A Case Study of the Incremental Utility for Disease Identification of Natural Language Processing in Electronic Medical Records

Diagnostic Trends and Prescription Patterns in Disruptive Mood Dysregulation Disorder and Bipolar Disorder

OBJECTIVE

Disruptive mood dysregulation disorder (DMDD) was introduced in DSM-5 to distinguish a subset of chronically irritable youth who may be incorrectly diagnosed and/or treated for pediatric bipolar disorder (BPD). This study characterized the rate of new treatment episodes and treated prevalence of BPD and DMDD from a longitudinal electronic health record database and examined the impact of DMDD on prescription trends.

METHOD

A retrospective cohort study using 2008-2018 Optum electronic health record data was conducted. Youth aged 10 to < 18 years with ≥ 183 days of database enrollment before the study cohort entry were included. Annual new treatment episode rates per 1,000 patient-years and treated prevalence (%) were estimated. Prescriptions for medications, concomitant diagnoses, and acute mental health service use for 2016-2018 were evaluated.

RESULTS

There were 7,677 youths with DMDD and 6,480 youths with BPD identified. Mean age (13-15 years) and ethnicity were similar for both groups. A rise in new treatment episode rates (0.87-1.75 per 1,000 patient-years, p < .0001) and treated prevalence (0.08%-0.35%, p < .0001) of DMDD diagnoses (2016-2018) following diagnosis inception was paralleled by decreasing new treatment episode rates (1.22-1.14 per 1,000 patient-years, p < .01) and treated prevalence (0.42%-0.36%, p < .0001) of BPD diagnoses (2015-2018). More youth in the DMDD group were prescribed medications compared with the BPD group (81.9% vs 69.4%), including antipsychotics (58.9% vs 51.0%). Higher proportions of youth with DMDD vs youth with BPD had disruptive behavior disorders (eg, 35.9% vs 20.5% had oppositional defiant disorder), and required inpatient hospitalization related to their mental health disorder (45.0% vs 33.0%).

CONCLUSION

Diagnosis of DMDD has had rapid uptake in clinical practice but is associated with increased antipsychotic and polypharmacy prescriptions and higher rates of comorbidity and inpatient hospitalization in youth with a DMDD diagnosis compared with a BPD diagnosis.

Using Artificial Intelligence Technology to Solve the Electronic Health Service by Processing the Online Case Information

With the continuous improvement of economic level and the continuous development of science and technology in China, information technology has begun to integrate into all walks of life. Medical units have begun to change from the traditional medical system to the intelligent system, and the processing of online case information has become an important component of medical informationization. To improve the efficiency of dealing with online case information, this study proposes to establish a fully connected neural network model to deal with online cases. Using jieba word segmentation tool and data preprocessing technology, the data of electronic medical records are sorted out, and the data are quantified using Word2Vec and other tools, and the data on electronic medical records are converted into one-hot binary variables. The quantified data are trained into a fully connected neural model, and the accuracy rate is about 88%. It is compared with naive Bayes and decision tree classification methods, and then a comparative experiment is carried out by solving e-health services in different ways. The results show that the fully connected neural network model has the best classification effect: the highest accuracy rate is about 93.7%, the highest precision rate is about 94.0%, the highest recall rate is about 95.3%, and the highest F1 score is about 94.6%. However, using artificial intelligence technology to solve electronic health services has great advantages, among which efficiency, assistance, and service satisfaction are all higher than 90%, which provides favorable technical support for electronic health services.

Artificial Intelligence, Real-World Automation and the Safety of Medicines

Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.