dataesr/scientific-paragraphs-categorization huggingface dataset:
Socio-demographic and anthropometric data Socio-demographic and anthropometric data were collected from every participant's medical record using a collection grid specially designed for the study and including: • The patient's age. • Family structure. • The type of anorexia nervosa (restricting type or binge-eating/purging type). • The prepubescent or pubescent form. • The age of the patient at the onset of the disorder. • Possible history of hospital admissions. • Potential co-morbidities. These data were collected to gain a better understanding of sample characteristics and to serve for comparisons. Other data concerning hospitalisation and care were also collected: • Weight, height and Body Mass Index (BMI) on admission to hospital, on the day lockdown was announced and one month and two months after the start of lockdown measures.
Data analysis All the interviews were audio-recorded, pseudonymised and transcribed verbatim in French by FL. The data were analysed by inductive thematic analysis  [19]. The authors chose this method because it produced reliable and valid findings through a straightforward and systematic set of procedures  [19]. The themes are strongly linked to the data because they emerged from the data per se without trying to match the data to a pre-existing theory or framework (even if the authors cannot ignore their theoretical knowledge). PS and SB read the interview transcripts several times to familiarise themselves with the data. The data were then analysed to identify and define different themes. This analysis was conducted separately to maximise the validity of the results. Finally, SB, PS, ML and FL discussed their preliminary results.
Study population Six patients and their parents were invited to take part in the study. They all agreed to participate, resulting in a total of eighteen semi-structured interviews. Four of the six patients went home on the day the lockdown measures were announced. One patient went home three days later once her parents were available (patient 3). The last child remained in hospital due to a clinical condition incompatible with discharge (patient 4). The participants' sociodemographic and medical data are presented in Table  1. Changes in the patients' BMI are shown in Table  2. All the patients presented restrictive anorexia nervosa at the time of inclusion. Only one patient (patient 6) was readmitted to hospital a few weeks after lockdown. One of the patients (patient 3) was not routinely weighed during the weekly follow-up interviews because the weighing procedure caused her too much anxiety.
" That reinforces the idea that a shorter hospital stay could be beneficial for some patients. One of the strengths of this study was the inclusion of the parents, thereby facilitating a hetero-assessment of patients' experiences that could overcome alexithymia often observed in anorexia nervosa patients  [51, 52]. In addition, interviews were conducted by FL in order to avoid desirability bias as patients and their family were usually followed up by ML. To our knowledge, this study is the only one of its kind in France to adopt a qualitative approach to investigating patient/parent experiences. However, there are several limitations in this study. Firstly, the sample size was limited. Nevertheless, data saturation may have been reached as no new element emerged in the interviews with the children and parents at the end of analysis.
Evidence before this study In a systematic review until December 2019, we showed that studies of machine learning in subtyping and risk prediction in cardiovascular diseases are limited by small population size, relatively few factors and poor generalisability of findings due to lack of external validation. We further searched PubMed, medRxiv, bioRxiv, arXiv, for relevant peer-reviewed articles and preprints, focusing on machine learning studies in heart failure. Studies remain focused on single diseases, limited risk factors, often single method of machine learning, rarely use subtyping and risk prediction together, and have not been externally validated across datasets. For heart failure, all subtype discovery studies have identified subtypes based on clustering, but so far with no application to clinical practice.
Added value of this study Across two independent, population-based datasets, we used four machine learning methods for subtyping and risk prediction with 89 aetiologic factors as well as 556 further factors for heart failure. We identified and validated five subtypes in incident heart failure, which differentially predicted outcomes. In addition, we externally validated clinical cluster differences by exploring corresponding genetic differences in a large-scale genetic cohort. Our methods and results highlight potential value of electronic health records and machine learning in understanding disease subtypes. Moreover, our approach to external, prognostic, and genetic validity provides a framework for validation of machine learning approaches for disease subtype discovery.
