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Please upgrade your browser to improve your experience and security IBPS released SO Mains scorecards through ibps.in today during March 20 The official website of IBPS at ibps.in hosts Specialist Officer exam 2025 (CRP SPL-XIV) results from December 14 The scoring system for IBPS SO Main 2025 is accessible for download until March 31 Having performed their Specialist Officer Main examination on December 14 2024 candidates can obtain their scores through the steps found on this page How to download IBPS SO Main 2025 scorecards Navigate the link to the 'IBPS SO Main 2025 scorecards' It will redirect you to the login page where you need to provide your registration number IBPS SO Main 2025 scorecards will appear on the screen The shortlisted candidates from CRP SPL-XIV Online Main Examination will receive interview invitations for Participating Banks conducted by Local Banks under IBPS management in each State/ UT/ Region The selected centres will become the venues for conducting interviews The call letter from the centre will include both the venue address as well as the scheduled date and time for Interviews The entirety of marks that contribute to the interview amount to 100 Participants who seek admittance into the interview phase must meet a minimum score requirement which equals 40% but only 35% for candidates belonging to SC/ST/OBC/PwBD categories Any scores received by the interview candidates who fail the minimum qualifying standards or barred from the selection process remain undisclosed to all parties Candidates must qualify online main examination alongside their interview performance to receive provisional allotment according to following details that will appear on ibps.in \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n\n \n \n\n \n \n \n \n \n \n\n \n Architecture Engineering\n& Construction\nIntegrated BIM tools Professional CAD/CAM tools built on Inventor and AutoCAD Design & Make with Autodesk Learn how 3D modeling and digitalization helped train builder AFF speed up its processes as it designed Le Grand Tour a Belle Époque–style hotel train that is destined to shape the future of boutique train travel in France A rendering of the Le Grand Tour luxury train Le Grand Tour is a new luxury hotel train that will soon begin taking passengers on a six-day The project was a growth opportunity for Ateliers de Fabrication Ferroviaire (AFF) a small French company behind the train’s design AFF draws on digital tools like 3D modeling to increase efficiency and coordinate design processes even though it has yet to leave the station “This is a completely new project that has taken us to a whole new level,” says Willy Snauwaert “The company has suddenly transformed from a railway carriage maintenance service into a haute couture workshop.” the small business employed 15 people and maintained freight wagons and passenger carriages AFF has hired 70 new employees from a variety of trades—including engineers and industrial designers—to keep up with the demands of its client the company behind the luxury train experience Creating a long-distance luxury train with a high-end services and amenities was an opportunity for AFF’s teams to expand their offerings This project helped the company turn its focus to a market of passionate travelers interested in luxurious train travel offering amenities like a gourmet restaurant on board and a luxury hotel service with a butler assigned to each cabin Even though the train is not yet on the tracks Snauwaert says the project could provide a showcase for the company to find similar markets in Europe “From a technical and a regulatory point of view this is an extraordinary project,” he says To speed up the development and approval phases it acquired railway rolling stock from the 1960s to renovate based on an approved model; and second which teams had to quickly learn how to apply in order to scale the project AFF’s digital transformation began in February 2021 and reached cruising speed in September of the following year The company uses Autodesk Inventor for its digital models which let the team integrate new components into the digital mockup and translate them into drawings for the production of parts “We wouldn’t have been able to carry out the project without it,” Snauwaert says To make the process run smoothly and to keep multiple designers working on the same document from accidentally overwriting each other’s work, AFF turned to Upchain an Autodesk cloud-based product data management (PDM) solution Upchain is integrated into Inventor and manages design data and engineering processes so that the AFF team can concentrate on its work and not waste time searching for data “We were experiencing synchronization problems and had several heart-stopping moments after losing data,” Snauwaert says “We needed a digital vault where we could find each previous version of the parts we produced This tool allows us to identify bad practices AFF has seen its efficiency improve thanks to digital tools data research can account for up to 20% of a designer’s working time and losing data can eat up an additional 20% AFF has received excellent support from Autodesk and RMR for using these tools,” Snauwaert says the AFF teams are working to improve validation workflows so that 2D and 3D plans are sent to the right people “The next step will be to manage the lifecycle of spare parts to ensure the right balance between purchasing and stocking,” Snauwaert says In addition to launching the Le Grand Tour carriages AFF hopes that the Grand Tour project will draw the attention of industry professionals to its skills in integrating new technologies into rolling stock AFF isn’t going to rest on its laurels and miss the next train Maxime Thomas is an editor for the French national and specialized press He has also worked in radio and covers various aspects of industrial life including digital transformation and its specific consequences for certain professions Learn how companies are designing and making a better world through innovation; keep up with accelerating technological advancements; and discover insights about the drivers of change impacting your industry Please select what you would like included for printing: Copy the text below and then paste that into your favorite email application This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply Service map data © OpenStreetMap contributors Baller Brown could do -and did- the following for you… Brown was raised in Camden New Jersey and graduated from Camden High School; where he set the school record as a quarterback with 47 touchdown passes Then Brown did the following post his high school career Brown switched to cornerback at Western Carolina