Assessing mortality prediction through different representation models based on concepts extracted from clinical notes

Recent years have seen particular interest in using electronic medical records (EMRs) for secondary purposes to enhance the quality and safety of healthcare delivery. EMRs tend to contain large amounts of valuable clinical notes. Learning of embedding is a method for converting notes into a format that makes them comparable. Transformer-based representation models have recently made a great leap forward. These models are pre-trained on large online datasets to understand natural language texts effectively. The quality of a learning embedding is influenced by how clinical notes are used as input to representation models. A clinical note has several sections with different levels of information value. It is also common for healthcare providers to use different expressions for the same concept. Existing methods use clinical notes directly or with an initial preprocessing as input to representation models. However, to learn a good embedding, we identified the most essential clinical notes section. We then mapped the extracted concepts from selected sections to the standard names in the Unified Medical Language System (UMLS). We used the standard phrases corresponding to the unique concepts as input for clinical models. We performed experiments to measure the usefulness of the learned embedding vectors in the task of hospital mortality prediction on a subset of the publicly available Medical Information Mart for Intensive Care (MIMIC-III) dataset. According to the experiments, clinical transformer-based representation models produced better results with getting input generated by standard names of extracted unique concepts compared to other input formats. The best-performing models were BioBERT, PubMedBERT, and UmlsBERT, respectively

Information Sharing and Information Quality in the Drugs and Medical Consumables Supply Chain Management (SCM): Drugs and Medical Consumables Supply Chain Management

Information management is considered as one of the keys to the supply chain management (SCM) success. This study intends to explore the factors affecting the knowledge sharing and knowledge quality in the SCM of the drugs and medical consumables. This applied study was conducted using analytical-survey research method. The research population consisted of 78 people involved in the supply chain of the drug and medical consumables in the teaching hospitals of Isfahan University of Medical Sciences (IUMS). Data were collected by a questionnaire whose face and content validity were both confirmed by pooling the ideas of some professors of Health Information Technology and Health-care Services Management faculties of IUMS. The reliability of the questionnaire was validated by estimating Cornbrash’s alpha coefficient (α=0.93). Data were analyzed using multiple regression analysis method and SPSS software version.23. Based on the results, the environmental uncertainty and intra-organizational facilitators had no impact on information sharing and information quality but inter-organizational relationships influenced the information sharing (p = 0.01) and information quality (p = 0.001). To achieve high-quality information sharing, establishing appropriate relationships with the other chain partners is of critical importance. Based on the results of the study, although there was no statistically significant relationship between the intra-organizational facilitators and environmental uncertainty and the information sharing and information quality, top management support, information technology (IT), appropriate selection of supplier and attention to the drug needs of the patients, should not be neglected

A graph database approach for temporal modeling of disease progression

The high cost of managing chronic diseases for individuals and governments, as well as the negative impact on the quality of life, highlights the importance of controlling and preventing the chronic disease progression. Understanding the disease progression model is one of the first steps, which can lead to more effective planning for interventions. Most of the different approaches for statistical modeling of disease progression work with the graph. On the other hand longitudinal medical data could be represented in the form of a graph and modeling them in this way has a great deal of potential for analyzing and tracking medical event. Data structures, data model features, query facilities and special commands in graph database for traversing and detection patterns could be useful for building summarized information based on transitions between different stages of a particular disease in individual graphs. Given the fact that clinical data is collected at different times, software and formats, there is a need for a flexible framework for data linkage. Use of graph databases brings this flexibility into account and provide a highly scalable framework for data integrating and linkage. In this study, at first simple medical observations related to patients with varying degrees of Alzheimer's disease stored in a graph database (Neo4j) and then by reviewing the capabilities of this environment in building transition graph of different stages of the disease, suggestions for the model development with more details were presented

A Study into patient similarity through representation learning from medical records

Patient similarity assessment, which identifies patients similar to a given patient, can help improve medical care. The assessment can be performed using Electronic Medical Records (EMRs). Patient similarity measurement requires converting heterogeneous EMRs into comparable formats to calculate their distance. While versatile document representation learning methods have been developed in recent years, it is still unclear how complex EMR data should be processed to create the most useful patient representations. This study presents a new data representation method for EMRs that takes the information in clinical narratives into account. To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs. In order to model the extracted data, we employed a tree structure that captures the temporal relations of multiple medical events from EMR. We processed clinical notes to extract symptoms, signs, and diseases using different tools such as medspaCy, MetaMap, and scispaCy and mapped entities to the Unified Medical Language System (UMLS). After creating a tree data structure, we utilized two novel relabeling methods for the non-leaf nodes of the tree to capture two temporal aspects of the extracted events. By traversing the tree, we generated a sequence that could create an embedding vector for each patient. The comprehensive evaluation of the proposed method for patient similarity and mortality prediction tasks demonstrated that our proposed model leads to lower mean squared error (MSE), higher precision, and normalized discounted cumulative gain (NDCG) relative to baselines