Methods We analysed individuals ≥30 years with incident HF from two population-based electronic health records resources (1998-2018; Clinical Practice Research Datalink, CPRD: n=188,799 HF cases; The Health Improvement Network, THIN: n=124,263 HF cases). Preand post-HF factors (n=645) included demography, history, examination, blood laboratory values and medications. We identified subtypes using four unsupervised ML methods (Kmeans, hierarchical, K-Medoids and mixture model clustering) with 87 (from 645) factors in each dataset. We evaluated subtypes for: (i) external validity (across independent datasets); (ii) prognostic validity (predictive accuracy for 1-year mortality); and (iii) uniquely, genetic validity (in UK Biobank; n=9573 cases): association with polygenic risk score (PRS) for 11 HF related traits, and direct association with 12 reported HF single nucleotide polymorphisms (SNPs).
Findings After identifying five clusters, we labelled HF subtypes: 1. Early-onset, 2. Late-onset, 3. AFrelated, 4. Metabolic, and 5. Cardiometabolic. External validity: Subtypes were similar across datasets (c-statistic: 0. 94, 0. 80, 0. 79, 0. 83, 0. 92 for the THIN model in CPRD and 0. 79, 0. 92, 0. 90, 0. 89, 0. 92 for the CPRD model in THIN for subtypes 1-5, respectively). Prognostic validity: One-year all-cause mortality, risk of non-fatal cardiovascular diseases and all-cause hospitalisation (before and after HF diagnosis) differed across subtypes in CPRD and THIN data. Genetic validity: The AF-related subtype showed associations with PRS for related traits. Late-onset and Cardiometabolic subtypes were most comparable and strongly associated with PRS for Hypertension, Myocardial Infarction and Obesity (p-value < 9.
Interpretation Across four methods and three datasets, and including genetic data, in the largest HF study to-date, ML algorithms identified five subtypes in individuals with incident HF. These subtypes may inform aetiologic research, clinical risk prediction and the design of HF trials. 3 ), despite causal associations  (4, 5). Current subtype classifications, including by aetiology (e. g. ischaemic vs non-ischaemic), pathophysiology (e. g. primary myocardial disease vs secondary neurohormonal activation), anatomy (e. g. left-vs right-sided(  6 )), haemodynamics (hypoperfusion vs congestion), presentation (e. g. acute vs chronic(6, 7)), setting (e. g. outpatient vs inpatient), left ventricular ejection fraction, LVEF (e. g. reduced vs mid-range or mildly reduced vs preserved(1)), symptoms (e.
g. New York Heart Association class  1-4(8)  or American Heart Association HF stage A-D(  9 )), comorbidities (e. g. end-stage renal disease  (10) ) or biomarkers (e. g. NT-proBNP (N-terminus-pro-brain natriuretic peptide) (9)), have not led to "precision medicine," "personalised care," or targeted therapies. Incomplete knowledge of subtypes across the whole range of aetiologic factors and population has also limited primary prevention and screening guidelines for HF  (10, 11). In clinical practice and research, subtypes are commonly classified by LVEF for diagnosis and prognosis, but in a recent machine learning (ML) study, EF did not predict survival in a large HF registry  (12). ML is rarely used to identify subtypes in large, nationally representative datasets linked across healthcare settings (i.
e. primary and secondary care), where "agnostic", unsupervised subtype discovery across risk factors may inform HF treatment and prevention. Moreover, studies to improve HF subtype classification and risk prediction have neither compared different ML methods in one study nor validated ML-based subtypes in a separate population, with limited studies of risk prediction. Our seven-stage framework (clinical relevance, patients, algorithm, internal validation, external validation, clinical utility, effectiveness) for practical ML implementation may yield more clinically relevant results  (13). Therefore, in a population of 322,635 individuals with incident HF and 645 factors across three population-based datasets, we used four unsupervised ML methods to: (i) Generate subtypes with clinical relevance throughout the course of HF, and low risk of bias for patient selection and algorithms ("Development").
