Towards Predicting Smoking Events for Just-in-time Interventions.

Consumer-grade heart rate (HR) sensors are widely used for tracking physical and mental health status. We explore the feasibility of using Polar H10 electrocardiogram (ECG) sensor to detect and predict cigarette smoking events in naturalistic settings with several machine learning approaches. We have collected and analyzed data for 28 participants observed over a two-week period. We found that using bidirectional long short-term memory (BiLSTM) with ECG-derived and GPS location input features yielded the highest mean accuracy of 69% for smoking event detection. For predicting smoking events, the highest accuracy of 67% was achieved using the fine-tuned LSTM approach. We also found a significant correlation between accuracy and the number of smoking events available from each participant. Our findings indicate that both detection and prediction of smoking events are feasible but require an individualized approach to training the models, particularly for prediction.

Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition.

OBJECTIVE: : Developing clinical natural language processing systems often requires access to many clinical documents, which are not widely available to the public due to privacy and security concerns. To address this challenge, we propose to develop methods to generate synthetic clinical notes and evaluate their utility in real clinical natural language processing tasks. MATERIALS AND METHODS: : We implemented 4 state-of-the-art text generation models, namely CharRNN, SegGAN, GPT-2, and CTRL, to generate clinical text for the History and Present Illness section. We then manually annotated clinical entities for randomly selected 500 History and Present Illness notes generated from the best-performing algorithm. To compare the utility of natural and synthetic corpora, we trained named entity recognition (NER) models from all 3 corpora and evaluated their performance on 2 independent natural corpora. RESULTS: : Our evaluation shows GPT-2 achieved the best BLEU (bilingual evaluation understudy) score (with a BLEU-2 of 0.92). NER models trained on synthetic corpus generated by GPT-2 showed slightly better performance on 2 independent corpora: strict F1 scores of 0.709 and 0.748, respectively, when compared with the NER models trained on natural corpus (F1 scores of 0.706 and 0.737, respectively), indicating the good utility of synthetic corpora in clinical NER model development. In addition, we also demonstrated that an augmented method that combines both natural and synthetic corpora achieved better performance than that uses the natural corpus only. CONCLUSIONS: : Recent advances in text generation have made it possible to generate synthetic clinical notes that could be useful for training NER models for information extraction from natural clinical notes, thus lowering the privacy concern and increasing data availability. Further investigation is needed to apply this technology to practice.

Comparing Existing Resources to Represent Dietary Supplements.

Dietary supplements, often considered as food, are widely consumed despite of limited knowledge around their safety/efficacy and any well-established regulatory policies, unlike their drug counterparts. Informatics methods may be useful in filling this knowledge gap, however, the lack of standardized representation of DS hinders this progress. In this pilot study, five electronic DS resources, i.e., NM, DSID & NHPID (ingredient level) and DSLD & LNHPD (product level), were evaluated and compared both quantitatively and qualitatively employing four phases. Essential data elements needed for comprehensive DS representation were compiled based on LanguaL code (food) & AHFSA (drugs) guidelines and employed as a check-list. We further investigated the completeness of DS representation by incorporating Ginseng and Fish oil as examples. We found fragmented and inconsistent distribution of DS representation in terms of essential data elements across five resources. This study provides a preliminary platform for development of standardized DS terminology/ontology model.

Automated processing of electronic medical records is a reliable method of determining aspirin use in populations at risk for cardiovascular events.

BACKGROUND: Low-dose aspirin reduces cardiovascular risk; however, monitoring over-the-counter medication use relies on the time-consuming and costly manual review of medical records. Our objective is to validate natural language processing (NLP) of the electronic medical record (EMR) for extracting medication exposure and contraindication information. METHODS: The text of EMRs for 499 patients with type 2 diabetes was searched using NLP for evidence of aspirin use and its contraindications. The results were compared to a standardised manual records review. RESULTS: Of the 499 patients, 351 (70%) were using aspirin and 148 (30%) were not, according to manual review. NLP correctly identified 346 of the 351 aspirin-positive and 134 of the 148 aspirin-negative patients, indicating a sensitivity of 99% (95% CI 97-100) and specificity of 91% (95% CI 88-97). Of the 148 aspirin-negative patients, 66 (45%) had contraindications and 82 (55%) did not, according to manual review. NLP search for contraindications correctly identified 61 of the 66 patients with contraindications and 58 of the 82 patients without, yielding a sensitivity of 92% (95% CI 84-97) and a specificity of 71% (95% CI 60-80). CONCLUSIONS: NLP of the EMR is accurate in ascertaining documented aspirin use and could potentially be used for epidemiological research as a source of cardiovascular risk factor information.

A computerized technique to assess language use patterns in patients with frontotemporal dementia.

Frontotemporal lobar degeneration (FTLD) is a neurodegenerative disorder that affects language. We applied a computerized information-theoretic technique to assess the type and severity of language-related FTLD symptoms. Audio-recorded samples of 48 FTLD patients from three participating medical centers were elicited using the Cookie Theft picture stimulus. The audio was transcribed and analyzed by calculating two measures: a perplexity index and an out-of-vocabulary (OOV) rate. The perplexity index represents the degree of deviation in word patterns used by FTLD patients compared to patterns of healthy adults. The OOV rate represents the proportion of words used by FTLD patients that were not used by the healthy speakers to describe the stimulus. In this clinically well-characterized cohort, the perplexity index and the OOV rate were sensitive to spontaneous language manifestations of semantic dementia and the distinction between semantic dementia and progressive logopenic aphasia variants of FTLD. Our study not only supports a novel technique for the characterization of language-related symptoms of FTLD in clinical trial settings, it also validates the basis for the clinical diagnosis of semantic dementia as a distinct syndrome.