Where he became a first-team All-SoCon selection and team captain ATH1 to be sure to do that as a new position Brown spent time with the Cincinnati Bengals during the 2oo7 and 2oo8 seasons on the practice squad He has had a couple of seasons as a Temple Adm.Min’ Flying a desk in their Athletic Department itself he prolly does get that bureaucratic (paper-pusher) side superior to most Baylor and Temple both called him their best recruiter in years He is said to have very deep Jersey Shole and affiliated/neighboring ties and recruiting roots up Nor-by-Nor-East there as in… never ever never never… even been a full Coordinator; much less a big-whistle or Head Honcho As some thought this to be a career bridge too far— or at least a bridge too soon Does pack some Family Fued: Steve Harvey swagg to his look(s) The very first this his BIO reads is: “The nation’s top recruiter Took his mum off of life-support (’16) his ownself Syracuse 2o23 record: 6 up 7 down and 2-6 in the A.c.c Defensive letter grade: This unit does rank well It does not seem so swell to me upon breaking tape Offensive letter grade: here the film and the ratings did align enuff… it bleeds a few plays/point that i leaves out there on tape and out on the field alike it has at least a little measure of headroom… maybe it is due to pop: clean after such a messy last week (This O should Oct.31st scare you mo’ than a scoschose… A—) Syracuse is 86th in Net Punting; and so is P1 Jack -of the WWII or vintage Errol Flynn narrow top-down shaved ‘stache– is a cool looking chap How such a retro swagg ’24 she-sells I do not know And he has the digits to back this in like Flynt look up… what with… being named: Third Team All-A.c.c After he produced the second-best net punting season in program history and 22nd nationally in punting this season Stoney made a whopping six tackles on the kickoff team out in H.S this is not your typical fondue-guzzling specialist was a punter at USC and in the NFL for the New York Giants was a punter at Colorado State and is now playing in the N.f.l Though he is a bit slow on the motion/drop… hence the swats would be: Brady Denaburg and then Chantilly Both are no-no’s… as Brady is all middle-child freaky at: 5o% And Jay’ is even mo’ Cindy’ed at 4o% As neither has gone well thus far this year I do know that Brady is said to have the bigger boot The only thingy being… it might land in Kentucky Oh is o’fer anything beyond ≥ 32 yards in collegiate terms thus far figure on some 4th-downs going for it or some short-punts here As right now neither K1 is much mo’ than a K3 And VT has a toe-the-line edge to be sure here The skeleton key to unlocking this one here is to.. View Results …the takeaway is… neither team played worth much of anything last week And yet one Orange gang went down and the other one moved onward and upward Which left us 4 wondering ‘out loud’ -and we all know just how dangerous that can be- if one of these two oranges was mo’ parts lemons In terms of team-tuffness or chemistry or lockeroom/cultural classification As that could help in terms of exercising some bad and truly overhyped ju-ju here in other words… although neither squadron is complete enuff to be a true alpha here one club could be closer to being the gamma than the other NOT the team that seems to not like G&R or Cold November Those who vote… in the People’s Republick of the 3o4 you ask Now that your fascist blood is big pumpin’… the polytrix of Fight Club here rate about… when we consult: gallop polling itself We foresee and fourscore two-quarter horses here VeeTee must control what they can control on O they must choose to sit the game-clock gunny ‘cuse O They let the ‘cuse O start to cook and that will abort the whole #ChallangeA.c.c.epted… there are 1,440 minutes in a day and it will take the duration or a Ric Flair 6o-minute man nooner …what Eye see here is… I see the 3rd fastest Tempo in all the 134-team TO’s to sub-in/out if necessary and just for catching a breather may not be the worst idea here VT had dropped their last 14 games when scoring 21 points or fewer (last win: 2o21 Richmond VT was 5-34 in their last 39 games scoring 21 pts or fewer ‘cuse is average on D in both opposing Qb’s sacked and in T.F.L are an inviting looking 113th best in T.F.L In getting in the sack terms… we see that ‘cuse is nearly 90-odd spots from us in terms of Pass-Pro’ They throw a LOT which does skew this… just not enuff to ‘splain this successfully/defensibly on their behalf ‘cuse is also supsringly user-friendly in T.F.L …here in the sportlight we limelight Qb1 25 ranked played regardless of position in the Class of 2o21 and a five-star prospect by the 247Sports Composite rankings Joseph’s Prep win its third-straight state championship Where he threw for six touchdowns in the Pennsylvania State 6A Semifinal and followed it up with four passing touchdowns in the title game A cardiac Qb1 to level his 3-bling game accordingly No wonder he keeps is OWN brand of tater-chips Not quite Kosar (side) or 12-o’clock (Gregory Peck) high No inks or favelins or branding to be seen Not a modern-era basketball on grass streety Qb1 On his lowest completion length metric of his career literally; pun-intended in distancing aerial terms 6′ shorter this year than any year b4 in completion distance terms Friendlier RTG is down nearly –26 points this campaign for it And has his ONLY (2) rushing TD’s this season thus far (13 sacks in his last 2o Q’s will do that to that) Down –16 in the stricter (mo’ Pro’) QBR vital as well Did throw a bit longer/deeper for ’23 ‘cuse nearly +3oo% mo’ major (TD) chucking prolific @home than out on the road There is also his near +4o-point betterment in QBR @home to boot VERY steady Qb half-to-half and 1Q out to 4Q Though he is nearly +2oo% better rushing in his own backyard than in yours Had been warming of late… until Pitt caught five Kyle’s father played Quarterback2 at Rutgers from 1988-1992 441-yarder passing career with 3 TD’s threw the air and –31 in ∑ (total) on the ground in two participatory seasons Though and either way… Kyle did come up with a cleat for a pacifier in his pigskin-lined This is a football-1st fam’ to be sure It downfield shows… ‘nother Major Harris in love with the chicks who love long and he’s the most accurate of all Atlantic Coast QBs on passes 20+ yards downfield (just over 51%) Best we will see in terms of vertical(s); or Opponents are running the ball less than 27 times per game against the Orange ’cause here we devoutly do NOT believe in: jinxs …or much less in textbook: Extrinsic Foci of That said… ‘word’ ‘rond the campfire makes us feel like we are not 1oo% locked even anywhere near limited itself… that makes our O very: balky and least we forgets… the way to play this non-Darell