Text summarization of medical records

Zásadní součástí zdravotní péče je vytváření, zpracování a uchovávání kvalitní zdravotnické dokumentace. Tento proces je časově náročný a značně zatěžuje zdravotnický personál. Cílem této práce je prozkoumat možnost automatizace části tohoto procesu. Konkrétně se tato práce zaměřuje na automatické generování odstavce "Průběh hospitalizace" v propouštěcí zprávě, což je důležitý lékařský dokument pro zajištění kontinuity péče. V práci jsou použity dva české textové lékařské datasety ze dvou oddělení Institutu Klinické a Experimentální Medicíny (IKEM). Datasety jsou nejdříve zanalyzovány a zpracovány do vhodného formátu. Jako základ řešení úlohy jsou využity jazykové modely založené na architektuře Transformer, předtrénované na vícejazyčných datasetech. Tyto modely jsou dále učeny na našem datasetu zpracovaném do formátu vhodného pro sumarizaci textu. Jsou prozkoumány jak extraktivní, tak abstraktivní přístupy k sumarizaci textu. Všechny modely jsou hodnoceny pomocí automatických metrik. Ty ukazují, že abstraktivní metody fungují lépe v porovnání s těmi extraktivními. Dále je provedeno manuální hodnocení nejlepších abstraktivních modelů, které ukazuje, že modely generují průběh hospitalizace správně na více než 40% testovacích vzorcích. Manuální hodnocení dále ukazuje, že automatické metriky měřící kvalitu generovaného průběhu hospitalizace (v porovnání s průběhem hospitalizace napsaného lékařem) jsou konzistentní s manuálním ohodnocením, což ospravedlňuje jejich použití.A vital part of healthcare is creating, processing and storing good quality medical documentation. The task is, however, time-consuming and burdens the medical care workers heavily. This thesis's goal is to explore the possible automation of part of this process. Concretely, it focuses on automatically generating the Hospitalization summarization paragraph of the Discharge report, an important medical document ensuring continuity of care. To this end, two Czech text medical datasets from two departments of the Institute for Clinical and Experimental Medicine (IKEM) are used. The datasets are analyzed and preprocessed for the task. Language models based on the Transformer architecture, pretrained on multilingual datasets are utilized. The models are further fine-tuned on the datasets, that are preprocessed for a text summarization task. Both extractive and abstractive text summarization approaches are explored. All the models are evaluated using automatic metrics. The automatic metrics show that abstractive summarization methods outperform the extractive ones on the task. Further, manual evaluation of the best performing abstractive summarization models is also conducted, showing that the models solve the task correctly on over 40% of the test samples. The manual evaluation also shows that the automatic metrics measuring the quality of the generated summary (using the summary written by the doctor as a reference) are consistent with the manually assigned quality labels, which justifies their use

Architecture and Time—Time in Architecture

Time is a physical quantity that has perpetually challenged humankind. A meter will always be a meter, and a kilo a kilo, but time can in many ways be interpreted as a relative term. In our contemporary world, many common devices are intricately related to time; the computer, the radio, the phone, and the television for example