Methods We used our published framework for ML implementation to inform our methods  (13). (  THIN: 10-fold), one fold was selected to represent the whole population in each dataset. PCA reduced noise, variance and dimensionality. We determined optimal number of clusters (silhouette width, prediction strength(  22 )) and ML method (similarity indices and matrices, and cluster stability: Rand, Jaccard means and Fowlkes and Mallows' indices(  23 ))(Web.
Discussion To our knowledge, this is the first study to define and validate data-driven heart failure disease clusters across multiple ML methods, nationally representative datasets and multiple validation methods. We make three distinct advances. First, we identified five incident HF subtypes: Early-onset; Late-onset; AF-related; Metabolic; and Cardiometabolic. Second, we confirmed internal, external, prognostic and genetic validity. Third, we developed a means of using identified subtypes in clinical practice and suggest ways of evaluating effectiveness. Our five subtypes are compatible with two major clustering studies  (12, 13). The Cardiometabolic subtype could represent ischaemic aetiology(1, 2, 9). For aetiology, guidelines and research, studies have predominantly focused on ischaemic versus nonischaemic HF, and HF defined by cut-offs based on EF  (27).
g. echocardiography. Methodologically, we offer advances in external validation of ML in subtype classification and in risk prediction in HF, which has been rare (only 4/27 and 2/31 studies respectively) and in small samples (n=44 to 3203 for studies of HF subtypes)  (13). Our subtypes showed good accuracy within and across datasets, and good predictive accuracy for Early-onset, Metabolic and Cardiometabolic subtypes, though less accurate for AF-related and Lateonset subtypes. The c-statistic for LVEF, the most commonly used feature to define HF subtype, was only 0. 52 in a large Swedish national registry study using ML  (12). Even after inclusion of more clinical factors (e. g. , echocardiography, NT-pro-BNP) or focus on certain subgroups or clinical scenarios  (31) , improved risk prediction for mortality and other outcomes remains challenging.
Our finding of PRS and SNPs associated with the AF-related subtype are novel, signalling potential utility of assessment for biologic validity of cluster analyses and their linkage to EHR  (26). The mild associations observed with related PRS for the Early onset HF subtype (with the exception of strong association with obesity and atrial arrhythmias), compared to Late-onset and Cardiometabolic subtypes, are of interest. Studies of ML in HF should focus on further validation in representative datasets from other countries, disease definition and use of high-dimensionality proteomics and imaging data. Recent guidelines describe the need for systematic approaches to design, evaluation and implementation of ML in healthcare  (13, 32, 33). We address issues at the development and validation stages to use of ML for subtype classification and risk prediction in HF.
This is one of the largest EHR analysis to-date to use ML in subtype classification and risk prediction of HF, and for the first time, investigating multiple ML methods, multiple nationally representative datasets and multiple validation methods. By using EHR, our derivation and validation cohorts are representative of real-world patients, increasing confidence that evaluation in clinical practice is worthwhile. We incorporated factors before and after HF diagnosis, enabling insights into trajectory as well as aetiology. However, there are several limitations. First, we are using EHR phenotypes of HF, which do not have complete biochemical (e. g. NT-pro-BNP) and imaging (e. g. LVEF) profiles, and therefore certain previous classifications are not possible (e. g. HF with preserved ejection fraction).
However, NT-pro-BNP and LVEF tend to be more available in secondary and tertiary settings and are commonly not available in unselected patients with new HF, where 26% were never hospitalised  (19). Moreover, our phenotypes have been validated and used in prior largescale studies  (19). Second, although we used 645 factors, risk factor phenotypes are limited by timing and accuracy of clinician recording in the EHR, which may affect analyses of factors before and after HF. Third, although we use two large, nationally representative primary care datasets, they are both from the UK and may not be representative of HF in other countries or settings. Fourth, we performed only supervised analyses of PRS of 11 traits related to HF and 12 SNPs previously associated with HF (and numbers in the Metabolic cluster were small), necessitating further genetic analysis in larger cohorts.