Lamonican non-mad-bomber is via the Under-Zone-flood That someone named (coach) Schwartzwalder invented it at some place named: ‘Syracuse’… way back when Brown did Playgirl to you You always think we’re worse off I hurt wise than the opponent Tuten is as fine as an RB can be 8 games in Chaplin has been and will deal with his shoulder The leg whip hurt like H at the time but it’s basically a bruise If we don’t win it’ll be because Marve didn’t game plan appropriately And no teammate calls a running back a drama queen on national tv if said rb is not ready to tugboat another fun read in the books from the 304– even a touch of Maj Harris in todays insight Look for the men in White & Marroon to play keep away from PA’s finest since Tommy Cruise All the Right Moves fame & glory to take the air out of the ball and R-P-O the ball to a VicTory today in the jiffy pop dome! View Results Metrics details A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data The mental health domain is particularly challenging partly because clinical documentation relies heavily on free text that is difficult to de-identify completely This problem could be tackled by using artificial medical data we present an approach to generate artificial clinical documents We apply this approach to discharge summaries from a large mental healthcare provider and discharge summaries from an intensive care unit We perform an extensive intrinsic evaluation where we (1) apply several measures of text preservation; (2) measure how much the model memorises training data; and (3) estimate clinical validity of the generated text based on a human evaluation task we perform an extrinsic evaluation by studying the impact of using artificial text in a downstream NLP text classification task We found that using this artificial data as training data can lead to classification results that are comparable to the original results using only a small amount of information from the original data to condition the generation of the artificial data is successful which holds promise for reducing the risk of these artificial data retaining rare information from the original data This is an important finding for our long-term goal of being able to generate artificial clinical data that can be released to the wider research community and accelerate advances in developing computational methods that use healthcare data Text generation is an active area of NLP research covering tasks The attempt closest to ours is the one of Lee6 They generate short-length (<20 tokens) chief complaint documents patient- and admission-related information as conditions They employ a fairly simple encoder–decoder (ED) architecture The clinical validity of the generated text is investigated by using it as test data for NLP models built with real data The utility of the generated data for downstream NLP tasks is rarely analysed few studies investigate to what extent these models retain rare information from the original data—rare information could potentially contain sensitive information there have been no attempts to automatically generate full EHR notes for NLP purposes we focus on the generation also of mental health records (MHRs) MHRs are characterised by a greater extent of complex narrative and rely less on structured coding Key phrases are extracted from paragraphs in the original data (genuine paragraph) and combined with clinical information (ICD-10 diagnosis code We perform an extensive intrinsic evaluation of generated text to (1) measure text preservation by using a range of shallow automatic metrics (2) measure how much the models memorise information from the training data and (3) assess the clinical validity of the generated text through human evaluation An NLP model is built on 1) genuine data and 2) artificial data Both models are tested on real (genuine) test data Comparing these results gives an indication of the usefulness of using artificial data for NLP model development Our study is an important first step towards our long-term goal of generating artificial data that: (a) are statistically close to original data and hence useful for NLP development and (b) can be released to the wider research community under appropriate but less strict governance regulations as they should not retain rare or unusual information from the original data that could pose any disclosure risks Our main goals are: (1) test the hypothesis that statistically and clinically valid data could be generated with our proposed approach; (2) test the hypothesis that the generated medical data could be useful for downstream NLP tasks; (3) test whether this generation process could be efficiently controlled by key phrases with the potential to control the risk of rare information seeping through into the generated artificial data It should be emphasised that data we use in this study is already de-identified (defined as removing protected health information (PHI)) the focus is not on de-identification per se it is to try to quantify and assess whether other unusual or rare information from the already de-identified input data leaks into the synthetic data and with that analyse and reason about the potential impact of this for releasing this type of data to the research community with less strict governance procedures Two text classification tasks are studied: diagnosis code and phenotype For the text generation experiments, the dataset is divided into training, validation, and test sets (train-gen-mhr, val-gen-mhr, and test-gen-mhr, respectively). We report the frequency of ICD-10 codes in the test set (Table 1) The final training set (train-gen-mhr) consists of 24,273 patient IDs and 12M tokens; the validation set (val-gen-mhr) consists of 1348 patient IDs and 653K tokens; and the test set (test-gen-mhr) consists of 1349 patient IDs Ten percentage and 20% of test-gen-mhr are randomly selected for the development and test purposes for the text classification task (diagnosis code) All the extracted data is then split into two subsets: train-gen-mimic (9767 patient IDs and 20M tokens) and val-gen-mimic (126 patient IDs The annotated phenotyping dataset (test-gen-mimic The phenotyping dataset was initially collected for MIMIC-II We could not hence reliably identify text fields in MIMIC-III for records with duplicated admission IDs We simply merged those records together giving preferences to annotations with a higher rate of positive labels This resulted in a small reduction of the initial dataset (<1%) 10% and 20% of test-gen-mimic are randomly selected for the development and test sets This results in the three following sets: train-class-mimic we attempt to control the proximity of the generated data to the original data We seek to compensate missing information with