dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The goal of our study is to identify features of narcolepsy and weight their relative contributions to diagnosis from a huge and complex international multicenter database (EU-NN database). We use machine learning because unlike human knowledge and classical statistics, SGB will learn its own rules to classify NT1 and NT2 by taking the entire information of the database into account. In addition, SGB learns to select features independent from existing diagnostic classification and is thus relatively independent from human circular reasoning. Our results suggest that SGB is appropriate to classify narcolepsy subtypes with a high accuracy, and it can consider mixture of clinical features on machine scale and learn to classify NT1 and NT2 by identifying the most important features that are even not part of the current classification criteria such as the mean REM sleep latency of MSLT (which indicates no circular reasoning).
These features are thus potential candidates for future classifications of narcolepsies. For validation reasons we decide to include NT1 although we are well aware that diagnosing NT1 is usually not a diagnostic challenge for clinicians. NT1 is especially suitable for validating our SGB model because all other forms of central hypersomnias are poorly defined and only NT1 is an accepted entity and could thus serve as a referenced "ground truth", which is extremely important and crucial for validating the supervised learning model. In other words, humans can only trust that SGB is a suitable ML algorithm to correctly "learn" the clinical features of narcolepsy from the EU-NN database if it can successfully reveal the "ground truth" (i. e. , classifying NT1 and NT2 correctly). The performance of SGB depends on the characteristics of the database because boosting will fail to find a predictive model or give extremely poor performance if the database is too noisy (i.
e. , the updating of the weak learners may be over-emphasizing noisy examples)  14, 15. Therefore, the extremely high performance of our models under any combination of trees and tree depth (Fig. 1 ) could suggest good data quality of the EU-NN database, which will guarantee solid future analysis on this database. We find very few 'misclassified' predictions given by the model, as described in the Results section. Careful scrutiny of these cases, shows that these misclassifications are due to incorrect data interpretation by the physicians or false data entries instead of incorrect predictions by the model. Our statistical analyses on the other predictors shown in Fig. 2  suggest that patients with NT1 have lower nocturnal PSG sleep efficiency and higher probability of carrying the HLA DQB1*06:02 subtype than patients with NT2, which is consistent with our current knowledge of narcolepsy  16, 17.
Unfortunately our study has also weaknesses. First, the idea of the EU-NN database is to collect a systematic sample of all narcolepsy and patients with hypersomnias in the participating centers. For many reasons not all patients have been included from all centers. Although we assume that our sample gives a representative figure about European narcolepsy patients, a selection bias is possible. In addition, although our SGB model can fit the EU-NN database well with less overfitting (i. e. , it shows good performance in the testing set), the generalization of our model to other narcolepsy databases such as the ones from the United States of America and China is unknown. We will know more in the future when the model is tested in other datasets. Second, in our model the weight of CSF-hypocretin is quite small compared to the values of features of cataplexy.
We are well aware that EDS together with low hypocretin is sufficient to diagnose NT1. Our results do not undervalue the importance of hypocretin in diagnosing NT1. Consistently, narcolepsy patients with hypocretin deficiency but no cataplexy are still predicted as NT1 by our machine. Actually, hypocretin level is ranked as No. 1 in another model built without cataplexy features (see Fig. 4 ). The reason for the low weight of hyprocretin in the presented results is due mainly to absent data on hypocretin. Only 183 out of 598 NT1 patients have a confirmed hypocretin deficiency. Third, the EU-NN database has missing values for key predictors other than hypocretin levels. Missing data is one of the common challenges of multicenter studies dealing with big dataset  18. As mentioned in the Method section, only part of the dataset is mandatory due to feasibility/availability of assessment and sociocultural background among centers and countries.
