Causal Text-to-Text Transformers for Water Pollution Forecasting

We propose a novel approach based on large language causal models to perform the task of time-series forecasting, and we use the proposed approach to effectively forecast the concentration of polluting substances in a water treatment plant; we address both short- and mid-term forecasting. As opposed to the classical state-of-the-art approaches for time-series forecasting, that handle numerical and categorical features following a standard deep learning approach, we transform the input features into a textual form and we then feed them to a standard causal model pre-trained on natural language tasks. Our empirical results provide evidence that large language models are more effective than state-of-the-art forecasting systems, and that they can be practically used in time-series forecasting tasks. We also show promising results on zero-shot learning. The results of this study open up to a wide range of works aimed at predicting future temporal values by leveraging natural language paradigms and models

Logical Rules and a Preliminary Prototype for Translating Mortality Coding Rules from ICD-10 to ICD-11

Iris is a system for coding multiple causes of death in ICD-10 and for the selection of the underlying cause of death, based on a knowledge base composed by a large number of rules. With the adoption of ICD-11, those rules need translation to ICD-11. A pre-project has been carried out to evaluate feasibility of transition to ICD-11, which included the analysis of the logical meta-rules needed for rule translation and development of a prototype support system for the expert that will translate the coding rules

Harmonization of ICF Body Structures and ICD-11 Anatomic Detail: One foundation for multiple classifications

The Family of International Classifications of the World Health Organization (WHO-FIC) currently includes three reference classifications, namely International Classification of Diseases (ICD), International Classification of Functioning, Disability, and Health (ICF), and International Classification of Health Interventions (ICHI). Recently, the three classifications have been incorporated into a single WHO-FIC Foundation that serves as the repository of all concepts in the classifications. Each classification serves a specific classification need. However, they share some common concepts that are present, in different forms, in two or all of them. For the WHO-FIC Foundation to be a logically consistent repository without duplicates, these common concepts must be reconciled. One important set of shared concepts is the representation of human anatomy entities, which are not always modeled in the same way and with the same level of detail. To understand the relationships among the three anatomical representations, an effort is needed to compare them, identifying common areas, gaps, and compatible and incompatible modeling. The work presented here contributes to this effort, focusing on the anatomy representations in ICF and ICD-11. For this aim, three experts were asked to identify, for each entity in the ICF Body Structures, one or more entities in the ICD-11 Anatomic Detail that could be considered identical, broader or narrower. To do this, they used a specifically developed web application, which also automatically identified the most obvious equivalences. A total of 631 maps were independently identified by the three mappers for 218 ICF Body Structures, with an interobserver agreement of 93.5%. Together with 113 maps identified by the software, they were then consolidated into 434 relations. The results highlight some differences between the two classifications: in general, ICF is less detailed than ICD-11; ICF favors lumping of structures; in very few cases, the two classifications follow different anatomic models. For these issues, solutions have to be found that are compliant with the WHO approach to classification modeling and maintenance

Supporting Fair and Efficient Emergency Medical Services in a Large Heterogeneous Region

Emergency Medical Services (EMS) are crucial in delivering timely and effective medical care to patients in need. However, the complex and dynamic nature of operations poses challenges for decision-making processes at strategic, tactical, and operational levels. This paper proposes an action-driven strategy for EMS management, employing a multi-objective optimizer and a simulator to evaluate potential outcomes of decisions. The approach combines historical data with dynamic simulations and multi-objective optimization techniques to inform decision-makers and improve the overall performance of the system. The research focuses on the Friuli Venezia Giulia region in north-eastern Italy. The region encompasses various landscapes and demographic situations that challenge fairness and equity in service access. Similar challenges are faced in other regions with comparable characteristics. The Decision Support System developed in this work accurately models the real-world system and provides valuable feedback and suggestions to EMS professionals, enabling them to make informed decisions and enhance the efficiency and fairness of the system

An economic analysis of email-based telemedicine: A cost minimisation study of two service models