the clinical information related to patients and their hospital admissions: patient gender and age timestamp of a record relative to admission date and the ordinal number of a sentence in a section we test the following three experimental setups: (a) artificial text generation using all the extracted key phrases (all) (b) using a set of best-scored key phrases plus clinical information (top+meta) and (c) using the one best-scored key phrase per sentence plus the clinical information (one+meta) we take all of the extracted key phrases (key reproducing the inputs instead of generating outputs) This baseline represents the worst possible generation model that copies the input without generating any context to it and shift of a word) required to change a generated sentence so that it exactly matches a genuine one ROUGE-L measuring recall and BLEU measuring precision are complementary whereas TER gives an idea of the amount of changes performed to the real text Table 2 reports the results of the intrinsic evaluation for both generation test sets the closer the generated text is to the original one: all provides the closest results (av which means that the risk of these generated sentences losing important information is higher The most promising results are obtained from the top+meta model This model results in longer sentences compared to the other models while still retaining the most balanced scores across the other metrics Y-axis—respective cumulative frequencies of the test-gen-mhr sentences As we are interested in keeping only the key meaning of the original text and modifying its context we focus on the text generated by top+meta and one+meta According to the standard procedures our generation models are trained to recreate real data there is thus an actual risk that the models will overfit and produce disclosing patient information in their outputs we have left the identification and masking of PHI cases (e.g. names and addresses) to be handled in the preprocessing The data we work with were already de-identified using bespoke the least frequent words were removed to not be included in the model vocabulary also indirect references to some rare events “the accident was widely reported in the press” in the generated text could potentially identify a patient the focus is on trying to assess the risk of such unusual or rare information from the already de-identified input data leaking into the synthetic data For the best-performing top+meta train-gen-mhr model we assess how well our model memorises the training data Inspired by Carlini et al.16 we regenerate sentences from the training data that contain rare (lower frequency quartile) n-grams We experiment with 2-grams and 3-grams as the average length of extracted key phrases as well as with 5-gram (as longer text spans) we randomly select 1K unique sentences with a rare n-gram each we also randomly select 1K unique sentences with a high-frequency n-gram each (upper frequency quartile) Note that 1-grams with frequency 1 were already excluded from the training data to limit the vocabulary size We first analyse how many of those selected n-grams are already extracted as key phrases in the input (%, in). Table 3 shows that low-frequency n-grams are extracted as key phrases more often than high-frequency n-grams: e.g. 16% of all the high-frequency 2-grams and 40% of all the low-frequency 2-grams both low-frequency and high-frequency 2-grams are equally present (48% for both cases) This means that only 8% of the low-frequency 2-grams are memorised and restored in the output while 32% of the high-frequency 2-grams is restored in the output we consider there is a low risk that our model reveals identifiable information The main proportion of rare information is provided with the input key phrases and can be controlled pretrained classifiers) to model inputs that would detect rare key phrases Also not many n-grams could be potential identifiers ~20% of tokens in rare 2-grams restored in the output are stopwords punctuation or numerical values that could be filtered out even with a rule-based procedure and makes no sense from the clinical point of view These are further grouped into four more generic categories: SAME Annotations were carried out by Joyce Kam (annotator 1) for test-gen-mhr for both top+meta and one+meta The students were provided with a file per discharge summary containing parallel genuine and generated text For each document, we defined A1 as the first annotator and A2 as the second annotator. Each cell in the matrix represents the number of sentences marked by an annotator with a certain category (as defined in Table 4) the quality of the generated text was in general high as compared to their expectations the annotation task was considered challenging for some specific cases (e.g. long sentences that were partially incomprehensible we report two examples of generated sentences (all paraphrased) with disagreement on annotation categories the artificial sentence includes a nonsensical fact; however the main symptom is retained (“NO SENSE” vs it seems as though one symptom is introduced in the artificial sentence; however the wording is not too far from the original one (“BAD/IRR” vs showing a significant improvement over LDA which in turn significantly outperforms BoW (CNN F1-scoreav = 0.48 Artificial data from our top+meta and one+meta methods are useful for our chosen downstream NLP tasks and manage to maintain model performance differences Similar tendencies are observed for all the three models in spite of their intrinsic differences: BoW is focused on n-gram counts LDA is topic oriented with the focus on keywords and CNN combines the adjacent distributed representations of words to analyse concepts the all setup provides the results closest to the original the key baseline (only all the key phrases without text generation) performs poorly for two models out of three: LDA and CNN it even distorts the results: key LDA outperforms key CNN (ΔF1-scoreav = 0.05) whereas CNN outperforms BoW for the real data in most cases our generation methods manage to capture useful information for downstream NLP tasks where only ∼31% of original words per sentence is used performs consistently well for two out of three models (LDA and CNN) We also analyse errors of the best-performing genuine and top+meta CNNs we focus on the “bad errors” of both models: false negatives (FNs) and false positives (FPs) where the model has high confidence in the wrong result The majority of “bad errors” are due to FPs (420 and 256 respectively) rather than FNs (110 and 127 42–45% of the genuine errors are found in top+meta as well while the number of FPs was higher for top+meta the genuine model resulted in a slightly higher number of FNs the