We check the missing values in other key predictors proposed by our model in Fig. 2  and find that 23. 1% patients have missing value of HLA DQB1*06:02. The main reason of absent HLA DQB1*06:02 is because the test is costly and it is not included in current diagnostic criteria. 18% patients have missing values in PSG. In MSLT 14% patients have missing data for the number of SOREMP and the mean REM sleep latency, while another 28. 5% patients have missing data only for mean REM sleep latency. There are multiple reasons for missing data in non-mandatory datasets such as human errors, dispensable tests for diagnostic purpose (e. g. when low CSF hypocretin levels already determine the diagnosis of NT1) to refusal of patients to perform a test, and variables not included in the current diagnostic criteria (e.
Fourth, we confirm the effect of predicting factors by using classical statistics, e. g. , logistic regression, t-test, Fisher's exact test. As these classical statistical analyses do not allow missing data we are forced to use only subgroups of patients with complete dataset. Since large number of missing data can reduce the weights of the predictors (e. g. , the weight of hypocretin level is smaller than cataplexy features), it is thus reasonable to assume that the relative influences of these predictors are even larger without missing data. Having these limitations in mind we recommend future well-controlled clinical and experimental studies to confirm stability and validity of mean REM sleep latency as a valuable candidate marker for classifying NT1 and NT2, such as evaluating the exact relationship between numbers of SOREMP and REM sleep latency (i.
Conclusion We used a mathematical model based on Stochastic Gradient Boosting (SGB) to find out, which kind of information can be derived from the complex European Narcolepsy Database. Our results show that machine learning approach may be valuable in diagnosing subtypes of narcolepsy and selecting new clinical features of narcolepsy on machine scale. It is important to test the validity and usefulness of our model in other datasets in near future. Future will then show whether clinicians could use modern computerized models in practice to enable more accurate diagnosis -and hence in planning best possible treatments for their patients.
Methods Patients. Only validated patients, i. e. , cases with complete mandatory data carrying an essential amount of information on the disease, are included in the analysis. The EU-NN has published definitions and standardized procedures for data acquisition. The rationale to define mandatory data is to cover essential clinical key aspects of hypersomnias that can be assessed in every center. In contrast non-mandatory data are expected to vary among different sleep centers due to sociocultural background of the population, available equipment or limited resources (e. g. CSF hypocretin levels). Mandatory data contains: In the majority of patients additional non-mandatory data are available and included in the current analysis. Non-mandatory data include sleep and vigilance test parameters from nocturnal polysomnography (PSG), multiple sleep latency test (MSLT), maintenance of wakefulness test (MWT), other vigilance tests, actigraphy, etc.
The new model F 3 is the sum of T1, T2 and T3 and it finally successfully classifies all the symbols. As a preliminary result Model F 3 classifies these symbols quite well as compared to any of the individual trees. Gradient boosting has the risk of overfitting. Overfitting indicates that precise predication can be drawn from the training set but the performance is low when using testing dataset (i. e. , the trained model has poor generalization ability to predict new cases). This can be controlled by several regularization methods, including shrinkage and applying the SGB method. Previous studies find that to multiply a shrinkage factor v (0 < v < 1, which was also called 'learning factor') to the new build decision tree in each iteration step can dramatically improve the model's generalization ability compared to the one without shrinking (i.
For the sake of model complexity and computing time, we exclude children case specific predictors, near zero-variance predictors and the predictors with more than 80% missing values from further analysis. Finally 81 predictors are included to build the classifier. The data are randomly split into training and testing sets with a ratio of 75%:25%. The SGB model is constructed with the caret package in R language  8. The number of trees (i. e. , the number of boosting iterations) and interaction depth (i. e. , the depth of trees) are the two parameters tuned. The numbers of trees are set between 1000 and 4000 with a stepwise increment of 50, and the interaction depth is set to 1, 3, 5, 7 and 9, respectively. The shrinkage factor is fixed as 0. 001. We select 10-fold cross-validation (CV) with 10 times repeats as the resampling scheme to estimate the test error.
