Analyzing the Spread of Informatics with PubMed.

We analyzed PubMed citations since 1988 to explore the dissemination of medical/health informatics concepts between countries and across medical domains. We extracted countries from the PubMed author affiliation field to identify and analyze the top 10 informatics publishing countries. We found that the informatics publications are becoming more similar over time and that the rate of exchange across countries has increased with the introduction of e-publishing. Nonetheless, with the exception of machine learning, the impact of core informatics concepts on mainstream medicine and radiology publications remains small.

Automatic Extraction of Skin and Soft Tissue Infection Status from Clinical Notes.

The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g. is the infection purulent or non-purulent?) are not captured reliably in structured clinical data. With this work, we trained and evaluated a rule-based clinical natural language processing system using 6,576 manually annotated clinical notes derived from the United States Veterans Health Administration (VA) with the goal of automatically extracting and classifying SSTI subtypes from clinical notes. The trained system achieved mention- and document-level performance metrics of the range 0.39 to 0.80 for mention level classification and 0.49 to 0.98 for document level classification.

Designing an Interprofessional Online Course to Foster Learning Health Systems.

The Learning Health Systems (LHS) framework demonstrates the potential for iterative interrogation of health data in real time and implementation of insights into practice. Yet, the lack of appropriately skilled workforce results in an inability to leverage existing data to design innovative solutions. We developed a tailored professional development program to foster a skilled workforce. The short course is wholly online, for interdisciplinary professionals working in the digital health arena. To transform healthcare systems, the workforce needs an understanding of LHS principles, data driven approaches, and the need for diversly skilled learning communities that can tackle these complex problems together.

Telehealth in Cystic Fibrosis. A systematic review incorporating a novel scoring system and expert weighting to identify a 'top 10 manuscripts' to inform future best practices implementation.

The ongoing development and integration of telehealth within CF care has been accelerated in response to the Covid-19 pandemic, with many centres publishing their experiences. Now, as the restrictions of the pandemic ease, the use of telehealth appears to be waning, with many centres returning to routine traditional face-to-face services. For most, telehealth is not integrated into clinical care models, and there is a lack of guidance on how to integrate such a service into clinical care. The aims of this systematic review were to first identify manuscripts which may inform best CF telehealth practices, and second, to analyse these finding to determine how the CF community may use telehealth to improve care for patients, families, and Multidisciplinary Teams into the future. To achieve this, the PRISMA review methodology was utilised, in combination with a modified novel scoring system that consolidates expert weighting from key CF stakeholders, allowing for the manuscripts to be placed in a hierarchy in accordance with their scientific robustness. From the 39 found manuscripts, the top ten are presented and further analysed. The top ten manuscripts are exemplars of where telehealth is used effectively within CF care at this time, and demonstrate specific use cases of its potential best practices. However, there is a lack of guidance for implementation and clinical decision making, which remains an area for improvement. Thus, it is suggested that further work explores and provides guidance for standardised implementation into CF clinical practice.

Virtual monitoring in CF - the importance of continuous monitoring in a multi-organ chronic condition.

Cystic Fibrosis (CF) is a chronic life-limiting condition that affects multiple organs within the body. Patients must adhere to strict medication regimens, physiotherapy, diet, and attend regular clinic appointments to manage their condition effectively. This necessary but burdensome requirement has prompted investigations into how different digital health technologies can enhance current care by providing the opportunity to virtually monitor patients. This review explores how virtual monitoring has been harnessed for assessment or performance of physiotherapy/exercise, diet/nutrition, symptom monitoring, medication adherence, and wellbeing/mental-health in people with CF. This review will also briefly discuss the potential future of CF virtual monitoring and some common barriers to its current adoption and implementation within CF. Due to the multifaceted nature of CF, it is anticipated that this review will be relevant to not only the CF community, but also those investigating and developing digital health solutions for the management of other chronic diseases.

Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5%-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 1.1 million unlabelled clinical documents; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. This model was compared to a version without the pre-training step, and a previously published RoBERTa model pretrained on MIMIC III, which has demonstrated strong performance on other pharmacovigilance tasks. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.955 (95% CI 0.933 - 0.978) for the task of identifying discharge summaries containing ADR mentions, significantly outperforming the two comparator models.

Artificial Intelligence and Deep Learning for Rheumatologists.

Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer-assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People.

School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis. Twenty-five journal articles that included narrative descriptions of depressive symptoms in young people were included. A total of 60 depressive symptoms were identified and categorized into five dimensions: behavioral (n = 8), cognitive (n = 14), emotional (n = 15), interpersonal (n = 13), and somatic (n = 10). This comprehensive depression symptom taxonomy can help school nurses to identify young people who may experience depression and will support future research to better screen for depression.

Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center.

OBJECTIVE: To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. MATERIALS AND METHODS: We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. RESULTS: University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. DISCUSSION: Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. CONCLUSION: EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.

Telehealth and virtual health monitoring in cystic fibrosis.

PURPOSE OF REVIEW: At many institutions, the Covid-19 pandemic made it necessary to rapidly change the way services are provided to patients, including those with cystic fibrosis (CF). The purpose of this review is to explore the past, present and future of telehealth and virtual monitoring in CF and to highlight certain challenges/considerations in developing such services. RECENT FINDINGS: The Covid-19 pandemic has proven that telehealth and virtual monitoring are a feasible means for safely providing services to CF patients when traditional care is not possible. However, both telehealth and virtual monitoring can also provide further support in the future in a post-covid era through a hybrid-model incorporating traditional care, remote data collection and sophisticated platforms to manage and share data with CF teams. SUMMARY: We provide a detailed overview of telehealth and virtual monitoring including examples of how paediatric and adult CF services adapted to the need for rapid change. Such services have proven popular with people with CF meaning that co-design with stakeholders will likely improve systems further. In the future, telehealth and virtual monitoring will become more sophisticated by harnessing increasingly powerful technologies such as artificial intelligence, connected monitoring devices and wearables. In this review, we harmonise definitions and terminologies before highlighting considerations and limitations for the future of telehealth and virtual monitoring in CF.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

Importance: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models. Objective: To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model. Design, Setting, and Participants: This was a retrospective cohort study that developed risk prediction models for 30-day readmission among all inpatients discharged from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of AMI who were not transferred from another facility. The model was externally validated at Dartmouth-Hitchcock Medical Center from April 2, 2011, to December 31, 2016. Data analysis occurred between January 4, 2019, and November 15, 2020. Exposures: Acute myocardial infarction that required hospital admission. Main Outcomes and Measures: The main outcome was thirty-day hospital readmission. A total of 141 candidate variables were considered from administrative codes, medication orders, and laboratory tests. Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts. Results: The final Vanderbilt University Medical Center cohort included 6163 unique patients, among whom the mean (SD) age was 67 (13) years, 4137 were male (67.1%), 1019 (16.5%) were Black or other race, and 933 (15.1%) were rehospitalized within 30 days. The final Dartmouth-Hitchcock Medical Center cohort included 4024 unique patients, with mean (SD) age of 68 (12) years; 2584 (64.2%) were male, 412 (10.2%) were rehospitalized within 30 days, and most of the cohort were non-Hispanic and White. The final test set AUROC performance was between 0.686 to 0.695 for the parametric models and 0.686 to 0.704 for the nonparametric models. In the validation cohort, AUROC performance was between 0.558 to 0.655 for parametric models and 0.606 to 0.608 for nonparametric models. Conclusions and Relevance: In this study, 5 machine learning models were developed and externally validated to predict 30-day readmission AMI hospitalization. These models can be deployed within an EHR using routinely collected data.

Trends in Low-Value Carotid Imaging in the Veterans Health Administration From 2007 to 2016.

