BMC Medical Informatics and Decision Making最新文献

Background: In clinical practice, the incidence of hypofibrinogenemia (HF) after tigecycline (TGC) treatment significantly exceeds the probability claimed by drug manufacturers.

Objective: We aimed to identify the risk factors for TGC-associated HF and develop prediction and survival models for TGC-associated HF and the timing of TGC-associated HF.

Methods: This single-center retrospective cohort study included 222 patients who were prescribed TGC. First, we used binary logistic regression to screen the independent factors influencing TGC-associated HF, which were used as predictors to train the extreme gradient boosting (XGBoost) model. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA) were used to evaluate the performance of the model in the verification cohort. Subsequently, we conducted survival analysis using the random survival forest (RSF) algorithm. A consistency index (C-index) was used to evaluate the accuracy of the RSF model in the verification cohort.

Results: Binary logistic regression identified nine independent factors influencing TGC-associated HF, and the XGBoost model was constructed using these nine predictors. The ROC and calibration curves showed that the model had good discrimination (areas under the ROC curves (AUC) = 0.792 [95% confidence interval (CI), 0.668-0.915]) and calibration ability. In addition, DCA and CICA demonstrated good clinical practicability of this model. Notably, the RSF model showed good accuracy (C-index = 0.746 [95%CI, 0.652-0.820]) in the verification cohort. Stratifying patients treated with TGC based on the RSF model revealed a statistically significant difference in the mean survival time between the low- and high-risk groups.

Conclusions: The XGBoost model effectively predicts the risk of TGC-associated HF, whereas the RSF model has advantages in risk stratification. These two models have significant clinical practical value, with the potential to reduce the risk of TGC therapy.

Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers' default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets.

Background: Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent, and deadly type of liver cancer. With the advent of deep learning techniques, significant advancements have been made in simplifying and optimizing the feature selection process.

Objective: Our scoping review presents an overview of the various deep learning models and algorithms utilized to address feature selection for HCC. The paper highlights the strengths and limitations of each approach, along with their potential applications in clinical practice. Additionally, it discusses the benefits of using deep learning to identify relevant features and their impact on the accuracy and efficiency of diagnosis, prognosis, and treatment of HCC.

Design: The review encompasses a comprehensive analysis of the research conducted in the past few years, focusing on the methodologies, datasets, and evaluation metrics adopted by different studies. The paper aims to identify the key trends and advancements in the field, shedding light on the promising areas for future research and development.

Results: The findings of this review indicate that deep learning techniques have shown promising results in simplifying feature selection for HCC. By leveraging large-scale datasets and advanced neural network architectures, these methods have demonstrated improved accuracy and robustness in identifying predictive features.

Conclusions: We analyze published studies to reveal the state-of-the-art HCC prediction and showcase how deep learning can boost accuracy and decrease false positives. But we also acknowledge the challenges that remain in translating this potential into clinical reality.

Aims: The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies.

Methods: The study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system's computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the presence or absence of pCR. Secondly, we fine-tuned the Generative Pre-trained Transformer-2 (GPT-2) model by attaching a simple feed-forward neural network (FFNN) layer to improve the detection performance of pCR from pathology reports.

Results: In a cohort of 351 female breast cancer patients who underwent neoadjuvant chemotherapy (NAC) and subsequent surgery between 2010 and 2017 in Calgary, the optimized method displayed a sensitivity of 95.3% (95%CI: 84.0-100.0%), a positive predictive value of 90.9% (95%CI: 76.5-100.0%), and an F1 score of 93.0% (95%CI: 83.7-100.0%). The results, achieved through diverse LLM integration, surpassed traditional machine learning models, underscoring the potential of LLMs in clinical pathology information extraction.

Conclusions: The study successfully demonstrates the efficacy of LLMs in interpreting and processing digital pathology data, particularly for determining pCR in breast cancer patients post-NAC. The superior performance of LLM-based pipelines over traditional models highlights their significant potential in extracting and analyzing key clinical data from narrative reports. While promising, these findings highlight the need for future external validation to confirm the reliability and broader applicability of these methods.

Background: Wearable sensors have revolutionized cardiac health monitoring, with Seismocardiography (SCG) at the forefront due to its non-invasive nature. However, the substantial motion artefacts have hindered the translation of SCG-based medical applications, primarily induced by walking. In contrast, our innovative technique, Adaptive Bidirectional Filtering (ABF), surpasses these challenges by refining SCG signals more effectively than any motion-induced noise. ABF leverages a noise-cancellation algorithm, operating on the benefits of the Redundant Multi-Scale Wavelet Decomposition (RMWD) and the bidirectional filtering framework, to achieve optimal signal quality.