Conclusions Across three large, population-scale datasets, four machine learning methods, 645 factors, and four validation methods, we identify five heart failure subtypes with good discriminatory accuracy within and across datasets, and good predictive accuracy for 1-year mortality. These subtypes may have implications for research in terms of use of EHR and ML to identify HF subtypes in future clinical trials and observational studies, as well as clinical practice in terms of management and prognosis. A l l -c a u s e m o r t a l i t y a t 1 y e a r.
Acknowledgments HH is supported by  Health Data Research UK  (grant No. LOND1), which is funded by the  UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England) ,  Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government) ,  Public Health Agency (Northern Ireland) ,  British Heart Foundation , and  Wellcome Trust. HH is a  NIHR  Senior Investigator. AB, SD, FA and HH are funded by the  NIHR University College London Hospitals Biomedical Research Centre. All authors are supported by the  BigData@Heart Consortium , funded by the  Innovative Medicines Initiative -2 joint  undertaking under grant agreement no  116074.
coefficient( 21 )(Figure 1 , 211 Web Figure4). Risk of algorithmic bias was minimised by comparing four ML methods: K-means (partitioning, non-parametric), Hierarchical (agglomerative), K-medoids (partitioning and dissimilarity matrix, and non-parametric) and Mixture modelling (parametric). To demonstrate validity (Validation)Internal (within dataset and across methods): After training and validation (CPRD: 15-fold;.
Web Figure 2 and 3)(19). 1) To generate subtypes (Development) Clinical relevance: By aiming to improve diagnostic and prognostic prediction of HF, our research concerned "patient benefit". We used two independent population-based primary care electronic health records (EHRs) with validity for HF and cardiovascular diseases (CVD) research ("target condition applicability": whether the disease defined in data matches research questions). Primary care EHR (The Health Improvement Network, THIN(14) and Clinical Practice Research Datalink, CPRD-GOLD), were linked by CPRD and NHS Digital (using unique national healthcare identifiers), with hospital admissions (Hospital Episodes Statistics, HES), and death registry (Office for National Statistics, ONS)(15). Both datasets are representative of the UK population, with prospective recording and follow-up ("data suitability").
0 (25). We assessed association with 12 HF SNPs(26) by extracting allelic dosages, inverted prior to analysis to reflect HF risk-increasing alleles. To test associations between PRS, HF SNPs (2)   To propose pathways to improve impact (Impact) Clinical utility: We assessed improved outcome prediction and open methods. We asked HF clinicians(n=5) about clinical relevance, justification and interpretability of results. Effectiveness: Validation Internal: The optimal number of clusters was 5. Identified clusters were stable (Rand index, Jaccard means, and Fowlkes and Mallows's indices >0. 8 for all subtypes, using all ML methods except hierarchical clustering). Across datasets, we used similarity matrices to find the most representative algorithm, which was K-means (Web figure 5 ). External: Subtypes were similar across datasets (c-statistic: 0.
Statistical Analyses The population sets are summarized in Supplementary Table  1. The modified full analysis set (mFAS) comprises all patients in the FAS with 1 draining fistulae at baseline (assessed by gentle finger compression). The primary, secondary, and additional endpoints are presented using mFAS. Pharmacokinetic (PK) data are presented using the PK set and mFAS; safety data are presented using the safety set. Unless stated otherwise, data are provided by dosing regimen and pooled over both regimens (VDZ pooled). Where appropriate, variables were further summarized by study visit. For endpoints related to reduction in the number of draining fistulae from baseline, patients with missing data at a study visit were classed as nonresponders at the respective visit. Because enrollment was stopped prematurely, resulting in low patient counts per dosing regimen, all analyses are descriptive.
Discussion In this study, >50% of patients with fistulizing CD treated with vedolizumab (with concurrent seton until week 14 and antibiotics until week 6) had 50% decrease in number of draining perianal fistulae, assessed by gentle finger compression during clinical examination. Clinically relevant reductions in draining fistulae were seen as early as week 2 and maintained through week 30, regardless of dosing regimen. The median time to first closure or to 100% closure appeared different between dosing regimens; however, the datasets were small and 95% CIs wide or incalculable. The proportion of patients responding to treatment is encouraging, indicating vedolizumab efficacy was maintained over 30 weeks of treatment. Comparatively, anti-TNF therapy leads to sustained efficacy in w30% of patients.