Input-Output Representations for Supervised Clustering Methods

V rámci této práce zkoumáme možná řešení pro problém supervizovaného shlukování se zaměřením na metody založené na neuronových sítích. Naší motivací jsou problémy jako je shrnutí textu podle sémantiky, extrakce tématu z textu a rychlá anotace dat. Cílem modelu řešícího supervizované shlukování je zpracovat množinu obsahující proměnný počet prvků a rozdělit tyto prvky do shluků. Za tímto účelem je model nejdříve trénován pomocí dat u kterých známe správné rozdělení prvků do shluků. V první části zkoumáme přístupy strojového učení ke zpracování dat strukturovaných jako proměnný počet prvků v množině a současné state of the art metody pro řešení supervizovaného shlukování. Uvádíme teorii nezbytnou k definování metod zpracovávajících množiny prvků a podrobně popisujeme dva state of the art modely pro řešení supervizovaného shlukování. Dále jsme navrhli dvě nové metody, z nichž každá používá jiné kódování vstupu a výstupu. Pomocí experimentů na jednom reálném a dvou syntetických datasetech porovnáváme popsané state of the art me- tody s metodami které jsme navrhli. Zaměřujeme se na schopnost zpracovat data se závislostmi mezi prvky uvnitř shluků a na škálovatelnost zkoumaných modelů při rostoucí velikosti vstupu.In this thesis, we explore solutions to the supervised clustering problem, focusing on neural network-based methods. We are motivated by problems such as semantic text summarization, topic extraction, and fast annotation of data. The goal of a supervised clustering model is to partition a set of a variable number of elements into clusters. This is done by first training the model using labeled data. In the first part, we explore machine learning approaches to processing set-structured data and the current state of the art methods for solving the supervised clustering problem. The theory necessary to define set-processing methods is reviewed, and two state of the art models for solving the supervised clustering problem are described in detail. We further propose two new methods, each using a different representation of the input and output. Using experiments on one real-world and two synthetic datasets, we compare the two state of the art methods with the proposed methods. We explore the ability to deal with intra-cluster data dependencies and the scalability of the examined models to the size of a set of elements to be clustered

A Primer on the Developing Doctrine of Constructive Fraud in Montana

A PRIMER ON THE DEVELOPING DOCTRINE OF CONSTRUCTIVE FRAUD IN MONTAN

A Primer on the Developing Doctrine of Constructive Fraud in Montana

A PRIMER ON THE DEVELOPING DOCTRINE OF CONSTRUCTIVE FRAUD IN MONTAN

Subseasonal hydrometeorological ensemble predictions in small- and medium-sized mountainous catchments: benefits of the NWP approach

Traditional ensemble streamflow prediction (ESP) systems are known to provide a valuable baseline to predict streamflows at the subseasonal to seasonal timescale. They exploit a combination of initial conditions and past meteorological observations, and can often provide useful forecasts of the expected streamflow in the upcoming month. In recent years, numerical weather prediction (NWP) models for subseasonal to seasonal timescales have made large progress and can provide added value to such a traditional ESP approach. Before using such meteorological predictions two major problems need to be solved: the correction of biases, and downscaling to increase the spatial resolution. Various methods exist to overcome these problems, but the potential of using NWP information and the relative merit of the different statistical and modelling steps remain open. To address this question, we compare a traditional ESP system with a subseasonal hydrometeorological ensemble prediction system in three alpine catchments with varying hydroclimatic conditions and areas between 80 and 1700&thinsp;km2. Uncorrected and corrected (pre-processed) temperature and precipitation reforecasts from the ECMWF subseasonal NWP model are used to run the hydrological simulations and the performance of the resulting streamflow predictions is assessed with commonly used verification scores characterizing different aspects of the forecasts (ensemble mean and spread). Our results indicate that the NWP-based approach can provide superior prediction to the ESP approach, especially at shorter lead times. In snow-dominated catchments the pre-processing of the meteorological input further improves the performance of the predictions. This is most pronounced in late winter and spring when snow melting occurs. Moreover, our results highlight the importance of snow-related processes for subseasonal streamflow predictions in mountainous regions.</p

Varespladib and cardiovascular events in patients with an acute coronary syndrome: the VISTA-16 randomized clinical trial

