Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence

Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model\u27s performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD

Depression and HIV Infection are Risk Factors for Incident Heart Failure Among Veterans: Veterans Aging Cohort Study.

Background: Both HIV and depression are associated with increased heart failure (HF) risk. Depression, a common comorbidity, may further increase the risk of HF among HIV+ adults. We assessed the association between HIV, depression and incident HF. Methods and Results: Veterans Aging Cohort Study (VACS) participants free from cardiovascular disease at baseline (N = 81,427; 26,908 HIV+, 54,51

Desain sistem informasi untuk mendukung pelayanan rekam medis unit rawat jalan di rumah sakit jiwa Prof. Dr. Soerojo Magelang

Latar belakang: Pada tahun 2009 sebagai respon atas kebutuhan dari masyarakat untuk mendapatkan pelayanan kesehatan yang komprehensif, Rumah Sakit Jiwa Prof. Dr. Soerojo Magelang  membuka pelayanan kesehatan umum atau non jiwa, yang otomatis meningkatkan jumlah  kunjungan pasien pada unit rawat jalan. Hal ini menyebabkan dokumen rekam medis yang harus disimpan semakin hari semakin banyak, menyebabkan terganggunya proses pengambilan kembali dokumen rekam medis sehingga mengurangi mutu dan kualitas pelayanan di rumah sakit. Penelitian ini bertujuan untuk menyusun desain model pengembangan sistem informasi untuk mendukung pelayanan rekam  medis unit rawat jalan di Rumah Sakit Jiwa Prof. Dr. Soerojo Magelang.Metodologi Penelitian: Jenis penelitian ini adalah penelitian studi kasus deskriptif. Subyek penelitian terdiri dari 14 orang, pengguna langsung maupun tidak langsung dari sistem. Pengumpulan data dilakukan dengan wawancara mendalam dan observasi menggunakan alat bantu kamera dan perekam.Hasil: Pengelolaan data rekam medis pada unit rawat jalan di Rumah Sakit Jiwa Prof. Dr. Soerojo Magelang mulai dari input data, proses, sampai menghasilkan laporan belum optimal sehingga mengakibatkan data dan informasi yang tersedia kurang akurat dan kurang tepat waktu. Selanjutnya disusun desain model pengembangan sistem informasi untuk mendukung pelayanan rekam  medis unit rawat jalan, yaitu desain input, desain output, desain proses, desain data base dan desain user interface.Kesimpulan: Desain sistem disusun berdasarkan kebutuhan data dan informasi oleh pihak internal maupun pihak eksternal rumah sakit. Desain sistem yang disusun akan menghasilkan laporan-laporan yang sesuai dengan kebutuhan rumah sakit, sehingga dapat meningkatkan mutu dan kualitas pelayanan di rumah sakit. 

The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression

Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges

Depression and HIV infection: Risk factors for cardiovascular disease

Depression, a common mental health disorder, is associated with higher risk of cardiovascular disease (CVD). Adults with HIV infection are often burdened with depression. Although both depression and HIV infection are risk factors for CVD, previous studies have not explored how co-occurring depression and HIV are associated with CVD outcomes or underlying physiology. The aim of this dissertation was to (i) measure the risk of incident heart failure (HF) with co-occurring major depressive disorder (MDD) and HIV infection; (ii) measure biomarkers of inflammation, coagulation, monocyte activation, and metabolism with depression and HIV infection; and (iii) provide a comprehensive biomarker profile associated with symptoms of major depression in HIV+ and HIV- participants. We analyzed data from the Veterans Aging Cohort Study (VACS), a prospective study of HIV+ and HIV- veterans matched on age, sex, race/ethnicity, and geographical region. In a sample of 81,427 participants, we found that those with co-occurring HIV infection and MDD had significantly higher risk of incident HF compared to HIV- participants without MDD, after adjusting for covariates. In a subset of 2,099 participants, we determined that depression was associated with higher concentrations of interleukin-6 and soluble CD14 (biomarkers for inflammation and monocyte activation) in HIV- participants but not HIV+ participants. HIV+ participants had higher concentrations of glucose and triglycerides and lower concentrations of high-density lipoprotein cholesterol with depression. In a smaller sample with more extensive biomarker data, we found a significant association between depression and lower concentrations of vascular endothelial growth factor in HIV+ participants. Neither biomarker study supported the hypothesis that co-occurring depressive symptoms and HIV infection would interact and produce excessively high concentrations of these biomarkers. The findings from this dissertation are significant for public health research and practice. Depression is extremely common and is a risk factor for CVD. In the future, investigators must elucidate specific mechanisms driving CVD risk with depression and identify effective therapies for preventing depression-related CVD morbidity and mortality in both HIV- and HIV+ adults. Meanwhile, clinicians must remain vigilant in identifying and managing depressive symptoms, especially among those who are at heightened risk for CVD due to HIV infection

