High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.

We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.

Deploying a national clinical text processing infrastructure.

OBJECTIVES: Clinical text processing offers a promising avenue for improving multiple aspects of healthcare, though operational deployment remains a substantial challenge. This case report details the implementation of a national clinical text processing infrastructure within the Department of Veterans Affairs (VA). METHODS: Two foundational use cases, cancer case management and suicide and overdose prevention, illustrate how text processing can be practically implemented at scale for diverse clinical applications using shared services. RESULTS: Insights from these use cases underline both commonalities and differences, providing a replicable model for future text processing applications. CONCLUSIONS: This project enables more efficient initiation, testing, and future deployment of text processing models, streamlining the integration of these use cases into healthcare operations. This project implementation is in a large integrated health delivery system in the United States, but we expect the lessons learned to be relevant to any health system, including smaller local and regional health systems in the United States.

Sarcoidosis rates in BCG-vaccinated and unvaccinated young adults: A natural experiment using Danish registers.

OBJECTIVES: Sarcoidosis may have an infectious trigger, including Mycobacterium spp. The Bacille Calmette-Guérin (BCG) vaccine provides partial protection against tuberculosis and induces trained immunity. We examined the incidence rate (IR) of sarcoidosis in Danish individuals born during high BCG vaccine uptake (born before 1976) compared with individuals born during low BCG vaccine uptake (born in or after 1976). METHODS: We performed a quasi-randomized registry-based incidence study using data from the Danish Civil Registration System and the Danish National Patient Registry between 1995 and 2016. We included individuals aged 25-35 years old and born between 1970 and 1981. Using Poisson regression models, we calculated the incidence rate ratio (IRR) of sarcoidosis in individuals born during low BCG vaccine uptake versus high BCG vaccine uptake, adjusting for age and calendar year (separately for men and women). RESULTS: The IR of sarcoidosis was increased for individuals born during low BCG vaccine uptake compared with individuals born during high BCG vaccine uptake, which was largely attributed to men. The IRR of sarcoidosis for men born during low BCG vaccine uptake versus high BCG vaccine uptake was 1.22 (95% confidence interval [CI] 1.02-1.45). In women, the IRR was 1.08 (95% CI 0.88-1.31). CONCLUSION: In this quasi-experimental study that minimizes confounding, the time period with high BCG vaccine uptake was associated with a lower incidence rate of sarcoidosis in men, with a similar effect seen in women that did not reach significance. Our findings support a potential protective effect of BCG vaccination against the development of sarcoidosis. Future interventional studies for high-risk individuals could be considered.

Revelations from a Machine Learning Analysis of the Most Downloaded Articles Published in Journal of Palliative Medicine 1999-2018.

The Journal of Palliative Medicine (JPM) is globally recognized as a leading interdisciplinary peer-reviewed palliative care journal providing balanced information that informs and improves the practice of palliative care. JPM shapes the values, integrity, and standards of the subspecialty of palliative medicine by what it chooses to publish. The global JPM readership chooses to download the articles that are of most relevance and utility to them. Utilizing machine learning methods, the top 100 most downloaded articles in JPM were analyzed to gain a better understanding of any latent trends and patterns in the topics between 1999 and 2018. The top five topic themes identified in the first decade were different from the ones identified in the second decade of publication. There is evidence of differentiation and maturation of the field in the context of comprehensive health care. Although noncancer serious illnesses have still not risen to the same prominence as cancer palliation, there is a directional quality to the emerging evidence as it pertains to cardiac, respiratory, neurological, renal, and other etiologies. Across both decades under study, there was persistent evidence of the importance of understanding and managing the mental health care needs of seriously ill patients and their families. A cause for concern is that the word "spirituality" was prominent in the first decade and was lacking in the second. Future palliative care clinical and research initiatives should focus on its development as an essential interprofessional and medical subspecialty germane to all types of serious illnesses and across all venues.

Development and validation of MedDRA Tagger: a tool for extraction and structuring medical information from clinical notes.