genuine model had a slightly higher recall than precision while the top+meta model showed comparable values the top+meta CNN reflects the behaviour of the genuine CNN also when looking at the different diagnoses and even potentially improves it by slightly reducing its FN count As a sanity check, we perform a series of ablation experiments to verify if the real key phrases in the artificial data influence the classifiers. Again for the top+meta setup, we remove the common key phrases from both the genuine and artificial data, and compare the performance of our classifiers. We focus on the best-performing LDA and CNN. Table 7 We again observe comparable performances for the genuine and artificial models This confirms that our artificial data captures relevant information Finally, Table 8 shows our text classification results for test-gen-mimic CNN is again the best-performing model showing a significant improvement over BoW which significantly outperforms LDA (CNN F1-scoreav = 0.46 Artificial data from top+meta and one+meta again manage to correctly reveal performance differences between models top+meta has the optimal performance for BoW and CNN Both top+meta and genuine samples have relatively high probabilities to belong to the same distribution with p-values of 0.28 and 0.13 for BoW and CNN We present an approach to generate clinical documents (EHR discharge summaries) To maintain semantic coherence at a paragraph level the sentence by sentence generation is guided by key phrases Different configurations of the amount of key phrases are applied to investigate how much of the original data is needed to generate useful artificial data We demonstrate the validity of our approach on two EHR datasets: on discharge summaries from a large MHR system and discharge summaries from an intensive care unit MHR notes are particularly challenging as they contain more complex narratives and this type of clinical documentation tends to rely less on structured coding An extensive intrinsic evaluation shows that the top+meta model which uses very little information from the original text This is promising in terms of assessing the risk of these models retaining information from the original data that should ideally be rephrased to ensure that the artificial data minimises any traces of the original data The clinical validity is at the same time to a large extent preserved an extrinsic evaluation is performed in downstream NLP text classification tasks with two datasets: diagnosis code and phenotype classification Using the artificial data as training data leads to comparable results as to those obtained from using the original data We have created a light-weight solution that any holder of clinical data could apply in order to generate synthetic data to outsource NLP algorithm development Clinical institutions do not often have the internal expertise for NLP development and getting the appropriate authorisation to allow this data to be accessed by external organisations is often time-consuming That is why we show that comparison of NLP models trained with synthetic data holds for real data The main purpose is to speed up the external NLP development process with some kind of proxy of real data and get a fair model faster while still adhering to governance procedures in using clinical data these best NLP models should be rebuilt with real data and properly tested We demonstrate that our methodology is not prone to overfitting and the data it generates can easily be shaped by the input selection This means that the sensitive information in the original training data can be efficiently protected Our findings have important implications for our long-term goal to generate artificial data that can be released to the wider research community we have investigated only one downstream NLP task a more universal approach to generate data for other clinical NLP tasks (e.g. information extraction or temporal modelling) is needed this text generation approach might not be optimal other types of clinical use cases might require multiple documents per patient; how to address longitudinal coherence would need further analysis Assessing clinical validity for other tasks might also require defining the human evaluation task slightly differently Having ways of generating artificial clinical data from already PHI de-identified original data that further alleviates the risk of containing any sensitive information could have a huge impact on the development of novel NLP and other data science approaches for analysing EHR data particularly by making data more widely available to the research community could have significant impact in using retrospective secondary healthcare data for translational research that can be used to improve quality of care for patients there are no agreed-upon metrics and thresholds to use for assessing the risk of revealing identifiable information from free-text data there are also very few studies that provide an evaluation of how realistic artificially generated data are and the impact of this for downstream tasks Our study is a first step in addressing these issues and we will further evaluate and analyse these questions initially by organising a workshop with service users as well as information governance practitioners in healthcare services The de-identified CRIS database has received ethical approval for secondary analysis: Oxford REC C The data are used in an entirely anonymised and data-secure format and therefore does not require informed consent from patients whose data are represented here patients are routinely informed of the data resource and have the opportunity to opt out (taken up by four people to date) CRIS data is made available to approved researchers working on approved projects Projects are approved by the CRIS Oversight Committee a body setup by and reporting to the SLaM Caldicott Guardian Researchers are approved by application to SLaM NHS Trust The study protocol presented here is CRIS approved project reference number 18-103 (“Towards Shareable Data in Clinical Natural Language Processing: Generating Synthetic Electronic Health Records”) No further approvals were required for work on this nature The study has been carried out in accordance with relevant guidelines and regulations for the MIMIC-III data These key phrases are sense-bearing elements: using them as guidance ensures semantic integrity and relevance of the generated text We extract key phrases at the paragraph level match them at the sentence level and further use them as inputs into our generation model each paragraph is generated sentence by sentence (standard practice in text generation) but taking the information ensuring its integrity into account the model fills related textual context