SERPICO 2022 Abstracts

Area 1 - SERPICO
Full Papers

Area 1 - SERPICO

Full Papers
Paper Nr: 1
Title:

Estimating Use of Short-term Asthma Reliever Inhalers from Electronic Prescription Records
Authors:

Holly Tibble, Aziz Sheikh and Athanasios Tsanas
Abstract: Asthma is a common chronic lung disease which can be effectively managed for most people through regular use of inhaled controller therapy. Short-acting Beta-2 Agonists (symptom relievers; SABA) may also be prescribed to be used as needed, however over-reliance may indicate poor symptom control. SABA usage can be estimated from refill rate observed in prescribing records. This study was a secondary analysis of a Scottish longitudinal dataset of linked primary and secondary care data. The aims of this study were to estimate the mean inhaled SABA dose per day for people diagnosed with asthma in a large EHR database, and to examine variation by demographic factors such as age, sex, and social deprivation. The prescriptions dataset contained over 40 million prescriptions between 2009 and 2017. 1,987,119 asthma reliever prescription records were identified (5% of all prescriptions), of which 97% were inhaled formulations. The Spearman correlation coefficient between subsequent years of aggregated (median) daily estimated SABA from one person-year to the next was 0.67. Higher median daily inhaled SABA amounts were statistically significantly associated (Wilcoxon Rank-Sum test p-value<0.05) with being older, male, living in an area of higher deprivation, and any non-inhaled SABA prescription.
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Paper Nr: 2
Title:

Comparative Analysis of Patient Distress in Opioid Treatment Programs using Natural Language Processing
Authors:

Fatemeh Shah-Mohammadi, Wanting Cui, Keren Bachi, Yasmin Hurd and Joseph Finkelstein
Abstract: Psychiatric and medical disorders, social and family environment, and legal distress are important determinants of distress that impact the effectiveness of the treatment in opioid treatment program (OTP). This information is not routinely captured in electronic health record, but may be found in clinical notes. This study aims to explore the feasibility and effectiveness of natural language processing (NLP) strategy for identifying legal, social, mental and medical determinates of distress along with emotional pain rooted in family environment from clinical narratives of patients with opioid addiction, and then using this information to find its impact on OTP outcomes. Analysis in this study showed that mental and legal distress significantly impact the result of the treatment in OTP.
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Paper Nr: 3
Title:

Exploring Feature Selection and Feature Transformation Techniques to Improve Telephone-based Biomedical Speech Signal Processing towards Parkinson’s Assessment
Authors:

Athanasios Tsanas and Siddharth Arora
Abstract: Clinical decision support tools mining speech signals for Parkinson’s Disease (PD) applications typically rely on relatively small numbers of participants, having collected data under highly controlled acoustic conditions. We recently reported on the Parkinson’s Voice Initiative (PVI), a large international project leading to the collection of 19,000+ sustained vowel phonations (control and PD groups) across seven countries, where participants were self-selected and provided phonations over the standard telephone network. In this study, we explored sustained vowels in a balanced subset of the US-speaking cohort in PVI comprising 3000 participants (1500 PD and 1500 controls). The aim was to investigate feature selection and feature transformation techniques towards improving binary differentiation of controls and PD and obtaining new insights in a lower dimensional space. We acoustically characterized each sustained vowel /a/ phonation using 307 dysphonia measures which had previously been successfully employed in speech-PD applications. We explored five different feature selection and two manifold embedding techniques to project data into new feature spaces which might be more predictive of the binary outcome, and presented those into a Random Forest. We assessed the performance of the resulting models using internal 10-fold Cross-Validation (CV). We report classification accuracy of 67% and provide tentative interpretation by comparing the different feature subsets identified using different methods. Collectively, these findings provide new insights towards developing parsimonious feature subsets to facilitate the development of a large-scale tool for PD screening at minimal cost using telephone-based sustained vowels.
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