Deep learning, while appearing promising for predictive modeling, has not surpassed the performance of traditional techniques; instead, its application within the domain of patient stratification presents an intriguing opportunity. The impact of new, real-time sensor-gathered environmental and behavioral variables still requires a definitive answer.
Scientific literature is a vital source for acquiring crucial biomedical knowledge, which is increasingly essential today. Information extraction pipelines can automatically extract meaningful relationships from textual data, necessitating further review by domain experts to ensure accuracy. In the recent two decades, considerable efforts have been made to unravel connections between phenotypic characteristics and health conditions; however, food's role, a major environmental influence, has remained underexplored. Our research introduces FooDis, a new Information Extraction pipeline. This pipeline uses cutting-edge Natural Language Processing techniques to analyze abstracts of biomedical scientific papers, proposing potential causal or therapeutic links between food and disease entities, referencing existing semantic resources. Comparing our pipeline's predictions with existing relationships reveals a 90% match for food-disease pairs present in both our findings and the NutriChem database, and a 93% match for common pairs within the DietRx platform. The FooDis pipeline's capacity for suggesting relations is also highlighted by the comparison, exhibiting high precision. Further exploration of the FooDis pipeline enables dynamic discovery of new relationships between food and diseases, which should be validated by domain experts and subsequently incorporated into NutriChem and DietRx's existing resources.
AI has enabled the grouping of lung cancer patients into high-risk and low-risk sub-groups based on their clinical features, thereby contributing to more accurate outcome prediction after radiotherapy, garnering significant attention in recent times. Infectious hematopoietic necrosis virus This meta-analysis was carried out to examine the joint predictive impact of AI models on lung cancer, acknowledging the substantial discrepancies in previous findings.
Following the precepts of the PRISMA guidelines, this research was carried out. Relevant literature was sought from the PubMed, ISI Web of Science, and Embase databases. For lung cancer patients who underwent radiotherapy, AI models forecast outcomes, including overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and local control (LC). This anticipated data formed the basis of the pooled effect calculation. The quality, heterogeneity, and publication bias of the constituent studies were also scrutinized.
For this meta-analysis, 4719 patients, stemming from a selection of eighteen articles, met the criteria for inclusion. Selleck Forskolin The consolidated hazard ratios (HRs) across the studies on lung cancer patients show values of 255 (95% CI=173-376) for OS, 245 (95% CI=078-764) for LC, 384 (95% CI=220-668) for PFS, and 266 (95% CI=096-734) for DFS. In a pooled analysis of articles on OS and LC in lung cancer patients, the area under the receiver operating characteristic curve (AUC) was 0.75 (95% CI = 0.67-0.84) and 0.80 (95% confidence interval: 0.68-0.95). The JSON format must return a list of sentences.
Clinical trials demonstrated the feasibility of employing AI to predict outcomes in lung cancer patients following radiotherapy. Precisely forecasting patient outcomes in lung cancer demands the execution of large-scale, prospective, multicenter studies.
Clinical trials highlighted the effectiveness of AI models in predicting post-radiotherapy outcomes in lung cancer patients. hepatoma-derived growth factor Precisely anticipating the outcomes for lung cancer patients requires the implementation of large-scale, multicenter, prospective studies.
mHealth apps, providing a means of collecting real-life data, are beneficial as supporting tools in various treatment approaches. In spite of this, datasets of this nature, especially those derived from apps depending on voluntary use, frequently experience inconsistent engagement and considerable user desertion. Data exploitation through machine learning strategies is obstructed, raising concerns about user inactivity with the application. This paper elaborates on a technique for recognizing phases with inconsistent dropout rates in a dataset and forecasting the dropout percentage for each phase. Our study also presents an approach to estimate the expected length of time a user will remain inactive, considering their current status. Phase determination is accomplished using change point detection; we present a strategy for dealing with irregular, misaligned time series data and predicting user phase through time series classification. Moreover, we explore the unfolding patterns of adherence across individual clusters. Data from a tinnitus mHealth application was used to examine our methodology, illustrating its applicability in studying adherence patterns within datasets that exhibit uneven, unaligned time series of different lengths and include missing data.
