| 1. |
- Ruilope, LM, et al.
(författare)
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Design and Baseline Characteristics of the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease Trial
- 2019
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Ingår i: American journal of nephrology. - : S. Karger AG. - 1421-9670 .- 0250-8095. ; 50:5, s. 345-356
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Tidskriftsartikel (refereegranskat)abstract
- <b><i>Background:</i></b> Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. <b><i>Patients and</i></b> <b><i>Methods:</i></b> The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate ≥25 mL/min/1.73 m<sup>2</sup> and albuminuria (urinary albumin-to-creatinine ratio ≥30 to ≤5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level α = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. <b><i>Conclusions:</i></b> FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049.
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| 2. |
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| 3. |
- Bittar, A., et al.
(författare)
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Text classification to inform suicide risk assessment in electronic health records
- 2019
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Ingår i: 17th World Congress on Medical and Health Informatics, MEDINFO 2019. - : IOS Press. - 9781643680026 ; , s. 40-44
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Konferensbidrag (refereegranskat)abstract
- Assessing a patient's risk of an impending suicide attempt has been hampered by limited information about dynamic factors that change rapidly in the days leading up to an attempt. The storage of patient data in electronic health records (EHRs) has facilitated population-level risk assessment studies using machine learning techniques. Until recently, most such work has used only structured EHR data and excluded the unstructured text of clinical notes. In this article, we describe our experiments on suicide risk assessment, modelling the problem as a classification task. Given the wealth of text data in mental health EHRs, we aimed to assess the impact of using this data in distinguishing periods prior to a suicide attempt from those not preceding such an attempt. We compare three different feature sets, one structured and two text-based, and show that inclusion of text features significantly improves classification accuracy in suicide risk assessment.
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| 4. |
- Viani, N., et al.
(författare)
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Time Expressions in Mental Health Records for Symptom Onset Extraction
- 2018
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Ingår i: EMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop. - : Association for Computational Linguistics. ; , s. 183-192
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Konferensbidrag (refereegranskat)abstract
- For psychiatric disorders such as schizophrenia, longer durations of untreated psychosis are associated with worse intervention outcomes. Data included in electronic health records (EHRs) can be useful for retrospective clinical studies, but much of this is stored as unstructured text which cannot be directly used in computation. Natural Language Processing (NLP) methods can be used to extract this data, in order to identify symptoms and treatments from mental health records, and temporally anchor the first emergence of these. We are developing an EHR corpus annotated with time expressions, clinical entities and their relations, to be used for NLP development. In this study, we focus on the first step, identifying time expressions in EHRs for patients with schizophrenia. We developed a gold standard corpus, compared this corpus to other related corpora in terms of content and time expression prevalence, and adapted two NLP systems for extracting time expressions. To the best of our knowledge, this is the first resource annotated for temporal entities in the mental health domain.
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| 5. |
- Ive, J., et al.
(författare)
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KCL-Health-NLP@CLEF eHealth 2018 Task 1 : ICD-10 coding of French and Italian death certificates with character-level convolutional neural networks
- 2018
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS.
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Konferensbidrag (refereegranskat)abstract
- In this paper we describe the participation of the KCL-Health-NLP team in the CLEF eHealth 2018 lab, specifically Task 1: Multilingual Information Extraction-ICD10 coding. The task involves the automatic coding of causes of death in death certificates in French, Italian and Hungarian according to the ICD-10 taxonomy. Choosing to work on the two Romance languages, we treated the task as a sequence-to-sequence prediction problem. Our system has an encoder-decoder architecture, with convolutional neural networks based on character em-beddings as encoders and recurrent neural network decoders. Our hypothesis was that a character-level representation would allow our model to generalise across two genealogically related languages. Results obtained by pre-training our Italian model on the French data set confirmed this intuition. We also explored the impact of character-level features extracted from dictionary-matched ICD codes. We obtained F-measures of 0.72/0.64 and 0.78 on the French aligned/raw and Italian raw internal test data, respectively. On the blind test set released by the task organisers, our top results were 0.65/0.52 and 0.69 F-measure, respectively.
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| 6. |
- Velupillai, Sumithra, et al.
(författare)
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Identifying suicidal adolescents from mental health records using natural language processing
- 2019
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Ingår i: 17th World Congress on Medical and Health Informatics, MEDINFO 2019. - : IOS Press. - 9781643680026 ; , s. 413-417
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Konferensbidrag (refereegranskat)abstract
- Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vulnerable group, but few studies have examined suicidal behaviour prevalence in large cohorts. Electronic Health Records (EHRs) are a rich source of secondary health care data that could be used to estimate prevalence. Most EHR documentation related to suicide risk is written in free text, thus requiring Natural Language Processing (NLP) approaches. We adapted and evaluated a simple lexicon- and rule-based NLP approach to identify suicidal adolescents from a large EHR database. We developed a comprehensive manually annotated EHR reference standard and assessed NLP performance at both document and patient level on data from 200 patients (~5000 documents). We achieved promising results (>80% f1 score at both document and patient level). Simple NLP approaches can be successfully used to identify patients who exhibit suicidal risk behaviour, and our proposed approach could be useful for other populations and settings.
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