Methodology: The ABF technique is a two-stage process that diminishes the artefacts emanating from motion. The first step by RMWD is the identification of the heart-associated signals and the isolating samples with those related frequencies. Subsequently, the adaptive bidirectional filter operates in two dimensions: it uses Time-Frequency masking that eliminates temporal noise while engaging in non-negative matrix Decomposition to ensure spatial correlation and dorsoventral vibration reduction jointly. The main component that is altered from the other filters is the recursive structure that changes to the motion-adapted filter, which uses vertical axis accelerometer data to differentiate better between accurate SCG signals and motion artefacts.

Outcome: Our empirical tests demonstrate exceptional signal improvement with the application of our ABF approach. The accuracy in heart rate estimation reached an impressive r-squared value of 0.95 at - 20 dB SNR, significantly outperforming the baseline value, which ranged from 0.1 to 0.85. The effectiveness of the motion-artifact-reduction methodology is also notable at an SNR of - 22 dB. Consequently, ECG inputs are not required. This method can be seamlessly integrated into noisy environments, enhancing ECG filtering, automatic beat detection, and rhythm interpretation processes, even in highly variable conditions. The ABF method effectively filters out up to 97% of motion-related noise components within the SCG signal from implantable devices. This advancement is poised to become an integral part of routine patient monitoring.

Polycystic Ovarian Disease or Polycystic Ovary Syndrome (PCOS) is becoming increasingly communal among women, owing to poor lifestyle choices. According to the research conducted by National Institutes of Health, it has been observe that PCOS, an endocrine condition common in women of childbearing age, has become a significant contributing factor to infertility. Ovarian abnormalities brought on by PCOS carry a high risk of miscarriage, infertility, cardiac problems, diabetes, uterine cancer, etc. Ovarian cysts, obesity, menstrual irregularities, elevated amounts of male hormones, acne vulgaris, hair loss, and hirsutism are some of the symptoms of PCOS. It is not easy to determine PCOS because of its different combinations of symptoms in different women and various criteria needed for diagnosis. Taking biochemical tests and ovary scanning is a time-consuming process and the financial expenses have become a hardship to the patients. Thus, early prognosis of PCOS is crucial to avoid infertility. The goal of the proposed work is to analyse PCOS symptoms based on clinical data for early diagnosis and to classify into PCOS affected or not. To achieve this objective, clinical features dataset and ultrasound imaging dataset from Kaggle is utilized. Initially 541 instances of 45 clinical features such as testosterone, hirsutism, family history, BMI, fast food, menstrual disorder, risk etc. are considered and correlation-based feature extraction method is applied to this dataset which results in 17 features. The extracted features are applied to various machine learning algorithms such as Logistic Regression, Naïve Bayes and Support Vector Machine. The performance of each method is evaluated based on accuracy, precision, recall, F1-score and the result shows that among three models, Support Vector Machine model achieved high accuracy of 94.44%. In addition to this, 3856 ultrasound images are analysed by CNN based deep learning algorithm and VGG16 transfer learning algorithm. The performance of these models is evaluated using training accuracy, loss and validation accuracy, loss and the result depicts that VGG16 outperforms than CNN model with validation accuracy of 98.29%.

Background: Medical dispute is a global public health issue, which has been garnering increasing attention. In this study, we used machine learning (ML) method to establish a dispute prediction model and explored the clinical-application efficiency of this model in effectively reducing the occurrence of medical disputes.

Methods: Retrospective study of All disputes filed by Gansu Medical Mediation Committee from 2019 to 2021 and patients with the same hospital level as that of the dispute group and hospitalization year were randomly selected as the control group in 1:1 ratio. SPSS software was used for univariate feature selection of the 14 factors that may cause disputes, and factors with statistical differences were selected. The data were divided into training and test sets in a 7:3 ratio. Six ML models were selected, and Python was used to establish a dispute prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, precision, average precision (AP), and F1 score were used to characterize the fitting and accuracy of the models, while decision curve analysis (DCA) was used to evaluate their clinical utility.

Results: A total of 1189 patients in the dispute and control groups were extracted. Following 11 influencing factors were selected: the inpatient department, doctor title, patient age, patient gender, patient occupation, payment method, hospitalization days, hospitalization times, discharge method, blood transfusion volume, and hospitalization espenses. Compared to other models, the AUC (0.945, 95% CI 0.913-0.981), Sensitivity (0.887), Accuracy (0.887), AP (0.834), and F1 score (0.880) of the random forest model were higher than those of other models, while the DCA curve indicated its high clinical benefits.

Conclusions: Inpatient department, hospitalization expenses, and discharge type are the primary influencing factors of dispute. Random forest exhibited high dispute prediction and clinical-application value and is expected to be promoted for offline dispute prediction.

Background: Fibroids are non-cancerous uterine growths that can cause symptoms impacting quality of life. The breadth of treatment options allows for patient-centered preference. While conversation aids are known to facilitate shared decision making, the implementation of these aids for uterine fibroids treatments is limited. We aimed to develop two end-user-acceptable uterine fibroids conversation aids for an implementation project. Our second aim was to outline the adaptations that were made to the conversation aids as implementation occurred.

