Artificial intelligence for unstructured healthcare data: application to coding of patient reporting of adverse drug reactions

Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will however only be optimized if systems can manage to deal with its tremendous flow of information, based primarily on unstructured text fields. The aim of this study was to develop an automated system allowing to code ADRs from patient reports. Our system was based on a knowledge base about drugs, enriched by supervised Machine Learning (ML) models trained on patients reporting data. To train our models, we selected all cases of ADRs reported by patients to a French Pharmacovigilance Centre through a national web-portal between March 2017 - March 2019 (n =2,058 reports). We tested both conventional ML models and deep-learning models. We performed an external validation using a dataset constituted of a random sample of ADRs reported to the Marseille Pharmacovigilance Centre over the same period (n=187). Here we show that regarding AUC and F-measure, the best model to identify ADRs was gradient boosting trees (LGBM), with an AUC of 0.93 [0.92-0.94] and F-measure of 0.72 [0.68 - 0.75]. This model was run for external validation showing an AUC of 0.91 and a F-measure of 0.58. We evaluated an artificial intelligence pipeline that was found able to learn how to identify correctly ADRs from unstructured data. This result allowed us to start a new study using more data to further improve our performance and offer a tool that is useful in practice to efficiently manage drug safety information

Lacosamide adjunctive therapy for partial-onset seizures: a meta-analysis

Background. The relative efficacy and safety of lacosamide as adjunctive therapy compared to other antiepileptic drugs has not been well established.Objective. To determine if lacosamide provides improved efficacy and safety, reduced length of hospital stay and improved quality of life compared with other anti-epileptic therapies for adults with partial-onset seizures.Data Sources. A systematic review of the medical literature using Medline (1946–Week 4, 2012), EMBASE (1980–Week 3, 2012), Cochrane Central Register of Controlled Trials (Issue 1 of 12, January 2012). Additional studies were identified (through to February 7, 2012) by searching bibliographies, the FDA drug approval files, clinical trial registries and major national and international neurology meeting abstracts. No restrictions on publication status or language were applied.Study Selection. Randomized controlled trials of lacosamide in adults with partial-onset seizures were included.Data Extraction. Study selection, extraction and risk of bias assessment were performed independently by two authors. Authors of studies were contacted for missing data.Data Synthesis. All pooled analyses used the random effects model.Results. Three trials (1311 patients) met inclusion criteria. Lacosamide increased the 50% responder rate compared to placebo (RR 1.68 [95% CI 1.36 to 2.08]; I2 = 0%). Discontinuation due to adverse events was statistically significantly higher in the lacosamide arm (RR3.13 [95% CI 1.94 to 5.06]; I2 = 0%). Individual adverse events (ataxia, dizziness, fatigue, and nausea) were also significantly higher in the lacosamide group.Limitations. All dosage arms from the included studies were pooled to make a single pair-wise comparison to placebo. Selective reporting of outcomes was found in all of the included RCTs.Conclusions. Lacosamide as adjunctive therapy in patients with partial-onset seizures increases the 50% responder rate but with significantly more adverse events compared to the placebo

Effects of food on physical and sleep complaints in children with ADHD: a randomised controlled pilot study

Attention deficit/hyperactivity disorder (ADHD), a common behavioural disorder in children, may be associated with comorbid physical and sleep complaints. Dietary intervention studies have shown convincing evidence of efficacy in reducing ADHD symptoms in children. In this pilot study, we investigated the effects of an elimination diet on physical and sleep complaints in children with ADHD. A group of 27 children (3.8–8.5 years old), who all met the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for ADHD, were assigned randomly to either a diet group (15/27) or a control group (12/27). The diet group followed a 5-week elimination diet; the control group adhered to their normal diet. Parents of both groups had to keep an extended diary and had to monitor the behaviour and the physical and sleep complaints of their child conscientiously. The primary endpoint was the clinical response, i.e. a decrease of physical and sleep complaints, at the end of the trial, based on parent ratings on a Physical Complaints Questionnaire. The number of physical and sleep complaints was significantly decreased in the diet group compared to the control group (p < 0.001), with a reduction in the diet group of 77% (p < 0.001, effect size = 2.0) and in the control group of 17% (p = 0.08, effect size = 0.2). Specific complaints that were significantly reduced were in three domains: headaches or bellyaches, unusual thirst or unusual perspiration, and sleep complaints. The reduction of complaints seemed to occur independently of the behavioural changes (p = 0.1). However, the power of this comparison was low. A positive correlation existed between the reduction of physical and behavioural symptoms (p < 0.01). The reduction did not differ between children with or without an atopic constitution (p = 0.7). An elimination diet may be an effective instrument to reduce physical complaints in children with ADHD, but more research is needed to determine the effects of food on (functional) somatic symptoms in children with and without ADHD. This trial was registered as an International Standard Randomised Controlled Trial, ISRCTN47247160