Importance: As part of the Choosing Wisely campaign, primary care, surgery, and neurology societies have identified carotid imaging ordered for screening, preoperative evaluation, and syncope as frequently low value. Objective: To determine the changes in overall and indication-specific rates of carotid imaging following Choosing Wisely recommendations. Design, Setting, and Participants: This serial cross-sectional study compared annual rates of carotid imaging before Choosing Wisely recommendations (ie, 2007 to 2012) and after (ie, 2013 to 2016) among adults receiving care in the Veterans Health Administration (VHA) national health system. Data analysis was performed from April 10, 2019, to November 27, 2019. Exposures: Release of the Choosing Wisely recommendations. Main Outcomes and Measures: Annual rates of overall imaging, imaging ordered for stroke workup, imaging ordered for low-value indications (ie, screening owing to carotid bruit, preoperative evaluation, and syncope). Indications were identified using a text lexicon algorithm based on electronic health record review of a stratified random sample of 1000 free-text imaging orders. The subsequent performance of carotid procedures within 6 months after carotid imaging was assessed. Results: Between 2007 and 2016, 809 071 carotid imaging examinations were identified (mean [SD] age of patients undergoing imaging, 69 [10] years; 776 632 [96%] men), of which 201 467 images (24.9%) were ordered for low-value indications (67 064 [8.2%] for carotid bruit, 25 032 [3.1%] for preoperative evaluation, and 109 400 [13.5%] for syncope), 257 369 (31.8%) for stroke workup, and 350 235 (43.3%) for other indications. Imaging for carotid bruits declined across the study period while there was no significant change in imaging for syncope or preoperative evaluation. Compared with the 6 years before, during the 4 years following Choosing Wisely recommendations, there was no change in the trend for syncope, a small decline in preoperative imaging (post-Choosing Wisely trend, -0.1 [95% CI, -0.1 to <-0.1] images per 10 000 veterans), and a continued but less steep decline in imaging for carotid bruits (post-Choosing Wisely trend, -0.3 [95% CI, -0.3 to -0.2] images per 10 000 veterans). During the study period, 17 689 carotid procedures were identified, of which 3232 (18.3%) were preceded by carotid imaging ordered for low-value indications. Conclusions and Relevance: These findings suggest that Choosing Wisely recommendations were not associated with a meaningful change in low-value carotid imaging in a national integrated health system. To reduce low-value testing and utilization cascades, interventions targeting ordering clinicians are needed to augment the impact of public awareness campaigns.

Portable Automated Surveillance of Surgical Site Infections Using Natural Language Processing: Development and Validation.

OBJECTIVES: We present the development and validation of a portable NLP approach for automated surveillance of SSIs. SUMMARY OF BACKGROUND DATA: The surveillance of SSIs is labor-intensive limiting the generalizability and scalability of surgical quality surveillance programs. METHODS: We abstracted patient clinical text notes after surgical procedures from 2 independent healthcare systems using different electronic healthcare records. An SSI detected as part of the American College of Surgeons' National Surgical Quality Improvement Program was used as the reference standard. We developed a rules-based NLP system (Easy Clinical Information Extractor [CIE]-SSI) for operative event-level detection of SSIs using an training cohort (4574 operative events) from 1 healthcare system and then conducted internal validation on a blind cohort from the same healthcare system (1850 operative events) and external validation on a blind cohort from the second healthcare system (15,360 operative events). EasyCIE-SSI performance was measured using sensitivity, specificity, and area under the receiver-operating-curve (AUC). RESULTS: The prevalence of SSI was 4% and 5% in the internal and external validation corpora. In internal validation, EasyCIE-SSI had a sensitivity, specificity, AUC of 94%, 88%, 0.912 for the detection of SSI, respectively. In external validation, EasyCIE-SSI had sensitivity, specificity, AUC of 79%, 92%, 0.852 for the detection of SSI, respectively. The sensitivity of EasyCIE-SSI decreased in clean, skin/subcutaneous, and outpatient procedures in the external validation compared to internal validation. CONCLUSION: Automated surveillance of SSIs can be achieved using NLP of clinical notes with high sensitivity and specificity.