The oxygen-18 to oxygen-16 ratio recorded in fossil planktonic foraminifer shells has been used for over 50 years in many geoscience applications. However, different planktonic foraminifer species generally yield distinct signals, as a consequence of their specific living habitats in the water column and along the year. This complexity is usually not taken into account in model-data integration studies. To overcome this shortcoming, we developed the Foraminifers As Modeled Entities (FAME) module. The module predicts the presence or absence of commonly used planktonic foraminifers and their oxygen-18 values. It is only forced by hydrographic data and uses a very limited number of parameters, almost all derived from culture experiments. FAME performance is evaluated using the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) Late Holocene planktonic foraminifer calcite oxygen-18 and abundance datasets.
Reference datasets In an attempt to validate the FAME approach, we apply its methodology to reference datasets, close to present-day observations. The first necessary step is the computation of a reference δ 18 O c field as obtained when forced by climatological data. For seawater temperature, we use the World Ocean Atlas 2013 (WOA13)  (Locarnini et al. , 2013)  data at a monthly resolution. Considering that there is no equivalent seawater oxygen-18 gridded dataset available in the World Ocean Atlas fields and that the existing NASA Goddard Institute for Space Studies (GISS) gridded dataset (LeGrande and  Schmidt, 2006)  presents large deviations with respect to the seawater oxygen-18 (δ 18 O sw ) raw data in numerous locations, we derived a δ 18 O sw dataset based on seawater salinity to δ 18 O sw relationships.
This dataset is built in two steps: (a) derivation of regional δ 18 O sw -salinity relationships from GISS δ 18 O sw and salinity  (Schmidt et al. , 1999)  clustered by oceanic regions, and (b) computation of a δ 18 O sw field based on the World Ocean Atlas 2013  (Zweng et al. , 2013)  salinity fields. The resulting field is at the World Ocean Atlas spatial resolution and is used as reference seawater oxygen-18 in the following. Details on the derivation of the δ 18 O sw dataset are given in Appendix A. As an independent test of the FAME results, we use the planktonic δ 18 O c measurements from the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) Late Holocene dataset  (Waelbroeck et al. , 2005)  restricted to high chronozone quality levels (i. e. , levels 1 to 4).
A few errors have been corrected in the published dataset: these concern the suppression of 10 Neogloboquadrina incompta (or N. pachyderma right) data points from the Nordic Seas where only Neogloboquadrina pachyderma should have been listed, and one outlier N. pachyderma value with no age control that was erroneously listed as having a level-4 chronozone quality. The corrected version of MARGO Late Holocene planktonic oxygen-18 dataset is available in the Supplement. As a result, the dataset used in the present study contains 248 values for Neogloboquadrina pachyderma, 128 values for Globigerina bulloides, 59 values for Neogloboquadrina incompta, 135 values for Globigerinoides ruber and 51 values for Globigerinoides sacculifer. In the remainder of the paper and following the genus reassignment of  Spezzaferri et al.
Calculation of the best-fitting maximum depth per foraminifer species In Eq. (  8 ), the maximum depth of integration per foraminifer species, z b (k), is a free parameter and needs to be determined. We have chosen to calculate it as the depth where the δ 18 O c simulated by FAME driven by the World Ocean Atlas 2013 temperature and derived seawater δ 18 O datasets is closest on average to MARGO Late Holocene δ 18 O c data. The rationale behind this choice is to specifically design FAME to enable model-data comparison with isotopic records from marine sediment cores. To determine the optimal value of z b (k), we repeated successive runs of FAME with values of z b ranging from 1500 m to the surface along the standard World Ocean Atlas vertical grid. The only difference between the different species at this stage are the species-specific terms in the equations presented and the data of each species from the MARGO Late Holocene set.
Error distribution Since the depth parameter was constrained using the MARGO Late Holocene dataset by error minimization, it is not surprising that the errors obtained with FAME are very small on average for each species considered (Fig. 2 ). The error distribution obtained with FAME is very similar to the one obtained with the simple surface equilibrium assumption for the two species closest to the surface (G. ruber and N. incompta). For deeper dwellers (T. sacculifer, G. bulloides & N. pachyderma), FAME results are better than those obtained with the old method, as expected, since deeper layers in the ocean are accounted for.
