dataesr/scientific-paragraphs-categorization huggingface dataset:
Chromatographic conditions. RP-HPLC-UV (ESI-MS) conditions. HPLC-System: Thermo Scientific Dionex Ultimate 3000 ® , Courtaboeuf, France) with binary pump, PDA detector, autosampler and Chromeleon software. Detection: 214 nm. Injection volume: 20 μL. Column: Zorbax SB ® -C18 (5 μm, 4. 6 × 250 mm), equipped with a pre-column and thermostated at 25 °C. Mobile Phase: Gradient: [MeOH:Water-0. 05% TFA]: 10:90 → 80:20 within 60 min). Flow: 0. 6 mL/min. All solutions were prepared in MeOH (2 mg/mL) and filtered through 0. 2 μm nylon filter disk prior the injection. ESI-MS spectrum (Thermo-Scientific Surveyor MSQ Plus single-quadrupole mass detector (Thermo Fisher Scientific, Courtaboeuf, France)). 2 experiments. Separations were performed using an Ultimate 3000 RSLC system equipped with a binary pump, an autosampler and a thermostated column compartment, equipped with a diode array detector (195-800 nm) (Dionex, Germering, Germany).
Capillary voltage was set at +4. 5 kV. The flows of nebulizing and drying gas (nitrogen) were respectively set at 2. 0 bar and 9. 0 L/min and drying gas was heated at 200 °C. Mass spectra were recorded in the range 50-1650 m/z. MS/MS experiments were conducted using data dependent acquisition (DDA) mode (auto-MS/MS) in a mass window from m/z 150-1200. Three precursor ions with intensities higher than 400 au were selected per fragmentation cycle among the most intense ions to be fragmented. These three precursor ions were allowed to be selected for two consecutive cycles and were then placed on an exclusion list for 0. 05 min. The collision energy was set at 35 eV and was applied as follows: 88% of the collision energy was applied during half of the fragmentation cycle, and 117% of the collision energy was applied during the half remaining cycle time.
Data collection and details of structure refinement The X-ray data for -Se were collected using Ag K radiation ( = 0. 56086 A ˚) on a Bruker D8 Venture equipped with a CMOS Photon100 detector at 100 (2) K (Oxford Cryosystem N 2 cooling system). The data were collected using ! scans with a width of 0. 5 per frame up to a resolution of (sin/) max = 1. 123 A ˚À1 with a completeness of 97%. Cell refinement, data integration and data reduction were carried out using the APEX3  (Bruker, 2015)  software package. Face indexing was performed for numerical absorption correction  (Busing & Levy, 1957; Coppens et al. , 1965). The SORTAV  (Blessing, 1997)  program present in the WinGX  (Farrugia, 2012)  software package was utilized for sorting, scaling and merging of the data. The crystal structure was solved by direct methods  (Harker & Kasper, 1948; Karle & Hauptman, 1950)  and first refined on the basis of a spherical-atom approximation based on F 2 using SHELXL2014  (Sheldrick, 2008 (Sheldrick, , 2014)).
Multipole modelling The multipolar charge-density refinement was performed against F 2 using the Hansen-Coppens multipolar model  (Hansen & Coppens, 1978)  implemented in the MoPro/ MoProviewer software package  (Jelsch et al. , 2005; Guillot et al. , 2014). The refinement was performed up to a resolution of (sin/) max = 1. 08 A ˚À1 for 9660 reflections with I > 3(I). In the first step of the refinement, the scale factor was refined against all these diffraction data using the SHELX refined parameters. Reflections 111 and 012 were affected by the beam stop and had unrealistically large differences between F obs and F calc which resulted in high residual density, so they were removed from the final refinement. Reflections 002 and 101 were also slightly affected by the beam stop, causing a skewed F obs /F calc ratio at sin/ < 0.
An isotropic extinction correction was applied during the refinement and this significantly improved the residual density around the Se and Br atoms. The topological analysis of the electron density was also performed and visualized using MoPro/MoProViewer. More information concerning the data reduction and multipolar modelling are given in Table  1. To judge the quality of the modelled electron density, the variations in |F obs |/|F calc | with sin/ and of F obs with F calc are presented in Fig. S1. These maps confirm the very good quality of the diffraction data, despite a few very low-and very high-resolution data. 2. 4. Computational details 2. 4. 1. Theoretical modelling. Single-point periodic quantum mechanical calculations were performed with the TZVP  (Scha ¨fer et al. , 1992; Peintinger et al.