Comparative Effectiveness of Carotid Endarterectomy vs Initial Medical Therapy in Patients With Asymptomatic Carotid Stenosis.

Importance: Carotid endarterectomy (CEA) among asymptomatic patients involves a trade-off between a higher short-term perioperative risk in exchange for a lower long-term risk of stroke. The clinical benefit observed in randomized clinical trials (RCTs) may not extend to real-world practice. Objective: To examine whether early intervention (CEA) was superior to initial medical therapy in real-world practice in preventing fatal and nonfatal strokes among patients with asymptomatic carotid stenosis. Design, Setting, and Participants: This comparative effectiveness study was conducted from August 28, 2018, to March 2, 2020, using the Corporate Data Warehouse, Suicide Data Repository, and other databases of the US Department of Veterans Affairs. Data analyzed were those of veterans of the US Armed Forces aged 65 years or older who received carotid imaging between January 1, 2005, and December 31, 2009. Patients without a carotid imaging report, those with carotid stenosis of less than 50% or hemodynamically insignificant stenosis, and those with a history of stroke or transient ischemic attack in the 6 months before index imaging were excluded. A cohort of patients who received initial medical therapy and a cohort of similar patients who received CEA were constructed and followed up for 5 years. The target trial method was used to compute weighted Kaplan-Meier curves and estimate the risk of fatal and nonfatal strokes in each cohort in the pragmatic sample across 5 years of follow-up. This analysis was repeated after restricting the sample to patients who met RCT inclusion criteria. Cumulative incidence functions for fatal and nonfatal strokes were estimated, accounting for nonstroke deaths as competing risks in both the pragmatic and RCT-like samples. Exposures: Receipt of CEA vs initial medical therapy. Main Outcomes and Measures: Fatal and nonfatal strokes. Results: Of the total 5221 patients, 2712 (51.9%; mean [SD] age, 73.6 [6.0] years; 2678 men [98.8%]) received CEA and 2509 (48.1%; mean [SD] age, 73.6 [6.0] years; 2479 men [98.8%]) received initial medical therapy within 1 year after the index carotid imaging. The observed rate of stroke or death (perioperative complications) within 30 days in the CEA cohort was 2.5% (95% CI, 2.0%-3.1%). The 5-year risk of fatal and nonfatal strokes was lower among patients randomized to CEA compared with patients randomized to initial medical therapy (5.6% vs 7.8%; risk difference, -2.3%; 95% CI, -4.0% to -0.3%). In an analysis that incorporated the competing risk of death, the risk difference between the 2 cohorts was lower and not statistically significant (risk difference, -0.8%; 95% CI, -2.1% to 0.5%). Among patients who met RCT inclusion criteria, the 5-year risk of fatal and nonfatal strokes was 5.5% (95% CI, 4.5%-6.5%) among patients randomized to CEA and was 7.6% (95% CI, 5.7%-9.5%) among those randomized to initial medical therapy (risk difference, -2.1%; 95% CI, -4.4% to -0.2%). Accounting for competing risks resulted in a risk difference of -0.9% (95% CI, -2.9% to 0.7%) that was not statistically significant. Conclusions and Relevance: This study found that the absolute reduction in the risk of fatal and nonfatal strokes associated with early CEA was less than half the risk difference in trials from 20 years ago and was no longer statistically significant when the competing risk of nonstroke deaths was accounted for in the analysis. Given the nonnegligible perioperative 30-day risks and the improvements in stroke prevention, medical therapy may be an acceptable therapeutic strategy.

Impact of Different Electronic Cohort Definitions to Identify Patients With Atrial Fibrillation From the Electronic Medical Record.

