LiSA : An assisted literature search pipeline for detecting serious adverse drug events with deep learning

Detecting safety signals attributed to a drug in scientific literature is a fundamental issue in pharmacovigilance. The constant increase in the volume of publications requires the automation of this tedious task, in order to find and extract relevant articles from the pack. This task is critical, as serious Adverse Drug Reactions (ADRs) still account for a large number of hospital admissions each year. The aim of this study is to develop an augmented intelligence methodology for automatically identifying relevant publications mentioning an established link between a Drug and a Serious Adverse Event, according to the European Medicines Agency (EMA) definition of seriousness. The proposed pipeline, called LiSA (for Literature Search Application), is based on three independent deep learning models supporting a precise detection of safety signals in the biomedical literature. By combining a Bidirectional Encoder Representations from Transformers (BERT) algorithms and a modular architecture, the pipeline achieves a precision of 0.81 and a recall of 0.89 at sentences level in articles extracted from PubMed (either abstract or full-text). We also measured that by using LiSA, a medical reviewer increases by a factor of 2.5 the number of relevant documents it can collect and evaluate compared to a simple keyword search. In the interest of re-usability, emphasis was placed on building a modular pipeline allowing the insertion of other NLP modules to enrich the results provided by the system, and extend it to other use cases. In addition, a lightweight visualization tool was developed to analyze and monitor safety signal results. Overall, the generic pipeline and the visualization tool proposed in this article allows for efficient and accurate monitoring of serious adverse drug reactions from the literature and can easily be adapted to similar pharmacovigilance use cases. To facilitate reproducibility and benefit other research studies, we also shared a first benchmark dataset for Serious Adverse Drug Events detection

Identification of Predictive Factors of Diabetic Ketoacidosis in Type 1 Diabetes Using a Subgroup Discovery Algorithm.

AIMS Diabetic ketoacidosis (DKA) is a serious and potentially fatal complication of type 1 diabetes and it is difficult to identify individuals at increased risk. The aim of this study was to identify predictive factors for DKA by retrospective analysis of registry data and use of a subgroup discovery algorithm. MATERIALS AND METHODS Data from adults and children with type 1 diabetes and >2 diabetes-related visits were analyzed from the Diabetes Prospective Follow-up Registry. Q-Finder®, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH <7.3 during a hospitalization event. RESULTS Data for 108,223 adults and children, of whom 5,609 (5.2%) had DKA, were studied. Q-Finder® analysis identified 11 profiles associated with increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; HbA1c ≥8.87 [73 mmol/mol]; no fast-acting insulin intake; age <15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycemia; hypoglycemic coma; and autoimmune thyroiditis. Risk of DKA increased with number of risk profiles matching patients' characteristics. CONCLUSIONS Q-Finder® confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA. This article is protected by copyright. All rights reserved

Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States

Abstract Background Early diagnosis of Gaucher disease (GD) allows for disease-specific treatment before significant symptoms arise, preventing/delaying onset of complications. Yet, many endure years-long diagnostic odysseys. We report the development of a machine learning algorithm to identify patients with GD from electronic health records. Methods We utilized Optum’s de-identified Integrated Claims-Clinical dataset (2007–2019) for feature engineering and algorithm training/testing, based on clinical characteristics of GD. Two algorithms were selected: one based on age of feature occurrence (age-based), and one based on occurrence of features (prevalence-based). Performance was compared with an adaptation of the available clinical diagnostic algorithm for identifying patients with diagnosed GD. Undiagnosed patients highly-ranked by the algorithms were compared with diagnosed GD patients. Results Splenomegaly was the most important predictor for diagnosed GD with both algorithms, followed by geographical location (northeast USA), thrombocytopenia, osteonecrosis, bone density disorders, and bone pain. Overall, 1204 and 2862 patients, respectively, would need to be assessed with the age- and prevalence-based algorithms, compared with 20,743 with the clinical diagnostic algorithm, to identify 28 patients with diagnosed GD in the integrated dataset. Undiagnosed patients highly-ranked by the algorithms had similar clinical manifestations as diagnosed GD patients. Conclusions The age-based algorithm identified younger patients, while the prevalence-based identified patients with advanced clinical manifestations. Their combined use better captures GD heterogeneity. The two algorithms were about 10–20-fold more efficient at identifying GD patients than the clinical diagnostic algorithm. Application of these algorithms could shorten diagnostic delay by identifying undiagnosed GD patients

Hells Bells – unique speleothems from the Yucatán Peninsula, Mexico, generated under highly specific subaquatic conditions

We here report on a type of meter-sized pendant speleothem growing under water in the submerged El Zapote sinkhole (cenote) west of Puerto Morelos on the Mexican Yucatán Peninsula. These conical, mantle-shaped downward expanding and diverging calcareous structures, here termed as Hells Bells, are yet unreported in the scientific literature. They are characterized by bell- or trumpet shaped longitudinal and circular, elliptical or horse-shoe-like horizontal cross-sections. Hells Bells grow downward, based on the downward divergence of the structures and the horizontally laminated internal texture of both blade-shaped spar calcite and microspar laminae. Age dating confirms that Hells Bells are young (\u3c 4500 yr) and formed in a subaquatic environment. They grow under lightless conditions in a stratified water body, which is characterized by a fresh water body overlying a salt water body with a stagnant transition zone (halocline) of several meters. We hypothesize that the growth of these structures is mediated by specific physical and biogeochemical conditions above and in the halocline. Stagnant hydraulic conditions led to extensive diffusion profiles of several nutrients including calcium originating from the salt water body. Dissolved organic carbon from the fresh water is microbially oxidized in the upper part of the halocline, where a distinct redox zonation was identified from oxic to anoxic conditions. Degradation processes combined with slightly alkaline pH values as well as the diffusive transport of calcium into this zone may induce an increase in calcite oversaturation. Phylogenetic analysis of the community on the surface of the Hells Bells suggests the presence of microorganisms involved in the nitrogen-cycle, from which some potentially have the capability to increase the pH by autotrophic growth and denitrifying activity, thus supporting calcite precipitation. The growth of Hells Bells is strictly dependent on the elevation of the halocline. This offers a wide potential for the use of Hells Bells as