Rapid and automated extraction of clinical information from patients' notes is a desirable though difficult task. Natural language processing (NLP) and machine learning have great potential to automate and accelerate such applications, but developing such models can require a large amount of labeled clinical text, which can be a slow and laborious process. To address this gap, we propose the MedDRA tagger, a fast annotation tool that makes use of industrial level libraries such as spaCy, biomedical ontologies and weak supervision to annotate and extract clinical concepts at scale. The tool can be used to annotate clinical text and obtain labels for training machine learning models and further refine the clinical concept extraction performance, or to extract clinical concepts for observational study purposes. To demonstrate the usability and versatility of our tool, we present three different use cases: we use the tagger to determine patients with a primary brain cancer diagnosis, we show evidence of rising mental health symptoms at the population level and our last use case shows the evolution of COVID-19 symptomatology throughout three waves between February 2020 and October 2021. The validation of our tool showed good performance on both specific annotations from our development set (F1 score 0.81) and open source annotated data set (F1 score 0.79). We successfully demonstrate the versatility of our pipeline with three different use cases. Finally, we note that the modular nature of our tool allows for a straightforward adaptation to another biomedical ontology. We also show that our tool is independent of EHR system, and as such generalizable.

Sarcoidosis incidence after mTOR inhibitor treatment.

OBJECTIVE: Mechanistic target of rapamycin (mTOR) inhibitors are effective in animal models of granulomatous disease, but their benefit in sarcoidosis patients is unknown. We evaluated the incidence of sarcoidosis in patients treated with mTOR inhibitors versus calcineurin inhibitors. METHODS: This was a cohort study using the Optum Clinformatics® Data Mart (CDM) Database (2003-2019), IBM® MarketScan® Research Database (2006-2016), and Danish health and administrative registries (1996-2018). Patients aged ≥18 years with ≥1 year continuous enrollment before and after kidney, liver, heart, or lung transplant treated with an mTOR inhibitor or calcineurin inhibitor were included. Patients diagnosed with sarcoidosis before, or up to 90 days after, transplant were excluded. The incidence of sarcoidosis by treatment group was calculated. RESULTS: In the Optum CDM/IBM MarketScan cohort, 1,898 patients were treated with an mTOR inhibitor (mean age 49 years; 34% female) and 9,894 patients were treated with a calcineurin inhibitor (mean age 50 years; 37% female). The mean follow-up in the mTOR inhibitor group was 1.1 years, with no incident sarcoidosis diagnosed. In the calcineurin inhibitor group, the mean follow-up was 2.2 years, with 12 incident sarcoidosis cases diagnosed. In the Danish cohort, 230 patients were treated with an mTOR inhibitor (mean age 49; 45% female), with no incident sarcoidosis diagnosed. There were 3,411 patients treated with a calcineurin inhibitor (mean age 45; 40% female), with 10 incident cases of sarcoidosis diagnosed. CONCLUSIONS: This study indicates a potential protective effect of mTOR inhibitor treatment compared with calcineurin inhibitor treatment against the development of sarcoidosis.

Risk of primary urological and genital cancers following incident breast cancer: a Danish population-based cohort study.

PURPOSE: The prevalence of breast cancer survivors has increased due to dissemination of population-based mammographic screening and improved treatments. Recent changes in anti-hormonal therapies for breast cancer may have modified the risks of subsequent urological and genital cancers. We examine the risk of subsequent primary urological and genital cancers in patients with incident breast cancer compared with risks in the general population. METHODS: Using population-based Danish medical registries, we identified a cohort of women with primary breast cancer (1990-2017). We followed them from one year after their breast cancer diagnosis until any subsequent urological or genital cancer diagnosis. We computed incidence rates and standardized incidence ratios (SIRs) with 95% confidence intervals (CIs) as the observed number of cancers relative to the expected number based on national incidence rates (by sex, age, and calendar year). RESULTS: Among 84,972 patients with breast cancer (median age 61 years), we observed 623 urological cancers and 1397 genital cancers during a median follow-up of 7.4 years. The incidence rate per 100,000 person-years was stable during follow-up (83 for urological cancers and 176 for genital cancers). The SIR was increased for ovarian cancer (1.37, 95% CI 1.23-1.52) and uterine cancer (1.37, 95% CI 1.25-1.50), but only during the pre-aromatase inhibitor era (before 2007). Moreover, the SIR of kidney cancer was increased (1.52, 95% CI 1.15-1.97), but only during 2007-2017. The SIR for urinary bladder cancer was marginally increased (1.15, 95% CI 1.04-1.28) with no temporal effects. No associations were observed for cervical cancer. CONCLUSION: Breast cancer survivors had higher risks of uterine and ovarian cancer than expected, but only before 2007, and of kidney cancer, but only after 2007. The risk of urinary bladder cancer was moderately increased without temporal effects, and we observed no association with cervical cancer.