Background Electronic medical records (EMRs) allow identification of disease-specific patient populations, but varying electronic cohort definitions could result in different populations. We compared the characteristics of an electronic medical record-derived atrial fibrillation (AF) patient population using 5 different electronic cohort definitions. Methods and Results Adult patients with at least 1 AF billing code from January 1, 2010, to December 31, 2017, were included. Based on different electronic cohort definitions, we trained 5 different logistic regression models using a labeled training data set (n=786). Each model yielded a predicted probability; patients were classified as having AF if the probability was higher than a specified cut point. Test characteristics were calculated for each model. These models were then applied to the full cohort and resulting characteristics were compared. In the training set, the comprehensive model (including demographics, billing codes, and natural language processing results) performed best, with an area under the curve of 0.89, sensitivity of 0.90, and specificity of 0.87. Among a candidate population (n=22 000), the proportion of patients identified as having AF varied from 61% in the model using diagnosis or procedure International Classification of Diseases (ICD) billing codes to 83% in the model using natural language processing of clinical notes. Among identified AF patients, the proportion of patients with a CHA2DS2-VASc score ≥2 varied from 69% to 85%; oral anticoagulant treatment rates varied from 50% to 66% depending on the model. Conclusions Different electronic cohort definitions result in substantially different AF study samples. This difference threatens the quality and reproducibility of electronic medical record-based research and quality initiatives.

Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports.

BACKGROUND: Despite advances in natural language processing (NLP), extracting information from clinical text is expensive. Interactive tools that are capable of easing the construction, review, and revision of NLP models can reduce this cost and improve the utility of clinical reports for clinical and secondary use. OBJECTIVES: We present the design and implementation of an interactive NLP tool for identifying incidental findings in radiology reports, along with a user study evaluating the performance and usability of the tool. METHODS: Expert reviewers provided gold standard annotations for 130 patient encounters (694 reports) at sentence, section, and report levels. We performed a user study with 15 physicians to evaluate the accuracy and usability of our tool. Participants reviewed encounters split into intervention (with predictions) and control conditions (no predictions). We measured changes in model performance, the time spent, and the number of user actions needed. The System Usability Scale (SUS) and an open-ended questionnaire were used to assess usability. RESULTS: Starting from bootstrapped models trained on 6 patient encounters, we observed an average increase in F1 score from 0.31 to 0.75 for reports, from 0.32 to 0.68 for sections, and from 0.22 to 0.60 for sentences on a held-out test data set, over an hour-long study session. We found that tool helped significantly reduce the time spent in reviewing encounters (134.30 vs. 148.44 seconds in intervention and control, respectively), while maintaining overall quality of labels as measured against the gold standard. The tool was well received by the study participants with a very good overall SUS score of 78.67. CONCLUSION: The user study demonstrated successful use of the tool by physicians for identifying incidental findings. These results support the viability of adopting interactive NLP tools in clinical care settings for a wider range of clinical applications.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data.

OBJECTIVE: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. METHODS: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. RESULTS: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.

Determining Onset for Familial Breast and Colorectal Cancer from Family History Comments in the Electronic Health Record.

Background. Family health history (FHH) can be used to identify individuals at elevated risk for familial cancers. Risk criteria for common cancers rely on age of onset, which is documented inconsistently as structured and unstructured data in electronic health records (EHRs). Objective. To investigate a natural language processing (NLP) approach to extract age of onset and age of death from free-text EHR fields. Methods. Using 474,651 FHH entries from 89,814 patients, we investigated two methods - frequent patterns (baseline) and NLP classifier. Results. For age of onset, the NLP classifier outperformed the baseline in precision (96% vs. 83%; 95% CI [94, 97] and [80, 86]) with equivalent recall (both 93%; 95% CI [91, 95]). When applied to the full dataset, the NLP approach increased the percentage of FHH entries for which cancer risk criteria could be applied from 10% to 15%. Conclusion. NLP combined with structured data may improve the computation of familial cancer risk criteria for various use cases.