Prediction of complications in diabetes mellitus using machine learning models with transplanted topic model features.

Purpose: This study aims to predict the progression of Diabetes Mellitus (DM) from the clinical notes through machine learning based on latent Dirichlet allocation (LDA) topic modeling. Particularly, 174,427 clinical notes of DM patients were collected from the electronic medical record (EMR) system of the Seoul National University Hospital outpatient clinic. Method: We developed a model to predict the development of DM complications. Topics developed by the topic model were exploited as the key feature of our machine-learning model. The proposed model generalized a correlation between topic structures and complications. Results: The model provided acceptable predictive performance for all four types of complications (diabetic retinopathy, diabetic nephropathy, nonalcoholic fatty liver disease, and cerebrovascular accident). Upon employing extreme gradient boosting (XGBoost), we obtained the F1 scores of the predictions for each complication type as 0.844, 0.921, 0.831, and 0.762. Conclusion: This study shows that a machine learning project based on topic modeling can effectively predict the progress of a disease. Furthermore, a unique way of topic model transplanting, which matches the dimension of the topic structures of the two data sets, is presented. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-023-00322-7.

Machine learning models for predicting early hemorrhage progression in traumatic brain injury.

This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predict the risk of ICH progression using initial CT scans and identify clinical factors associated with this progression. A retrospective analysis of TBI patients between January 2010 and December 2021 was performed, focusing on initial CT evaluations and demographic, comorbid, and medical history data. ICH was categorized into intraparenchymal hemorrhage (IPH), petechial hemorrhage (PH), and subarachnoid hemorrhage (SAH). Within our study cohort, we identified a 22.2% progression rate of ICH among 650 TBI patients. The Random Forest algorithm identified variables such as petechial hemorrhage (PH) and countercoup injury as significant predictors of ICH progression. The XGBoost algorithm, incorporating key variables identified through SHAP values, demonstrated robust performance, achieving an AUC of 0.9. Additionally, an individual risk assessment diagram, utilizing significant SHAP values, visually represented the impact of each variable on the risk of ICH progression, providing personalized risk profiles. This approach, highlighted by an AUC of 0.913, underscores the model's precision in predicting ICH progression, marking a significant step towards enhancing TBI patient management through early identification of ICH progression risks.

Automated Information Extraction from Thyroid Operation Narrative: A Comparative Study of GPT-4 and Fine-tuned KoELECTRA.

In the rapidly evolving field of healthcare, the integration of artificial intelligence (AI) has become a pivotal component in the automation of clinical workflows, ushering in a new era of efficiency and accuracy. This study focuses on the transformative capabilities of the fine-tuned KoELECTRA model in comparison to the GPT-4 model, aiming to facilitate automated information extraction from thyroid operation narratives. The current research landscape is dominated by traditional methods heavily reliant on regular expressions, which often face challenges in processing free-style text formats containing critical details of operation records, including frozen biopsy reports. Addressing this, the study leverages advanced natural language processing (NLP) techniques to foster a paradigm shift towards more sophisticated data processing systems. Through this comparative study, we aspire to unveil a more streamlined, precise, and efficient approach to document processing in the healthcare domain, potentially revolutionizing the way medical data is handled and analyzed.

Dynamic insights into infection risk over time in two-stage implant-based breast reconstruction: a retrospective cohort study.

BACKGROUND: Infections following postmastectomy implant-based breast reconstruction (IBR) can compromise surgical outcomes and lead to significant morbidity. This study aimed to discern the timing of infections in two-stage IBR and associated risk factors. METHOD: A review of electronic health records was conducted on 1096 breasts in 1058 patients undergoing two-stage IBR at Seoul National University Hospital (2015-2020). Infections following the first-stage tissue expander (TE) insertion and second-stage TE exchange were analyzed separately, considering associated risk factors. RESULTS: Over a median follow-up of 53.5 months, infections occurred in 2.9% (32/1096) after the first stage and 4.1% (44/1070) after the second stage. Infections following the first-stage procedure exhibited a bimodal distribution across time, while those after the second-stage procedure showed a unimodal pattern. When analyzing risk factors for infection after the first-stage procedure, axillary lymph node dissection (ALND) was associated with early (≤7 weeks) infection, while both ALND and obesity were independent predictors of late (>7 weeks) infection. For infections following the second-stage procedure, obesity, postmastectomy radiotherapy, a history of expander infection, ALND, and the use of textured implants were identified as independent risk factors. Postmastectomy radiotherapy was related to non-salvaged outcomes after infection following both stages. CONCLUSION: Infections following first and second-stage IBR exhibit distinct timelines reflecting different pathophysiology. Understanding these timelines and associated risk factors will inform patient selection for IBR and aid in tailored postoperative surveillance planning. These findings contribute to refining patient suitability for IBR and optimizing personalized postoperative care strategies.

Guidelines for Manufacturing and Application of Organoids: Lung.

The objective of standard guideline for utilization of human lung organoids is to provide the basic guidelines required for the manufacture, culture, and quality control of the lung organoids for use in non-clinical efficacy and inhalation toxicity assessments of the respiratory system. As a first step towards the utilization of human lung organoids, the current guideline provides basic, minimal standards that can promote development of alternative testing methods, and can be referenced not only for research, clinical, or commercial uses, but also by experts and researchers at regulatory institutions when assessing safety and efficacy.

Quantitative and qualitative mutational impact of ionizing radiation on normal cells.

The comprehensive genomic impact of ionizing radiation (IR), a carcinogen, on healthy somatic cells remains unclear. Using large-scale whole-genome sequencing (WGS) of clones expanded from irradiated murine and human single cells, we revealed that IR induces a characteristic spectrum of short insertions or deletions (indels) and structural variations (SVs), including balanced inversions, translocations, composite SVs (deletion-insertion, deletion-inversion, and deletion-translocation composites), and complex genomic rearrangements (CGRs), including chromoplexy, chromothripsis, and SV by breakage-fusion-bridge cycles. Our findings suggest that 1 Gy IR exposure causes an average of 2.33 mutational events per Gb genome, comprising 2.15 indels, 0.17 SVs, and 0.01 CGRs, despite a high level of inter-cellular stochasticity. The mutational burden was dependent on total irradiation dose, regardless of dose rate or cell type. The findings were further validated in IR-induced secondary cancers and single cells without clonalization. Overall, our study highlights a comprehensive and clear picture of IR effects on normal mammalian genomes.