Robustness of results To test the robustness of our calibration in depth or error distribution, we performed a full set of additional analyses using the lower and upper growth functions as presented in Fig. A1 , introduced here above. Regarding depth calibration, we find our results to be largely insensitive to the use of these upper-bound and lowerbound values for the growth functions. Specifically, the uncertainty in the maximum growth depth is largest on N. pachyderma (range of 475 to 600 m) and G. bulloides (400 to 450 m). It is somewhat smaller for T. sacculifer (100 to Error distribution for the "old method" (grey) and the "FAME method" (orange) using climatological datasets as compared to MARGO Late Holocene dataset  (Waelbroeck et al. , 2005). Best-fitting distributions are calculated and plotted as a solid line for the FAME method and as a dashed line for the old method, except for T.
Geographical distribution To further check our methodology against the MARGO Late Holocene dataset, we compare the zone of presence Ocean regions where FAME predicts that the species is present at some time of the year (µ > 0) are plotted in blue, with shades of blue indicating the number of months of presence. Overlaid are the MARGO Late Holocene data (quality levels 1-4) species' abundance data, plotted using the yellow-white to dark red color bar and given in percent. A qualitative correspondence between simulated FAME presence/absence and the occurrence of 10 % level in the difference species is noted. of each species predicted by FAME (grossly determined by µ ) with the observed reported abundances in the MARGO dataset (restricted to chronology quality values 1-4). As noted above, we cannot predict the relative abundance of each species.
The very low number of high-quality dated data points in the latter area prevents a definitive conclusion. Also, specific zones, consistent for several species, may be noted, such as the Benguela upwelling regions where FAME fails to predict the absence of T. sacculifer and G. ruber. One possible explanation for this mismatch could be the impact of increased nutrient availability on observed abundances as a consequence of the upwelling systems, whereas nutrients are at present ignored in the FAME approach. Another possibility could be the quality of the vertical oceanic structure obtained from the World Ocean Atlas in those upwelling regions. Finally, it should be noted that our com- parison ignores the natural interannual variability since we are using climatologies. The interannual variability involves changes in the location of the fronts and currents, and thus bears the potential of shifting the spatial boundaries between the different foraminifer species.
Further discussion of the abundance comparison including all data points from the MARGO Late Holocene dataset regardless of the dating quality is given in Appendix B. To further investigate the functioning of the FAME model, it is useful to consider the spatial distribution of the depth at which each species' growth is maximum. An example is given for the month of July in Fig. 4. It clearly shows that even though the maximum depth allowed for each species is fixed through the z b (k) parameter, the predicted/computed calcification depth varies according to the location in the World Ocean. Except for G. ruber which always calcifies in the topmost ocean layers, the depth of maximum growth exhibits large spatial variations, notably at the edge of the species' domains; in July, this is particularly marked in the case of G.
Effect of a large climatic change on the computed oxygen-18 content of the calcite Though FAME gives realistic results when forced by atlas data, it is mostly designed to retrieve the species-specific effect of climate change on the recorded δ 18 O c. To highlight the effect of seasonal and vertical weighting of the δ 18 O c signal computed by FAME, we have performed a simplified experiment showing the effect of a change in the foraminifers' living conditions on their δ 18 O c signal. To simulate a change in climatic conditions, we apply a homogeneous 4 • C decrease to the WOA13 sea temperature dataset and compute the anomaly in δ 18 O c between that new cold state and the original one for each species, as well as for the surface equilibrium approach (Fig. 6 ). This anomaly is noted 18 O c in what follows.