around given key phrases We train our models for the gap-filling task. In the input, we have the clinical information and the key phrases and in the output, we have the full original EHR record. For example, a training example: input “F20 F 23 female allergies” -> “A female in her twenties has allergies” (Fig. 1) The model is trained to restore the text highlighted in bold reuse the provided test set and cast the task as a binary classification task Considering the random initialisation of parameters in our models for each experiment we retrain the model five times to increase reliability of our estimations and CNN) are chosen as three state-of-the-art models at different stages of the development of NLP in the order of the typical performance improvement we consider the data points from each five runs of each model for binary prediction training as a single sample Further information on research design is available in the Nature Research Reporting Summary linked to this article but protected data (CRIS data are protected under a governance model Thus our trained models can not be made publicly available online There are multiple implementations of the Transformer model publicly available thus the procedure to create a model is straightforward Data extraction and preprocessing scripts are publicly available online: https://github.com/KCL-Health-NLP/artificialMHR. Our preprocessing pipeline, including sentence detection uses the spaCy toolkit (https://spacy.io/ We discard all the sentences with length under five words We replace all the out-of-vocabulary words and words with frequency 1 with the UNK placeholder All other parameters are those provided by default Experiments with the CRIS data are performed on an Azure Tesla GK210 GPU (NC6 configuration with the Tesla K80 Accelerator) experiments with the MIMIC data – on a GeForce GTX 1070 8Gb GPU Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions PixelVAE: A latent variable model for natural images In Proceedings of International Conference on Learning Representations (ICLR) (2016) On the automatic generation of medical imaging reports In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) Liu, P. J. Learning to write notes in electronic health records. Preprint at CoRR https://arxiv.org/abs/1808.02622 (2018) Natural language generation for electronic health records a freely accessible critical care database Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives Automated de-identification of free-text medical records ROUGE: A package for automatic evaluation of summaries In Proceedings of ACL workshop on Text Summarization Branches Out (2004) BLEU: a method for automatic evaluation of machine translation In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics A study of translation edit rate with targeted human annotation In Proceedings of Association for Machine Translation in the Americas The Secret Sharer: Measuring unintended neural network memorization & extracting secrets In Proceedings of the 28th USENIX Security Symposium Kim, Y. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1746–1751, https://doi.org/10.3115/v1/D14-1181 (2014) An empirical investigation of statistical significance in NLP In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning C-sanitized: a privacy model for document redaction and sanitization t-Plausibility: Generalizing words to desensitize text Sequence to sequence learning with neural networks Neural machine translation by jointly learning to align and translate In Proceedings of International Conference on Learning Representations (ICLR) (2015) In Proceedings of the First Workshop on Storytelling Rose, S., Engel, D., Cramer, N. & Cowley, W. Automatic keyword extraction from individual documents. In Berry, M. & Kogan, J. (eds) Text Mining: Applications and Theory, 1–20, https://doi.org/10.1002/9780470689646.ch1 (2010) OpenNMT: Open-source toolkit for neural machine translation Software framework for topic modelling with large corpora In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks In Proceedings of the Annual Meeting of the Association for Computational Linguistics: Interactive poster and demonstration sessions (ACL) Distributed representations of words and phrases and their compositionality Download references This work was partly funded by EPSRC Healtex Feasibility Funding (grant EP/N027280/1): “Towards Shareable Data in Clinical Natural Language Processing: Generating Synthetic Electronic Health Records” and S.Velupillai are part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London The views expressed are those of the authors and not necessarily those of the NHS an initiative funded by UK Research and Innovation Department of Health and Social Care (England) and the devolved administrations S.Velupillai has received support from the Swedish Research Council(2015-00359)/the Marie Skłodowska Curie Actions is funded by a National Institute for Health Research Post Doctoral Fellowship award (grant number PDF-2017-10-029) R.N.C.’s research is funded by the Medical Research Council (MC_PC_17213) These authors contributed equally: Joyce Kam Cambridgeshire and Peterborough NHS Foundation Trust South London and Maudsley NHS Foundation Trust All the authors substantially contributed to the design of work They have also helped in resolving related questions The authors declare no competing interests Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Download citation DOI: https://doi.org/10.1038/s41746-020-0267-x Anyone you share the following link with will be able to read this content: a shareable link is not currently available for this article Sign up for the Nature Briefing newsletter — what matters in science Baller Brown attended Boyle County High School in Danville Wr Brown played football at the University of Kentucky under head coach Hal Mumme and his assistants before transferring to the University of Massachusetts Amherst He did earn all-S.E.C.edu honors as a Wildcat Student-athlete Brown earned his bachelor’s degree in business management and his master’s in business administration from Massachusetts he was an Atlantic 10 All-Academic honoree and an NCAA Division I-AA Athletic Directors’ Academic All-Star team selection After that baller Brown went Coach Brown started his coaching career as a graduate assistant at UMass Then Brown caught his big break as Brown was named the offensive coordinator at Texas Tech under new Red Raiders head coach Tommy Tuberville Then after a year as O’cord at Kentucky Coach Brown blew his first big whistle down at Troy At Troy Coach Brown tallied Troy’s first 10-win season since joining FBS in 2001 in 2016 He scored a massive upset over all-galaxy L.S.U Making him something of their mini-me Frank Beamer And has acquired