Risk of primary gastrointestinal cancers following incident non-metastatic breast cancer: a Danish population-based cohort study.

OBJECTIVE: We examined the risk of primary gastrointestinal cancers in women with breast cancer and compared this risk with that of the general population. DESIGN: Using population-based Danish registries, we conducted a cohort study of women with incident non-metastatic breast cancer (1990-2017). We computed cumulative cancer incidences and standardised incidence ratios (SIRs). RESULTS: Among 84 972 patients with breast cancer, we observed 2340 gastrointestinal cancers. After 20 years of follow-up, the cumulative incidence of gastrointestinal cancers was 4%, driven mainly by colon cancers. Only risk of stomach cancer was continually increased beyond 1 year following breast cancer. The SIR for colon cancer was neutral during 2-5 years of follow-up and approximately 1.2-fold increased thereafter. For cancer of the oesophagus, the SIR was increased only during 6-10 years. There was a weak association with pancreas cancer beyond 10 years. Between 1990-2006 and 2007-2017, the 1-10 years SIR estimate decreased and reached unity for upper gastrointestinal cancers (oesophagus, stomach, and small intestine). For lower gastrointestinal cancers (colon, rectum, and anal canal), the SIR estimate was increased only after 2007. No temporal effects were observed for the remaining gastrointestinal cancers. Treatment effects were negligible. CONCLUSION: Breast cancer survivors were at increased risk of oesophagus and stomach cancer, but only before 2007. The risk of colon cancer was increased, but only after 2007.

Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data.

OBJECTIVES: Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data. MATERIALS AND METHODS: We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results Program (SEER). We identified de novo MBC patients from CCR and extracted information on distant recurrences from patient notes in EMR. Furthermore, we trained a regularized logistic regression model for recurrent MBC classification and evaluated its performance on a gold standard set of 146 patients. RESULTS: There were 11 459 breast cancer patients in total and the median follow-up time was 96.3 months. We identified 1886 MBC patients, 512 (27.1%) of whom were de novo MBC patients and 1374 (72.9%) were recurrent MBC patients. Our final MBC classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.917, with sensitivity 0.861, specificity 0.878, and accuracy 0.870. DISCUSSION AND CONCLUSION: To enable population-based research on MBC, we developed a framework for retrospective case detection combining EMR and CCR data. Our classifier achieved good AUC, sensitivity, and specificity without expert-labeled examples.

New Paradigms for Patient-Centered Outcomes Research in Electronic Medical Records: An Example of Detecting Urinary Incontinence Following Prostatectomy.