Multipolar refinement of the theoretical data was carried out up to the same levels as those used for the experimental charge-density refinement. The final R(F) and R(F 2 ) were 0. 003 and 0. 005, respectively, for the theoretical model. 2. 4. 2. Electrostatic potential maps. Experimental and theoretical three-dimensional molecular electrostatic potential maps (MESPs) of -Se were plotted on a Hirshfeld isosurface using the CrystalExplorer17 software  (Turner et al. , 2017)  at the MP2/6-311G** level, and also using the Mopro viewer  (Guillot et al. , 2014). 2. 4. 3. Natural bond orbital analysis. The natural bond orbital analysis  (Reed et al. , 1986 (Reed et al. , , 1988 ) was performed at the B3LYP/6-311G** level using the NBO6. 0  (Glendening et al. , 2013)  package integrated with GAUSSIAN09  (Frisch, 2009).
Crystal packing The compound -Se crystallizes in the P2 1 /n space group with Z = 4 (Fig. 3a  and Table  1 ). It is interesting to note that the two Cg1 and Cg2 rings present in the molecule (Fig. 2 ) are almost perpendicular to each other, with an angle between their planes of 83. 8 (Fig. S7a ). No other ebselen derivative present in the Cambridge Structural Database (CSD, Version 5. 39;  Groom et al. , 2016)  has such a pronounced deviation from planarity (Fig. S7 ). The salient feature in the crystal packing of -Se is the presence of a short and directional SeÁ Á ÁO chalcogen bond (SeÁ Á ÁO = 2. 667 A ˚, N-SeÁ Á ÁO = 174 ) and a C-HÁ Á ÁO hydrogen bond (HÁ Á ÁO = 2. 37 A ˚, C-HÁ Á ÁO = 122 ) which form a molecular chain (Fig. 3c ). In a previous charge-density study of ebselen derivatives  (Thomas et al.
Results: A total of six hospitals were visited and all institutions had a pharmacovigilance centre, only three could however be described as functional or partially functional. Only one centre had a financial provision for pharmacovigilance activities. Of note was the absence of the national adverse drug reaction reporting form in one of the hospitals. The number of adverse drug reaction reports found in the databases of the centres ranged from none to 26 for the previous year and only one centre had fully committed their reports to the National Pharmacovigilance Centre. There were few documented medicines related admissions ranging from 0. 0985/1000 to 1. 67/1000 and poor documentation of pharmacovigilance activities characterised all centres. Conclusion: This study has shown an urgent need to strengthen the pharmacovigilance systems in the South-South zone of Nigeria.
Data collection The data were obtained using a modified WHO data collection form for pharmacovigilance indicators  [29]  by one of the researchers through interviews of the focal person for pharmacovigilance or the pharmacovigilance committee. The components of this form included the background information, structural indicators, process indicators, outcome/impact indicators. The phrasing of the assessment questions was adapted to address the tertiary hospital setting (Additional file 1). The background information collected characteristics of the hospitals: teaching hospital staff strength, i. e. number of post registration health professionals in different categories: doctors, nurses, pharmacists, specialist disposition, average out-patient attendance over the last year, total number of beds in the hospital.
Core process indicators The absolute number of ADR reports received among the 6 hospitals in the previous year ranged from 0 to 26, two hospitals had no reports for the previous year 2015. Furthermore, the total number of reports in the local database ranged from 0 to 831. Cohort event monitoring of antimalarials (artemisinin-based combination therapy) was carried out and completed in UBTH in the five years preceding the analysis as a form of active surveillance. There were limited numbers of reports on ADRs, medication errors, lack of therapeutic effectiveness etc. in most of the centers. Documentation of feedback and causality assessment carried out on reports in the centers was equally poor in this study (Table  3 ).
Background: Medical coding is used for a variety of activities, from observational studies to hospital billing. However, comorbidities tend to be under-reported by medical coders. The aim of this study was to develop an algorithm to detect comorbidities in electronic health records (EHR) by using a clinical data warehouse (CDW) and a knowledge database. Methods: We enriched the Theriaque pharmaceutical database with the French national Comorbidities List to identify drugs associated with at least one major comorbid condition and diagnoses associated with a drug indication. Then, we compared the drug indications in the Theriaque database with the ICD-10 billing codes in EHR to detect potentially missing comorbidities based on drug prescriptions. Finally, we improved comorbidity detection by matching drug prescriptions and laboratory test results.
