitive level assessment.

Design and Application of Multi-Center Clinical Research Platform for Phenotyping of Voriconazole Hepatotoxicity.

We introduce a phenotyping pipeline for voriconazole hepatotoxicity based on a multi-center clinical research platform. Using the platform's queue construction, feature generation, and feature screening functions, 52 features were obtained for model training. The prediction model of voriconazole hepatotoxicity was obtained by using the model training and evaluation functions of the platform. Important risk factors and protection factors of the model were listed.

Does a Digital Health Application Could Be the Supplement to the Influenza Surveillance System?

Epidemics of seasonal influenza is a major public health concern in china. Historical percentage of influenza-like illness (ILI%) from CDC and health enquiry data from a health-related application were collected, when combining the real-time ILI-related search queries with one-week ago's ILI%, it was able to predict the trend of ILI correctly and timely. Digital health application is potentializing a supplement to the traditional influenza surveillance systems in China.

A Post-Marketing Drug Evaluation Framework Based on Real-World Electronic Health Records Data.

Real-world data (RWD) could be a new way to evaluate the safety and efficacy of post-marketing drugs, while there is no common method for how to use RWD for drug evaluation. In this paper, we present a framework for real-world drug evaluation based on electronic medical record (EHR) data. We designed a data model customized for post-marketing drug evaluation and a unified post-marketing drug evaluation pipeline. The proposed framework can be applied to drug evaluations with different study paradigms for different purposes by flexible use of the proposed data model and pipeline. A prototype system has been developed according to the framework. Real-world EHRs in a tertiary hospital in China between 2010 and 2020 were converted to the proposed data model, and as a test case, we conducted a research on the risk of allergic reactions to cefodizime and ceftriaxone using the prototype system.

Temporal Phenotyping for End-Stage Renal Disease Using Longitudinal Electronic Health Records.

End Stage Renal Disease (ESRD) is a highly heterogeneous disease with significant differences in prevalence, mortality, complications, and treatment modalities across age, sex, race, and ethnicity. An improved knowledge of disease characteristics results from the use of a data-driven phenotypic classification strategy to identify patients of different subtypes and expose the clinical traits of different subtypes. This study used topic models and process mining techniques to perform subtyping of ESRD patients on hemodialysis based on real-world longitudinal electronic health record data. The mined subtypes are interpretable and clinically significant, and they can reflect differences in the progression of the disease state and clinical outcomes.

Construction of a Prediction Model for Voriconazole-Induced Hepatotoxicity Based on Mixed-Effects Random Forest.

Voriconazole is a second-generation triazole antifungal agent with strong antifungal activity against a variety of clinically significant pathogens. Controlling blood concentrations within guideline limits through blood concentration monitoring can reduce the probability of hepatotoxicity in patients with voriconazole. However, statistical analysis based on real-world data found that there were still several patients who had blood concentration monitoring developed voriconazole induced hepatotoxicity. Therefore, it has important clinical significance to predict whether hepatotoxicity will occur in patients who meet the guidelines for voriconazole plasma concentration requirements. In this study, based on real-world data, the mixed-effects random forest was used to analyze the electronic medical record data of patients who met the guidelines for voriconazole blood concentration requirements during hospitalization, and a predictive model was constructed to predict whether patients would develop hepatotoxicity within 30 days after using voriconazole.