INTRODUCTION: National initiatives to develop quality metrics emphasize the need to include patient-centered outcomes. Patient-centered outcomes are complex, require documentation of patient communications, and have not been routinely collected by healthcare providers. The widespread implementation of electronic medical records (EHR) offers opportunities to assess patient-centered outcomes within the routine healthcare delivery system. The objective of this study was to test the feasibility and accuracy of identifying patient centered outcomes within the EHR. METHODS: Data from patients with localized prostate cancer undergoing prostatectomy were used to develop and test algorithms to accurately identify patient-centered outcomes in post-operative EHRs - we used urinary incontinence as the use case. Standard data mining techniques were used to extract and annotate free text and structured data to assess urinary incontinence recorded within the EHRs. RESULTS: A total 5,349 prostate cancer patients were identified in our EHR-system between 1998-2013. Among these EHRs, 30.3% had a text mention of urinary incontinence within 90 days post-operative compared to less than 1.0% with a structured data field for urinary incontinence (i.e. ICD-9 code). Our workflow had good precision and recall for urinary incontinence (positive predictive value: 0.73 and sensitivity: 0.84). DISCUSSION: Our data indicate that important patient-centered outcomes, such as urinary incontinence, are being captured in EHRs as free text and highlight the long-standing importance of accurate clinician documentation. Standard data mining algorithms can accurately and efficiently identify these outcomes in existing EHRs; the complete assessment of these outcomes is essential to move practice into the patient-centered realm of healthcare.

Detecting unplanned care from clinician notes in electronic health records.

PURPOSE: Reduction in unplanned episodes of care, such as emergency department visits and unplanned hospitalizations, are important quality outcome measures. However, many events are only documented in free-text clinician notes and are labor intensive to detect by manual medical record review. METHODS: We studied 308,096 free-text machine-readable documents linked to individual entries in our electronic health records, representing care for patients with breast, GI, or thoracic cancer, whose treatment was initiated at one academic medical center, Stanford Health Care (SHC). Using a clinical text-mining tool, we detected unplanned episodes documented in clinician notes (for non-SHC visits) or in coded encounter data for SHC-delivered care and the most frequent symptoms documented in emergency department (ED) notes. RESULTS: Combined reporting increased the identification of patients with one or more unplanned care visits by 32% (15% using coded data; 20% using all the data) among patients with 3 months of follow-up and by 21% (23% using coded data; 28% using all the data) among those with 1 year of follow-up. Based on the textual analysis of SHC ED notes, pain (75%), followed by nausea (54%), vomiting (47%), infection (36%), fever (28%), and anemia (27%), were the most frequent symptoms mentioned. Pain, nausea, and vomiting co-occur in 35% of all ED encounter notes. CONCLUSION: The text-mining methods we describe can be applied to automatically review free-text clinician notes to detect unplanned episodes of care mentioned in these notes. These methods have broad application for quality improvement efforts in which events of interest occur outside of a network that allows for patient data sharing.

Developing health surveillance networks: An adaptive approach.

The research literature on public health information and communication networks shows enormous promise and tremendous obstacles. There is a great deal of evidence to suggest that when electronic health information systems are widely employed, and clinical information is easily shared, trained individuals can track and monitor health status, and avert acute events that can potentially effect an individual or a population. However, the research literature also leaves unresolved important questions about effectiveness vs. efficacy: that is, whether health information sharing can achieve compliance on a large scale, particularly across social, political, economic and geographic boundaries. For this reason, we propose adaptive health care information networks to collect, process and disseminate health information and reduce medical errors. This research assesses existing electronic health monitoring initiatives in the United States and worldwide, discusses their progress and limitations. We identify how health care information networks could be improved by the application of innovative theories and technologies, such as complex adaptive theory, expert systems, and grid technology.

The state of the art in the reduction of medical errors.

The IOM report, To Err is Human, Building a Better Health System, galvanized public and political attention to the prevalence of medical errors in the Unites States. The IOM set a clear goal, "given the current knowledge about the magnitude of the problem, the committee believes it would be irresponsible to expect anything less than a 50 percent reduction over five years." As part of the IOM's four-part strategy was a recommendation that error reporting systems be established. No one denies that errors that occur in medicine can not be reduced if they cannot be defined. To achieve this goal of reducing errors, we have established a definition of a "medical error", described the current taxonomies that have been created over the last five years for their classification, and suggested a conceptual model for designing and testing a medical error reporting system. A system that facilitates identification, relies on health professionals and electronic repositories of clinical information to report events, and tracks and monitors medical errors, reliably, efficiently, and accurately is the objective of our design. Our next step is to implement, test, and evaluate this system based on our research.