Summary and conclusions We developed the FAME (Foraminifers As Modeled Entities) module to account for planktonic foraminifer speciesspecific habitat when computing their calcite oxygen-18. In contrast to models predicting the abundance of planktonic foraminifers, FAME only aims at predicting the presence or absence of a given species and its oxygen-18 value. FAME is only forced by hydrographic data and uses a very limited number of parameters, almost all derived from culture experiments. Taken together, these characteristics make FAME a uniquely simple and robust model for predicting changes in the oxygen-18 of commonly used planktonic foraminifer species in response to changing climatic conditions. FAME performance is evaluated using MARGO Late Holocene planktonic foraminifer δ 18 O c and abundance datasets.
A2 Computation of the δ 18 O sw -salinity relationships The basins defined in the previous section are then used to cluster the raw data, δ 18 O sw and salinity, of the GISS database  (Schmidt et al. , 1999)  in the respective basins. Furthermore, only data locations where both δ 18 O sw and salin- The "*" symbol highlights the regions where FAME and WOA regions do not cover the same area (see text). ity are given in the original database for depths less than 200 m are retained under the additional constraint that depth of the ocean should be more than 175 m. The latter are to ensure that the values are representative of high sea values and not coastal areas, possibly under fluvial influence. Additionally, all values below 5 permil in salinity are ignored for all basins. Lastly, for two basins (North Atlantic and Bay of Bengal), the existence of two different slopes where only one corresponds to open-ocean conditions renders necessary the addition of one additional condition to keep only the latter.
g. ,  Mackas et al. , 2006)  and are characterized by strong contrasts in surface water properties with respect to the surrounding regions, large interannual and intra-seasonal variability, and high phytoplankton production. The existence of this consistent pattern in upwelling regions in the unsorted database confirms that G. ruber and T. sacculifer distributions are not well simulated in upwelling regions, either because nutrients are presently not accounted for in FAME or because the increased nutrient availability and/or the vertical structure of oceanic physical properties is not faithfully depicted in the 1 • resolution WOA13 dataset we used in input. The unsorted distribution for G. bulloides still presents an excellent match for the limits but with some discrepancies in the equatorial and tropical latitudes; albeit MARGO unsorted data do not present a large regional consistency outside the northern coast of Brazil (where FAME also predicts the absence of G.
bulloides). Aside from some minor mismatches in the southern Indian Ocean, the conclusions for N. pachyderma are also largely unaffected by the use of all the points from the MARGO database. To conclude, the use of all the data points regardless of the quality of the chronological control in the MARGO Late Holocene database does not add much new information, especially since the data points should be considered with caution as they could correspond to a different climate regime than the Late Holocene. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-11-3587-2018-supplement. Author contributions. DMR and CW designed the study. DMR wrote the code and performed the experiments with regular input from CW. DMR wrote the manuscript with contribution form all co-authors.
Figure2. Error distribution for the "old method" (grey) and the "FAME method" (orange) using climatological datasets as compared to MARGO Late Holocene dataset (Waelbroeck et al. , 2005). Best-fitting distributions are calculated and plotted as a solid line for the FAME method and as a dashed line for the old method, except for T. sacculifer, for which the small number of available data points yields a poor fit both for FAME and the old method. The mean and deviation are given for FAME and the old method at the top of each panels.
Figure3. Model-data comparison of species' abundances. Ocean regions where FAME predicts that the species is present at some time of the year (µ > 0) are plotted in blue, with shades of blue indicating the number of months of presence. Overlaid are the MARGO Late Holocene data (quality levels 1-4) species' abundance data, plotted using the yellow-white to dark red color bar and given in percent. A qualitative correspondence between simulated FAME presence/absence and the occurrence of 10 % level in the difference species is noted.
Acknowledgements. This is a contribution to the  ACCLIMATE ERC  project. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013 ; grant agreement no. 339108 ). We thank  Lukas Jonkers  and  Michal Kucera  for fruitful discussions on earlier versions of this work. Edited by: Sandra Arndt Reviewed by: two anonymous referees.