Handling missing data values properly is vital for accurate estimations and informed decisions, especially in sensitive fields like clinical research. To cope with the burgeoning diversity and multifaceted nature of data, numerous researchers have developed deep learning-based imputation techniques. This systematic review evaluated the application of these techniques, focusing on the kinds of data collected, for the purpose of supporting researchers in various healthcare disciplines to manage missing data.
Articles that detailed the use of DL-based models in imputation, published before February 8, 2023, were systematically extracted from five databases: MEDLINE, Web of Science, Embase, CINAHL, and Scopus. Our review of selected publications included a consideration of four key areas: data formats, the fundamental designs of the models, imputation strategies, and comparisons with methods not utilizing deep learning. Deep learning model adoption was mapped through an evidence map differentiated by data type characteristics.
Analysis of 1822 articles yielded 111 included articles. The most frequently researched categories within this group were tabular static data (29%, 32 of 111 articles) and temporal data (40%, 44 of 111 articles). The analysis of our findings demonstrates a notable trend in model architecture selections and data types, including the significant application of autoencoders and recurrent neural networks when dealing with tabular time-series data. The diverse application of imputation strategies was also observed when comparing different data types. The most common approach to imputation, integrating the process with subsequent downstream tasks, was most popular for tabular temporal datasets (52%, 23/44) and multi-modal datasets (56%, 5/9). Deep learning imputation methods consistently outperformed non-deep learning methods in terms of imputation accuracy across numerous investigations.
Imputation models, based on deep learning, encompass a variety of network architectures. Data types' diverse characteristics often influence the specific designation they receive in healthcare. Although deep learning-based imputation models are not always superior to conventional techniques for all datasets, they might nonetheless deliver satisfactory performance on specific data types or datasets. Current deep learning-based imputation models, while powerful, have yet to overcome the limitations of portability, interpretability, and fairness.
The family of deep learning-based imputation models is marked by a diversity of network configurations. Healthcare designations are usually adjusted based on the different characteristics of the data types. Although DL-based imputation models do not always outperform conventional approaches on all datasets, they have the potential to achieve satisfactory results for a particular dataset or a specific data type. Difficulties in terms of portability, interpretability, and fairness persist in current deep learning-based imputation models.
A group of natural language processing (NLP) tasks are used in medical information extraction to convert clinical text into pre-defined, structured data representations. This stage is vital to the exploration of possibilities inherent in electronic medical records (EMRs). The flourishing advancement of NLP technologies has seemingly made model implementation and performance less of a barrier, yet the hurdle now lies in creating a high-quality annotated corpus and the sophisticated engineering processes. The current study introduces an engineering framework with three essential tasks: medical entity recognition, relation extraction, and attribute extraction. This framework demonstrates the complete workflow, from EMR data acquisition to model performance assessment. Compatibility across various tasks is a key design feature of our comprehensive annotation scheme. Our corpus's large scale and high quality are ensured by electronic medical records from a general hospital in Ningbo, China, and the manual annotation process conducted by experienced physicians. A Chinese clinical corpus provides the basis for the medical information extraction system, whose performance approaches human-level annotation accuracy. The annotated corpus, (a subset of) which includes the annotation scheme, and its accompanying code are all publicly released for further research.
Evolutionary algorithms have demonstrated their capacity to find the optimal structure for various learning algorithms, with neural networks being a prime example. Because of their versatility and positive results, Convolutional Neural Networks (CNNs) have been extensively used in many image processing operations. The design of Convolutional Neural Networks profoundly influences their performance metrics, including precision and computational resources, making the selection of an ideal structure crucial before practical application. This paper details a genetic programming approach for improving the design of convolutional neural networks for the accurate diagnosis of COVID-19 cases using X-ray images.