Methods: We used a multi-phase user-centered participatory approach to develop a text-based and picture-enhanced conversation aid for uterine fibroids. We conducted a focus group with project stakeholders and user-testing interviews with eligible individuals with symptomatic uterine fibroids. We analyzed the results of the user-testing interviews using Morville's Honeycomb framework. Spanish translations of the conversation aids occurred in parallel with the English iterations. We documented the continuous adaptations of the conversation aids that occurred during the project using an expanded framework for reporting adaptations and modifications to evidence-based interventions (FRAME).

Results: The first iteration of the conversation aids was developed in December 2018. Focus group participants (n = 6) appreciated the brevity of the tools and suggested changes to the bar graphs and illustrations used in the picture-enhanced version. User-testing with interview participants (n = 9) found that both conversation aids were satisfactory, with minor changes suggested. However, during implementation, significant changes were suggested by patients, other stakeholders, and participating clinicians when they reviewed the content. The most significant changes required the addition or deletion of information about treatment options as newer research was published or as novel interventions were introduced into clinical practice.

Conclusions: This multi-year project revealed the necessity of continuously adapting the uterine fibroids conversation aids so they remain acceptable in an implementation and sustainability context. Therefore, it is important to seek regular user feedback and plan for the need to undertake updates and revisions to conversation aids if they are going to be acceptable for clinical use.

Background: Patients undergo regular clinical follow-up after laminoplasty for cervical myelopathy. However, those whose symptoms significantly improve and remain stable do not need to conform to a regular follow-up schedule. Based on the 1-year postoperative outcomes, we aimed to use a machine-learning (ML) algorithm to predict 2-year postoperative outcomes.

Methods: We enrolled 80 patients who underwent cervical laminoplasty for cervical myelopathy. The patients' Japanese Orthopedic Association (JOA) scores (range: 0-17) were analyzed at the 1-, 3-, 6-, and 12-month postoperative timepoints to evaluate their ability to predict the 2-year postoperative outcomes. The patient acceptable symptom state (PASS) was defined as a JOA score ≥ 14.25 at 24 months postoperatively and, based on clinical outcomes recorded up to the 1-year postoperative timepoint, eight ML algorithms were developed to predict PASS status at the 24-month postoperative timepoint. The performance of each of these algorithms was evaluated, and its generalizability was assessed using a prospective internal test set.

Results: The long short-term memory (LSTM)-based algorithm demonstrated the best performance (area under the receiver operating characteristic curve, 0.90 ± 0.13).

Conclusions: The LSTM-based algorithm accurately predicted which group was likely to achieve PASS at the 24-month postoperative timepoint. Although this study included a small number of patients with limited available clinical data, the concept of using past outcomes to predict further outcomes presented herein may provide insights for optimizing clinical schedules and efficient medical resource utilization.

Trial registration: This study was registered as a clinical trial (Clinical Trial No. NCT02487901), and the study protocol was approved by the Seoul National University Hospital Institutional Review Board (IRB No. 1505-037-670).

Background: Electronic prescribing (e-prescribing) is an essential technology in the modern health system. This technology has made many changes in the prescription process, which have advantages and disadvantages and have created opportunities for transforming the health system. This study aimed to investigate the strengths, weaknesses, opportunities, and threats of the e-prescribing system implemented in Iran from the physician's viewpoint.

Methods: This phenomenological qualitative study was conducted in 2022. The participants were 15 Iranian specialist physicians working at Urmia University of Medical Sciences, selected purposively and deliberately. Data was collected through in-depth semi-structured interviews using an interview guide comprising 16 questions. Interviews were conducted until data saturation was reached. The audio data was transcribed into text and analyzed using the thematic analysis. To ensure the validity and reliability of the findings, the criteria introduced by Lincoln and Guba were employed.

Results: The results of this study showed that the e-prescribing system executed in Iran has diverse and multidimensional strengths, weaknesses, opportunities, and threats. In the strengths section, the analysis of the interviews led to the extraction of semantic units in the categories of prescription process, prescriber, patient, technical, economic, communication, and insurance. Also, the weaknesses in the three categories of the prescriber, patient, and technical were debatable. The opportunities extracted from the narratives of the interviewees were placed in four categories including technical, national macro policies, Ministry of Health macro-policies, and socio-cultural issues. Finally, the discussed threats are classified into two technical and macro policies of the Ministry of Health categories. On the other hand, technical issues played an effective role in all aspects of the SWOT model.

Conclusion: The e-prescribing system in Iran has strengths, weaknesses, opportunities, and threats. An overarching factor across all aspects of the SWOT model was technical infrastructure. A robust technical infrastructure is considered a strength and an opportunity for the growth of the electronic prescribing system in Iran. Conversely, any shortcomings in these systems are viewed as weaknesses and pose a threat to the system's sustainability.