We tested the obtained algorithm by using two retrospective datasets extracted from the Rennes University Hospital (RUH) CDW. The first dataset included all adult patients hospitalized in the ear, nose, throat (ENT) surgical ward between October and December 2014 (ENT dataset). The second included all adult patients hospitalized at RUH between January and February 2015 (general dataset). We reviewed medical records to find written evidence of the suggested comorbidities in current or past stays. Results: Among the 22,132 Common Units of Dispensation (CUD) codes present in the Theriaque database, 19,970 drugs (90. 2%) were associated with one or several ICD-10 diagnoses, based on their indication, and 11,162 (50. 4%) with at least one of the 4878 comorbidities from the comorbidity list. Among the 122 patients of the ENT dataset, 75.
4% had at least one drug prescription without corresponding ICD-10 code. The comorbidity diagnoses suggested by the algorithm were confirmed in 44. 6% of the cases. Among the 4312 patients of the general dataset, 68. 4% had at least one drug prescription without corresponding ICD-10 code. The comorbidity diagnoses suggested by the algorithm were confirmed in 20. 3% of reviewed cases. Conclusions: This simple algorithm based on combining accessible and immediately reusable data from knowledge databases, drug prescriptions and laboratory test results can detect comorbidities.
For this, clinical coders need to have access to all the data contained in the medical record. With the increasing use of electronic health records (EHR), such data become more easily available, especially through clinical data warehouses (CDW) that are great sources of integrated heterogeneous and exhaustive information  [9, 10]. In parallel, knowledge databases, such as pharmaceutical databases, are used to analyze and process clinical data and can also support clinical decision-making  [11, 12]. The challenge lies in combining these different sources of information to improve the accuracy and exhaustivity of medical codes. The aim of this study was to develop an algorithm to detect comorbidities in EHR using a clinical data warehouse and knowledge databases. We focused on two different types of data, drug prescriptions and laboratory tests, to assess how they contribute and complement each other for identifying comorbidities.
The RUH CDW (EHOP) The EHOP  [10, 13]  CDW contains most of the EHR, including clinical notes, drug prescriptions, laboratory test records and administrative data. Each patient's EHR includes all information about his/her hospital stay. If the patient was hospitalized several times, all these hospital stays are grouped in a single EHR. Diagnoses are coded using the French version of the international classification of diseases, 10th edition (ICD-10), that incorporates the specific French modifications. Data are de-identified and subdivided in structured (drug prescriptions, laboratory test records…) and unstructured (clinical notes) data. EHOP does not include records from the Anesthesiology Department and the Intensive Care Unit (available only in paper form) and the nursing files.
Theriaque database The Theriaque database  [14]  is one of the four drug databases  [15]  certified by the French Health Authorities (specifically by the Haute Autorité en Santé) and provides exhaustive information on drugs with a marketing authorization. For each drug, this database gives one or more indications (ICD-10 codes, when possible), its Anatomical Therapeutic Chemical (ATC) code  [16, 17]  and an unique identifier called "Common Unit of Dispensation" (CUD) ("Unité Commune de Dispensation" in French). CUD codes are the French standard for drug identification and are used to process drug data in the Hospital Information System.
Algorithm development To detect comorbidities, we developed a three-step algorithm that we then tested in a retrospective observational study (Figure  1 ). Step one: Enrichment of the theriaque database with the comorbidity list We paired the Theriaque database with the Comorbidity List to identify drugs associated with at least one major comorbid condition. This step also identified ICD-10 codes associated with a drug indication and eliminated all ICD-10 codes that could not be paired with at least one drug indication. It also excluded drugs with very broad (i. e. , more than twenty indications) or only imprecise (e. g. , "Pain, unspecified", R52. 9) indications, based on their ATC classification (Additional file 1). Step two: Detection of comorbidities in the EHR by using drug prescriptions 2.
Pairing the ICD-10 drug indications with the ICD-10 billing codes The aim of this step was to estimate the proportion of existing ICD-10 codes from the EHR that can be correctly retrieved by the algorithm. For each hospital stay, we retrieved the names of all the prescribed/administered drugs and all ICD-10 codes originally assigned to that hospital stay by the non-medical coding staff. Then, we compared these ICD-10 codes with those suggested by the Theriaque database for each prescribed/administered drug. This step led to the identification of stays where all ICD-10 codes and drug prescriptions were matched.