IMPORTANCE: Secretory phospholipase A2(sPLA2) generates bioactive phospholipid products implicated in atherosclerosis. The sPLA2inhibitor varespladib has favorable effects on lipid and inflammatory markers; however, its effect on cardiovascular outcomes is unknown. OBJECTIVE: To determine the effects of sPLA2inhibition with varespladib on cardiovascular outcomes. DESIGN, SETTING, AND PARTICIPANTS: A double-blind, randomized, multicenter trial at 362 academic and community hospitals in Europe, Australia, New Zealand, India, and North America of 5145 patients randomized within 96 hours of presentation of an acute coronary syndrome (ACS) to either varespladib (n = 2572) or placebo (n = 2573) with enrollment between June 1, 2010, and March 7, 2012 (study termination on March 9, 2012). INTERVENTIONS: Participants were randomized to receive varespladib (500 mg) or placebo daily for 16 weeks, in addition to atorvastatin and other established therapies. MAIN OUTCOMES AND MEASURES: The primary efficacy measurewas a composite of cardiovascular mortality, nonfatal myocardial infarction (MI), nonfatal stroke, or unstable angina with evidence of ischemia requiring hospitalization at 16 weeks. Six-month survival status was also evaluated. RESULTS: At a prespecified interim analysis, including 212 primary end point events, the independent data and safety monitoring board recommended termination of the trial for futility and possible harm. The primary end point occurred in 136 patients (6.1%) treated with varespladib compared with 109 patients (5.1%) treated with placebo (hazard ratio [HR], 1.25; 95%CI, 0.97-1.61; log-rank P = .08). Varespladib was associated with a greater risk of MI (78 [3.4%] vs 47 [2.2%]; HR, 1.66; 95%CI, 1.16-2.39; log-rank P = .005). The composite secondary end point of cardiovascular mortality, MI, and stroke was observed in 107 patients (4.6%) in the varespladib group and 79 patients (3.8%) in the placebo group (HR, 1.36; 95% CI, 1.02-1.82; P = .04). CONCLUSIONS AND RELEVANCE: In patients with recent ACS, varespladib did not reduce the risk of recurrent cardiovascular events and significantly increased the risk of MI. The sPLA2inhibition with varespladib may be harmful and is not a useful strategy to reduce adverse cardiovascular outcomes after ACS. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01130246. Copyright 2014 American Medical Association. All rights reserved

Drone-based photogrammetry combined with deep-learning to estimate hail size distributions and melting of hail on the ground

Hail is a major threat associated with severe thunderstorms and an estimation of the hail size is important for issuing warnings to the public. Operational radar products exist that estimate the size of the expected hail. For the verification of such products, ground based observations are necessary. Automatic hail sensors, as for example within the Swiss hail network, record the kinetic energy of hailstones and can estimate with this the hail diameters. However, due to the small size of the observational area of these sensors (0.2 m2) the estimation of the hail size distribution (HSD) can have large uncertainties. To overcome this issue, we combine drone-based aerial photogrammetry with a state-of-the-art custom trained deep-learning object detection model to identify hailstones in the images and estimate the HSD in a final step. This approach is applied to photogrammetric image data of hail on the ground from a supercell storm, that crossed central Switzerland from southwest to northeast in the afternoon of June 20, 2021. The hail swath of this intense right-moving supercell was intercepted a few minutes after the passage at a soccer field near Entlebuch (Canton Lucerne, Switzerland) and aerial images of the hail on the ground were taken by a commercial DJI drone, equipped with a 50 megapixels full frame camera system. The average ground sampling distance (GSD) that could be reached was 1.5 mm per pixel, which is set by the mounted camera objective with a focal length of 35 mm and a flight altitude of 12 m above ground. A 2D orthomosaic model of the survey area (750 m2) is created based on 116 captured images during the first drone mapping flight. Hail is then detected by using a region-based Convolutional Neural Network (Mask R-CNN). We first characterize the hail sizes based on the individual hail segmentation masks resulting from the model detections and investigate the performance by using manual hail annotations by experts to generate validation and test data sets. The final HSD, composed of 18209 hailstones, is compared with nearby automatic hail sensor observations, the operational weather radar based hail product MESHS (Maximum Expected Severe Hail Size) and some crowdsourced hail reports. Based on the retrieved drone hail data set, a statistical assessment of sampling errors of hail sensors is carried out. Furthermore, five repetitions of the drone-based photogrammetry mission within about 18 min give the unique opportunity to investigate the hail melting process on the ground for this specific supercell hailstorm and location

Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland

Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)