something of a program-wide renaissance man’s tag for it to his sizeable credit Coach Brown is perfected in his bowl career And he has been the betting ‘dog every time less once This tells you the man can break a tape and really preps shrewdly on extra time Even mo’ curiously… to be rumored to be an offensive-whisper D’s have basically sparked their respective Os at each tour stop What with… in seven combined seasons as a head coach the sewing circle vibe is that this is that rarefied analytics/numbers guy (same as ohhh Fu’); who actually (still) gets the human element We shall see on that… as 2o22 is the proving ground of that As the coaching sewing circles whispers also say that Coach Brown was suffering from some hard(er) to manage Holgorsen kids who did not buy in and have been left out 2021 record: 6 up 7 down and 4-5 in the Big-8 IF/when Daniels finds his true mid-range+++ passing depth Tritto the oLine gelling— as it is nearly umpossible to key or takeaway 3 or 4 different guys at the very same Anywho… said to be a pretty legit hoopster down under; so he must have some athlete in him somewhere Oliver kicks with either foot and can punt spiral or end-over-end rugby style The CCW part (counterclockwise) is tricky the first few times you try to handle it Same as a Koufax passer; everything is bassackwards Such can handcuff or muff a punt for you just ’cause… Oliver is a twisty or dual Aussie and Uncle Sam citizen Did come up in Australia Rules Football and then tutored his American leg game over at: Prokick Australia this may not be your typical fondue parting Tupperware hosting kinda Kicker Big 12 Commissioner’s Honor Roll is quality footsie This Straw stirs the drink with a long of 48 collegiate booting yards so far To early to seriously gauge his futball leg strength Tho’ reports say he has mo’ in the bag and just rolls a 3-wood here-n-there he swoons that signature “good day” Paul Hogan accent As this seems not less than a good get to me Time=tell what Ollie is/is not beyond that Casey Legg -great surname- is w.V.u.’s r-Jr. He is serviceable thus far as a K1 as his career 69% F.G.A Legg has a college career-long of 51-yards; though his reasonable range seems to hover in the mid-40’s give/take Legg only has one make ≥45-yards career-to-date Although he is a forty on campus what with being Academic All-Big 12 First Team Casey was WVa.’s kick-off guy until this year and he did close strong after a modest FG-Kicking start to last year maybe there is mo’ in the bag here than his statistics connote so you need to mind the F.G.A./P.A.T.-block punch-point accordingly Casey does enjoy 3o4 State Championship bling in the other football {sic: soccer} And he was his team’s leading hoops scorer as a scholastic Jr there has gotta be some athletics for a fake somewhere in the mix here Enjoys ‘dancing’… whatever that encodes to mean… Casey Legg is perfected so far in this campaign and might even be a dang good K1 if he has found a new ceiling or level View Results As the team you all love to hate is ah coming to town— and make no misQ and it once was a truly historic rivalry as any possible metric of intensity itself went when it left off (and shoulda stayed left off) up in Mo-town As these two do not exactly like each other or get along loaded for bear breech barrels or a grizzly House of Dragons breach birth here we come https://www.facebook.com/100000450038680/videos/2036247063246554/ In cases of canceling effect(s) or strength on strength crime #ChallangeA.c.c.epted… there are 1,440 minutes in a day and it will take every single one of them -or not less than sixty- to secure a VicTory dance There literally is NO margin of error for Thursday nite West Virginia was forced to undergo a defensive overhaul this offseason due to several transfers Of the team’s seven defensive players that played the most snaps from last season four entered the portal and joined another Power 5 program including its 2021 leader in pressures (interior defensive lineman Akheem Mesidor defensive stops (off-ball linebacker Josh Chandler-Semedo 45) and plays on the ball (cornerback Daryl Porter Jr. the Mountaineers saw their three highest-graded wide receivers from last season also transfer to a new program coming soon… semi… sorta… the so-called: Forum Guide of Graham Houston fame is merely… see: above memories here… now boiled down to the single one sportlight… pay attention men… Regarding these two combined and downright squirrelly looking 3-3 clubs… is up a near astonishing +547 Σ or total yards | with 2-1 VeeTee up a surprising +5o4 Σ or total yards in seasonal terms Meaning: both are better than most observers think; although has been more quixotic or just excessively inconsistent in both aerial and in grounded terms alike our last big whistle told all of you that we did not play well when we got too up to play a rival tho’ this is not how you facedown a rival in heat) Now ^^^this^^^ is where I’ma hoping our new big whistle pegs this one deeply… we prolly need at least a little measure of w.V.u we gotta play one helluva a clean game and clean nearly everything single thing up S getting our Top-2 Rb’s back right as rain would not injure our efforting whatsoever That being fairly said… it is clear to me that the one way to upset w.V.u (While hoping that Daniels does not go seam-splitter and denude us and our (Hokie)-pokey recovery speed secondary medium to long) and the greater chunk-play threat from archrival WVa all 3o4 middle fingers point to… that And an errthymeme is what you will be getting an earful of for ~4 hours or 4Life Let us just hope it is not an O&M err-ache come midnite Thursday I can’t tell if you mean Vice’s leftovers need to do the job or not Left hope Rudolf’s Road Graders come to life …how do yah’ll see all these things We due to get some and we due to lose some. Cause I want Coach pry’s team to bust some eeer ass tonight is it possible on this Thursday night to recall similar times when VT smoked the opposition and on national TV no less The crowd will be there with memories of yore A great kitchen is more than just expensive finishes and coveted hardware stylish touches and a well-considered layout Whether designing a kitchen or giving it a reboot, you don’t need to break the bank for a five-star space. Designer Martine Cooper says no matter what your budget is your workflow and who uses the space,” she says Want a relaxing modern kitchen that is good-looking and functional Read on for hot trends and budget-friendly tips for creating a dream space with true staying power was in awe of Kyle and Leslie’s organised kitchen “Storage galore and drawers for days,” she marvelled She was equally impressed with Kristy and Brett’s tandem pantry unit and James Bond-style rise-and-fall splashback concealing secret storage space there are more inexpensive ways to incorporate storage from drawer dividers and pretty canisters to shelf risers that allow you to stack to the height of the shelf “Keeping pantry shelves shallow brings everything to the front and keeps items stacked two to three deep so they don’t disappear,” says designer Angie Rogers “Ensure there are plenty of drawers that separate goods into sections so you can pull out the whole drawer and look down onto its contents.” Designer Sarah Elshaug suggests installing a system into tricky corner units to ensure all space is utilised. “The Le Mans system is the best solution, and store-bought lazy Susans are great for smaller corner cupboards,” she says.  