3. Comparing the theriaque database drug indications with the EHR information For the stays where not all drug prescriptions were matched with ICD-10 codes, we derived the potentially missing diagnoses using the prescribed drugs. Briefly, each drug prescription without at least one ICD-10 code that matched the drug indications, according to the Theriaque database, was a clue for a possible missing comorbidity. For these drugs, the Theriaque database suggested a list of ICD-10 codes, among which some were included in the Comorbidity List and were thus flagged as possible comorbidities. Step three: Detection of comorbidities in the EHR using drug prescriptions and laboratory test results In the last step, we improved comorbidity detection by using another source of structured data. For this study, we focused on sodium polystyrene sulfonate (SPS), because this drug is only prescribed in the case of hyperkalemia (ICD-10 code E87.
Study populations We retrospectively tested the algorithm using two different datasets extracted from the EHOP CDW. The first dataset included all stays of adult patients hospitalized at RUH ear, nose, throat (ENT) surgical ward between October and December 2014 and with at least one drug prescription (ENT dataset). We chose this unit because comorbidities unrelated to the cause of hospitalization are rarely mentioned in the surgical record and thus, it represented a good test for our algorithm. The second dataset included the stays of all adult patients hospitalized at RUH between January and February 2015 and with at least one drug prescription (general dataset).
Algorithm evaluation We reviewed all the diagnoses suggested by the algorithm using the data from the medical records extracted from the EHOP CDW, to find written evidence about such comorbidities in the current or past hospital stays. We evaluated the algorithm performance by computing Precision, Recall and F-Score metrics for the ENT dataset. For the general dataset, we did not compute Recall because the value obtained for the ENT dataset suggested that the algorithm could efficiently detect comorbidities based on the drug prescriptions. Precision is the ratio between the number of stays with confirmed diagnoses and all stays with suggested diagnoses. Recall is the ratio between the number of stays with confirmed diagnoses and all stays with missing diagnoses. The F-Score is the harmonic mean of Precision and Recall.
Statistical analysis We described quantitative variables using means and standard deviation and categorical variables using counts and percentages. We calculated the inter-expert agreement using the Cohen's Kappa (κ) coefficient. We performed all statistical analyses with R 3. 3. 1  [19]. The suggested diagnoses were confirmed or not by one medical expert for the ENT dataset and by two medical experts for the general dataset. These investigators had a public health background and were familiar with the EHOP CDW and the Program of Medicalization of Information Systems. They also knew well the coding guidelines because they have been involved in coding for a few years before this study. The two experts reviewed all records from the ENT dataset and only a random sample from the general dataset, to estimate the algorithm metrics and inter-observer agreement.
We calculated that the size of the general dataset sample required to have a 90% inter-observer agreement rate, with β = 0. 80 and α = 0. 05, was 285 stays. For each drug prescription, the two experts had to confirm at least one of the suggested comorbidities (ICD-10 codes) or to reject all of them. To confirm a diagnosis, they compared the title of the suggested ICD-10 code or its synonyms (based on the list of synonyms provided by ICD-10) with the diseases/symptoms recorded in the EHR. If they rejected all diagnoses suggested by the algorithm based on the drug prescriptions (structured data), they examined the discharge notes (unstructured data) to confirm their decision. This was done to ensure that the rejection was based on the absence of data on a given disease/symptom and not on the CDW inability to extract all available data.
Results Enrichment of the theriaque database with the comorbidity list and identification of the relevant ICD-10 codes Among the 22,132 CUD codes presents in the Theriaque database, 19,970 (90. 2%) were associated with one or more ICD-10 codes, based on the drug indication(s). Moreover, 11,162 (50. 4%) CUD codes were associated with at least one of the 4878 comorbidities from the Comorbidity List. Among the 40,000 codes included in the French ICD-10, only 4407 (11. 0%) were present in the Theriaque database as a drug indication.