Acknowledgments: Thank you to  EHESP (Rennes, France)  and to  IMT Atlantique (Nantes, France)  for allowing the realization of this study. Thanks to  Anne Gérard ,  Hélène Renaud , and  Sophie Payet  for the manipulations performed at  EHESP. Thanks to the technical team of  GEPEA laboratory at IMT Atlantique  for the implementation of the AHU. Acknowledgments: Thank you to  EHESP (Rennes, France)  and to  IMT Atlantique (Nantes, France)  for allowing the realization of this study. Thanks to  Anne Gérard ,  Hélène Renaud , and  Sophie Payet  for the manipulations performed at  EHESP. Thanks to the technical team of  GEPEA laboratory at IMT Atlantique  for the implementation of the AHU.
1] [12]  Two recent large-scale GWASs on fractures at any bone site, including a mixture of fractures at different bone sites and also including less-validated self-reported fractures, have identified 15 fracture loci. 7, 11 All the identified fracture loci were also associated with BMD, supporting the notion that BMD is a shared major risk factor for fractures at different bone sites. Nevertheless, it is likely that the causal mechanisms at least partly differ by fracture location. For instance, vertebral fracture risk depends largely on trabecular BMD, while risk of falls is an important determinant of hip fracture risk. 13 e, herein, hypothesized that bone-site specific fracture loci, also including non-BMD-related loci, exist for hip fractures. To evaluate this hypothesis, we established a large dataset of hip fracture cases using data from five large Northern European biobanks in which high-quality national registers had been used to identify hip fractures according to the International Classification of Diseases (ICD; ICD10 codes S72.
DISCUSSION Although hip fracture is the clinically most important fracture, the genetic architecture and the causal risk factors of hip fractures are not characterized in detail. We, herein, established a large hip fracture dataset and performed a large-scale GWAS metaanalysis on hip fractures, identifying one non-BMD-related genetic hip fracture signal and four signals in loci known to be involved in bone biology. In addition, we demonstrate that the causal clinical risk factors for hip fractures include decreased FN-BMD, Alzheimer's disease, and ever smoked regularly. We also demonstrate that the impact of these causal risk factors differs by age, gender, and fracture site. Finally, we provide causal effect estimates of the effect of FN-BMD on hip fractures, including separate effect estimates by age and gender, supporting the use of change in FN-BMD as an efficient surrogate outcome to estimate effects of BMD-targeting treatments on hip fractures in future clinical trials.
Interestingly, this smoking parameter was directly causally associated with risk of falls but not associated with FN-BMD. In addition to increasing risk of falls, it is possible that smoking may regulate bone strength via effects on bone microstructure or other bone quality parameters, which are not captured by FN-BMD. A causal effect of smoking on hip fracture risk is supported by some previous observational association studies. 28, 29 he strength of this study is the large number of hip fracture cases included, generating the most comprehensive assessment of the genetic determinants of hip fracture risk so far. This well-powered dataset also enabled us to identify and estimate the strength of causal clinical risk factors for hip fractures. In conclusion, this hip fracture GWAS identified one non-BMDrelated and four BMD-related genetic determinants for hip fractures.
ACKNOWLEDGMENTS HUNT: The  Trøndelag Health Study (HUNT)  is a collaboration between  HUNT Research Center (Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology NTNU) ,  Trøndelag County Council, Central Norway Regional Health , and the  Norwegian Institute of Public Health. We thank the population of the County of Trøndelag  and the staff at  HUNT Research Center  for their contribution. UK Biobank:  UK Biobank  was established by the  Wellcome Trust medical charity, Medical Research Council, Department of Health , the  Scottish government , and the  Northwest Regional Development Agency. It has also had funding from the  Welsh government ,  British Heart Foundation ,  Cancer Research UK , and  Diabetes UK. UK Biobank  is supported by the  National Health Service (NHS).