<p>Abstract</p> <p>Background</p> <p>Email-based telemedicine has been reported to be an efficient method of delivering online health services to patients at a distance and is often described as a low-cost form of telemedicine. The service may be low-cost if the healthcare organisation utilise their existing email infrastructure to provide their telemedicine service. Many healthcare organisations use commercial-off-the-shelf (COTS) email applications. COTS email applications are designed for peer-to-peer communication; hence, in situations where multiple clinicians need to be involved, COTS applications may be deficient in delivering telemedicine. Larger services often rely on different staff disciplines to run their service and telemedicine tools for supervisors, clinicians and administrative staff are not available in COTS applications. Hence, some organisations may choose to develop a purpose-written email application to support telemedicine. We have conducted a cost-minimisation analysis of two different service models for establishing and operating an email service. The first service model used a COTS email application and the second used a purpose-written telemedicine application.</p> <p>Methods</p> <p>The actual costs used in the analysis were from two organisations that originally ran their counselling service with a COTS email application and later implemented a purpose-written application. The purpose-written application automated a number of the tasks associated with running an email-based service. We calculated a threshold at which the higher initial costs for software development were offset by efficiency gains from automation. We also performed a sensitivity analysis to determine the effect of individual costs on the threshold.</p> <p>Results</p> <p>The cost of providing an email service at 1000 consultations per annum was $19,930 using a COTS email application and$31,925 using a purpose-written application. At 10,000 consultations per annum the cost of providing the service using COTS email software was $293,341 compared to$272,749 for the purpose-written application. The threshold was calculated at a workload of 5216 consultations per annum. When more than 5216 email consultations per annum are undertaken, the purpose-written application was cheaper than the COTS service model. The sensitivity analysis showed the threshold was most sensitive to changes in administrative staff salaries.</p> <p>Conclusion</p> <p>In the context of telemedicine, we have compared two different service models for email-based communication – purpose-written and COTS applications. Under the circumstances described in the paper, when workload exceeded 5216 email consultations per annum, there were savings made when a purpose-written email application was used. This analysis provides a useful economic model for organisations contemplating the use of an email-based telemedicine system.</p

Natural Language Processing to extract SNOMED-CT codes from pathological reports

Objective. The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department. Methods. Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports. Results. The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance. Conclusions. AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports

Linking Whole-Slide Microscope Images with DICOM by Using JPEG2000 Interactive Protocol

The use of digitized histopathologic specimens (also known as whole-slide images (WSIs)) in clinical medicine requires compatibility with the Digital Imaging and Communications in Medicine (DICOM) standard. Unfortunately, WSIs usually exceed DICOM image object size limit, making it impossible to store and exchange them in a straightforward way. Moreover, transmitting the entire DICOM image for viewing is ineffective for WSIs. With the JPEG2000 Interactive Protocol (JPIP), WSIs can be linked with DICOM by transmitting image data over an auxiliary connection, apart from patient data. In this study, we explored the feasibility of using JPIP to link JPEG2000 WSIs with a DICOM-based Picture Archiving and Communications System (PACS). We first modified an open-source DICOM library by adding support for JPIP as described in the existing DICOM Supplement 106. Second, the modified library was used as a basis for a software package (JVSdicom), which provides a proof-of-concept for a DICOM client–server system that can transmit patient data, conventional DICOM imagery (e.g., radiological), and JPIP-linked JPEG2000 WSIs. The software package consists of a compression application (JVSdicom Compressor) for producing DICOM-compatible JPEG2000 WSIs, a DICOM PACS server application (JVSdicom Server), and a DICOM PACS client application (JVSdicom Workstation). JVSdicom is available for free from our Web site (http://jvsmicroscope.uta.fi/), which also features a public JVSdicom Server, containing example X-ray images and histopathology WSIs of breast cancer cases. The software developed indicates that JPEG2000 and JPIP provide a well-working solution for linking WSIs with DICOM, requiring only minor modifications to current DICOM standard specification

Ontology-based, Tissue MicroArray oriented, image centered tissue bank

<p>Abstract</p> <p>Background</p> <p>Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information.</p> <p>Results</p> <p>In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data.</p> <p>Conclusions</p> <p>Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes.</p