Inter-observer agreement We assessed the inter-expert agreement using a random sample (n = 285) from the general dataset (see Materials and Methods). The two medical experts reached an agreement on 280 cases (κ = 0. 74; 95% CI, 0. 58-0. 90). For the five remaining stays, the experts' disagreement could be explained by the Program of Medicalization of the Information Systems coding rules. For instance, the reimbursement guidelines do not allow coding some diagnoses/symptoms. Accordingly, even if present in the medical record (and, thus, confirming the suggested comorbid conditions), these diagnoses will not be coded in the hospital stay (and, as a consequence, the drug will not be associated with an ICD-10).
Evaluation of the ENT dataset Among the 730 ICD-10 billing codes present in the 122 stays of the ENT dataset, 339 (46. 4%) were detected by the algorithm. The algorithm suggested a possible missing comorbidity in 92 of the 122 stays (75. 4%) (Additional file 3). The expert confirmed the suggested diagnoses in 41 of these 92 stays (44. 6%) and added 58 ICD-10 codes to the stay (Additional file 4). Among these 41 stays, 12 (29. 2%) had a major comorbidity (i. e. , included in the French Comorbidity List) (Table  1 ) (Additional file 5). Recall was 95. 3% and Precision was 44. 6%, with an F-Score of 60. 7%. The medical expert did not confirm the suggested diagnoses in 51 of the 92 stays for two main reasons: i) the stays did not contain information concerning the suggested comorbidity (n = 31; 60.
Evaluation of the general dataset Among the 4932 ICD-10 billing codes present in the 4312 stays of the general dataset, 1271 (25. 8%) were detected by the algorithm. The algorithm suggested a possible missing comorbidity in 2950 of the 4312 stays (68. 4%) (Table  1 ). The two experts confirmed the suggested diagnoses in 58 of the 285 reviewed stays (Precision, 20. 3%) and added 76 ICD-10 codes (Additional file 4). Among these 58 stays, 18 (31. 0%) had a major comorbidity added (Table  1 ) (Additional file 5). The two experts could not confirm the suggested diagnoses in 227 reviewed stays for two mains reasons: i) the EHR had no information concerning the suggested comorbidity (n = 95; 41. 9%); ii) the indication for the drug prescription was implicit and therefore could not be added to the stay (n = 132; 58.
1%). Combining SPS prescriptions and laboratory results (plasma potassium concentration) improved the detection of hyperkalemia by 66. 8%, compared with using only the laboratory results. Specifically, 1270 stays in the general dataset had laboratory results including blood potassium values and in 152 of them (12%) this value was higher than 5 mmol/L, but without a hyperkalemia ICD-10 billing code. This means that for 12% of stays in this dataset the hyperkalemia ICD-10 code could be added only on the basis of the laboratory test results. Moreover, 299 stays in this dataset included an SPS prescription. Among them, 85 did not have a hyperkalemia ICD-10 billing code and 67 had a potassium concentration value higher than 5 mmol/L, which means that in 78. 8% (67/85) of stays, the algorithm could detect a hyperkalemia ICD-10 code by combining the laboratory test result and the SPS prescription.
Discussion This study demonstrates that an algorithm based on clinical data and knowledge databases can detect comorbidities in EHR. The algorithm suggested missing comorbidities for more than half of the stays and, depending on the dataset, billing codes were improved by 6-8% after expert review. Moreover, by combining drug prescriptions and laboratory test results hyperkalemia detection was improved by 67%. To develop this algorithm, we applied strategies similar to those used for adverse drug event detection  [20] [21] [22]. First, we enriched a pharmaceutical database with a comorbidity list. Indeed, 90% of drugs in the Theriaque database have at least one ICD-10 drug indication. However, only 11% of the existing billing codes are present in the Theriaque database. On the other hand, the algorithm cannot use drugs with a wide range of indications (up to hundred codes) because they cannot be associated with a limited list of comorbidities.
Moreover, in specialties, such as psychiatry and rheumatology, most drugs are prescribed to treat symptoms rather than specific diseases. In addition, some major comorbidities from the French Comorbidity List are treated surgically (e. g. , gastrocolic fistulas  [23] ) and therefore cannot be detected with this algorithm. Despite these limitations, the algorithm covers 75% of stays with at least one drug prescription and about half of all hospital stays, confirming that drugs are adequate to capture comorbidities. Then, we combined laboratory test results and prescription of drugs with narrow indications (SPS, in this study) to improve comorbidity detection  [24]. This result needs to be confirmed in other similar situations, to further validate the algorithm performance. We then used data extracted from the EHOP CDW to confirm the diagnoses suggested by the algorithm.