IMPORTANCE Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings.OBJECTIVE To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems. DESIGN, SETTING, AND PARTICIPANTSFor this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years' worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019. MAIN OUTCOMES AND MEASURES The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site. RESULTS Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%])included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance. CONCLUSIONS AND RELEVANCEAcross 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect (continued) Key Points Question Can a process for training machine-learning algorithms based on electronic health records identify individuals at increased risk of suicide attempts across independent health care systems? Abstract (contin...

Cervical cancer is the leading cause of cancer mortality in India, accounting for 17% of all cancer deaths among women aged 30 to 69 years. At current incidence rates, the annual burden of new cases in India is projected to increase to 225,000 by 2025, but there are few large-scale, organized cervical cancer prevention programs in the country. We conducted a review of the cervical cancer prevention research literature and programmatic experiences in India to summarize the current state of knowledge and practices and recommend research priorities to address the gap in services. We found that research and programs in India have demonstrated the feasibility and acceptability of cervical cancer prevention efforts and that screening strategies requiring minimal additional human resources and laboratory infrastructure can reduce morbidity and mortality. However, additional evidence generated through implementation science research is needed to ensure that cervical cancer prevention efforts have the desired impact and are cost-effective. Specifically, implementation science research is needed to understand individual-and community-level barriers to screening and diagnostic and treatment services; to improve health care worker performance; to strengthen links among screening, diagnosis, and treatment; and to determine optimal program design, outcomes, and costs. With a quarter of the global burden of cervical cancer in India, there is no better time than now to translate research findings to practice. Implementation science can help ensure that investments in cervical cancer prevention and control result in the greatest impact. The Oncologist 2013;18:1285-1297 Implications for Practice: Considerable research has been conducted on the prevention of cervical cancer in India. The majority of studies have focused on the feasibility, acceptability, and impact of secondary prevention of cancer through screening, early detection, and treatment. Despite this evidence, there have been few government-led public health programs to prevent and control cervical cancer. The primary goals of this review are to summarize the lessons learned from cervical cancer prevention research and pilot programs in India and to identify research priorities to facilitate the translation of existing knowledge into policies and programs that advance cervical cancer prevention.

Objective Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions. Materials and Methods We propose a temporally enhanced variant of the random forest (RF) model—Omni-Temporal Balanced Random Forests (OT-BRFs)—that incorporates temporal information in every tree within the forest. We develop and validate this model using longitudinal EHRs and clinician notes from the Mass General Brigham Health System recorded between 1998 and 2018, and compare its performance to a baseline Naive Bayes Classifier and 2 standard versions of balanced RFs. Results Temporal variables were found to be associated with suicide risk: Elevated suicide risk was observed in individuals with a higher total number of visits as well as those with a low rate of visits over time, while lower suicide risk was observed in individuals with a longer period of EHR coverage. RF models were more accurate than Naive Bayesian classifiers at predicting suicide risk in advance (area under the receiver operating curve = 0.824 vs. 0.754, respectively). The proposed OT-BRF model performed best among all RF approaches, yielding a sensitivity of 0.339 at 95% specificity, compared to 0.290 and 0.286 for the other 2 RF models. Temporal variables were assigned high importance by the models that incorporated them. Discussion We demonstrate that temporal variables have an important role to play in suicide risk detection and that requiring their inclusion in all RF trees leads to increased predictive performance. Integrating temporal information into risk prediction models helps the models interpret patient data in temporal context, improving predictive performance.

Clinical risk prediction models powered by electronic health records (EHRs) are becoming increasingly widespread in clinical practice. With suicide-related mortality rates rising in recent years, it is becoming increasingly urgent to understand, predict, and prevent suicidal behavior. Here, we compare the predictive value of structured and unstructured EHR data for predicting suicide risk. We find that Naive Bayes Classifier (NBC) and Random Forest (RF) models trained on structured EHR data perform better than those based on unstructured EHR data. An NBC model trained on both structured and unstructured data yields similar performance (AUC = 0.743) to an NBC model trained on structured data alone (0.742, p = 0.668), while an RF model trained on both data types yields significantly better results (AUC = 0.903) than an RF model trained on structured data alone (0.887, p < 0.001), likely due to the RF model’s ability to capture interactions between the two data types. To investigate these interactions, we propose and implement a general framework for identifying specific structured-unstructured feature pairs whose interactions differ between case and non-case cohorts, and thus have the potential to improve predictive performance and increase understanding of clinical risk. We find that such feature pairs tend to capture heterogeneous pairs of general concepts, rather than homogeneous pairs of specific concepts. These findings and this framework can be used to improve current and future EHR-based clinical modeling efforts.

Cervical cancer is the leading cause of cancer mortality in India, accounting for 17% of all cancer deaths among women aged 30 to 69 years. At current incidence rates, the annual burden of new cases in India is projected to increase to 225,000 by 2025, but there are few large-scale, organized cervical cancer prevention programs in the country. We conducted a review of the cervical cancer prevention research literature and programmatic experiences in India to summarize the current state of knowledge and practices and recommend research priorities to address the gap in services. We found that research and programs in India have demonstrated the feasibility and acceptability of cervical cancer prevention efforts and that screening strategies requiring minimal additional human resources and laboratory infrastructure can reduce morbidity and mortality. However, additional evidence generated through implementation science research is needed to ensure that cervical cancer prevention efforts have the desired impact and are cost-effective. Specifically, implementation science research is needed to understand individual- and community-level barriers to screening and diagnostic and treatment services; to improve health care worker performance; to strengthen links among screening, diagnosis, and treatment; and to determine optimal program design, outcomes, and costs. With a quarter of the global burden of cervical cancer in India, there is no better time than now to translate research findings to practice. Implementation science can help ensure that investments in cervical cancer prevention and control result in the greatest impact.