The best ways to add colour to your kitchen The Block 2023 kitchens : Did the contestants get it right? Do you need a butler's pantry in a kitchen? Make good use of all available “vertical real estate”, Cooper says. “The splashback and side of cabinetry is great for knives and spices and frees up drawer space,” she says. “Use a non-permanent kitchen trolley as an additional benchtop with storage underneath.” Great styling is vital when creating a beautiful space. A case in point is Kristy and Brett’s kitchen stools swathed in luxe boucle, and Steph and Gian’s seamless mesh of Scandinavian and Japanese styles. Custom colours also feature heavily, with Eliza and Liberty’s vibrant orange oven and Leah and Ash’s hot-pink powder-coated coffee machine both standouts. “You have to be brave!” Cooper says of the vibrant trend. “Personal taste evolves, so you may not want to be tied into a red mixer tap or coffee machine. Try smaller items like coffee cups, planters and vases in your preferred colour to ensure you are ready to commit to being bold. It’s a fun way to add some personality without breaking the bank.” Rogers says beautiful design detail also elevates a kitchen. “Choose big pendant lights to create a focal point and wallpaper your pantry,” she suggests. “A splashback is a smaller surface area, so spend your budget on beautiful handmade marble tiles.” You can save money on cabinetry finishes without sacrificing style. “A 2pac paint finish on door and drawer fronts is nice, but laminate is more affordable with many gloss levels, timber texture and colours available,” Rogers says. “Stone benches vary considerably, so shop for cheaper offcuts with your stone mason that might work.” Make your kitchen a sensory place to spend time in, splurging on extras with longevity. “Invest in quality cabinetry handles, tapware and benchtops, along with soft-close hinges and drawer runners,” Rogers suggests. “You’ll touch and feel them daily, so you want them to feel good and last for years.” There were butler’s pantries galore on The Block this season, with Steph and Gian’s declared “exquisite” by judge Neale Whittaker. Now, more than just a place to leave functionality out of sight, the butler’s pantry is a luxe overflow space to be coveted. “Create a complementary colour and materials palette but down-spec materials, using a laminate benchtop with a stone-look finish,” Cooper suggests. In a plain space, paper the walls or add vibrant colour to the insides of cabinetry, and choose hardware that acts like jewellery. “Create plenty of storage affordably using modular carcasses from IKEA that come in set sizes and are flat packed,” Elshaug says. “Open-carcass drawers and shelves are a great option too, and less expensive than decorative cabinetry fronts.” Whittaker was thrilled with Kristy and Brett’s fresh, pristine space and said it was “refreshing to see an all-white kitchen”. Regarded as a safe colour scheme for the better part of the last two decades, now we see the sleek white kitchen emerge as a space to shine. Luckily, creating an all-white kitchen is also very affordable. “Add paint, crisp blinds, a freshly tiled splashback or island bench, or a new laminate benchtop,” Rogers suggests. “Apply VJ panelling over existing walls and update furniture or feature lighting. To ensure it doesn’t look stark, style it with warmer materials like brass, timber, rattan, handmade tiles and jute.” Check out exclusive content weekly, including floor plans, expert renovation advice and property insights. The information on this website is intended to be of a general nature only and doesn't consider your objectives, financial situation or needs. where we are privileged to live and operate Home With the new year arrival comes new opportunities The Month of January is full of competitive recruitment examination one being the IBPS Specialist Officer Mains Exam Specialist Officer works in coordination with other banking officials to to assist them in areas like Information Technology (IT) Information Technology (IT) emerges as the framer of various sector If you are preparing for IBPS SO Main IT Officer Professional Knowledge you are the right place as this article will help students who want to pursue their career as a IT Specialist Officer in Public Sector Bank IBPS SO Mains Exam is be held on 25 January 2020 This is the high time to start working for the Mains stage The mains examination of IBPS SO demands professional knowledge of the field Here is a pattern for the IBPS SO Law Officer: IBPS SO Mains Exam is extremely different from the IBPS SO Prelims Exam knowledge of your field is tested so that they can pick the eligible candidate for working in coordination with the other bank officials The merit list of Specialist Officer is framed in accordance with the mains and interview stage of the IBPS SO Recruitment candidates after appearing for the mains exam should start focusing on the Mains stage to get their name in the merit list It is important for an aspirant to understand the nature of the examination before making a strategy for the same Here are few topics that are to be prepared for the IBPS SO IT Officer Professional Knowledge: DBMS (Database Management System) is one of the most important topic Solve as many theoretical questions as possible from different mock test and previous year’s paper so that you don’t have to go with the last minute hussle You need to read these three topics thoroughly from the following books: Work hard and do not let anything else comes in your way Choosing the right way is the need of the hour Cosmopolitan Middle East Home » Life » These wedding venues in Lebanon are so main character coded Imagine your big day at one of these stunning spots 💋✨ 2024 is turning out to be THE year of viral weddings and at the forefront of the action are Lebanon’s finest venues As if straight out of a fairytale, these spots in Lebanon are almost just as stunning as the brides that grace them (almost). 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