TOWARDS SCALABLE MENTAL HEALTH: LEVERAGING DIGITAL TOOLS IN COMBINATION WITH COMPUTATIONAL MODELING TO AID IN TREATMENT AND ASSESSMENT OF MAJOR DEPRESSIVE DISORDER

Major depressive disorder (MDD) is a debilitating disorder that impacts the lives of nearly 280 million individuals worldwide, representing 5% of the overall adult population. Unfortunately, these statistics have been both trending upward and are also likely an underestimate. This can be primarily attributed to lack of screening paired with a lack of providers. Worldwide, there are roughly 450 individuals living with MDD per mental health care provider. Adding to this burden, approximately half of affected individuals that do receive care of any kind will fail to remain in remission. The goal of this thesis work is to leverage statistical and machine learning models to help close these gaps in both MDD assessment and treatment. The data used in this thesis comes from a variety of sources including cross-sectional data from a physician wellness visit, randomized controlled trial (RCT) data from various digital interventions for MDD, and longitudinal data assessing individual’s depressive symptoms over time from the Tracking Depression Study. Supervised machine learning methods were applied to the wellness visit data to predict MDD presence and the RCT data to predict treatment response. The implication of these approaches is that in practice, they could enable passive assessments of MDD followed by personalized treatment planning using scalable interventions. As an addition to these machine learning approaches, statistical models were used to analyze longitudinal MDD symptom data to further understand individual changes in symptom dynamics. This work lays the foundation for dynamic treatment allocation that adapts as an individual’s experience changes

Inégalités socioéconomiques, contraintes psychosociales au travail et données administratives sur la dépression : résultats du PROspective Québec

Contexte : On estime à plus de 300 millions le nombre de personnes atteintes de troubles dépressifs dans le monde, ce qui en fait la principale source d'années vécues avec un trouble mental. Les mécanismes sous-jacents de la dépression sont mal compris, mais l'importance des facteurs socioéconomiques et psychosociaux est de plus en plus reconnue. Des études antérieures ont observé des risques plus élevés de dépression chez les personnes ayant un faible statut socioéconomique et chez celles exposées aux contraintes psychosociales au travail. Cependant, il y a plusieurs lacunes dans les connaissances sur : 1. la validité des données administratives pour mesurer la dépression chez les travailleurs; 2. les voies causales liant les inégalités socioéconomiques et les contraintes psychosociales au travail à l'incidence de la dépression; et 3. des études prospectives sur la proportion de cas de dépression attribuables à l'exposition à des contraintes psychosociales au travail. Objectifs :1. Évaluer la validité des mesures de dépression à partir des données administratives de la Régie d'assurance maladie du Québec. 2. Estimer les effets des indicateurs socioéconomiques (éducation, revenu familial et type d'occupation) et des contraintes psychosociales au travail (job strain et déséquilibre effort-reconnaissance) sur l'incidence de la dépression et la contribution de ces contraintes au gradient socioéconomique de la dépression. 3. Estimer les fractions populationnelles de cas de dépression attribuables à l'exposition aux contraintes psychosociales au travail. Les objectifs ont été investigués séparément chez les hommes et chez les femmes. Méthodes : Une étude prospective comportant initialement 9 188 cols blanc de Québec a été réalisée. La validité des données administratives a été évaluée par sa sensibilité, spécificité et concordance avec le Composite International Diagnostic Interview - Short Form (CIDI-SF). On a estimé des analogues interventionnels randomisés des effets directs des indicateurs socioéconomiques et des effets indirects médiés par les contraintes psychosociales au travail. Des fractions attribuables populationnelles ont été estimées à partir d'une méthode Kaplan-Meier pondérée. Résultats 1. Les données administratives de dépression ont une spécificité ≥ 96%, sensibilité de 19-32% et concordance (κ de Cohen) de 0,21-0,25 avec les données du questionnaire CIDI-SF. En analyse de groupes connus, les cas administratifs de dépression étaient comparables aux cas du CIDI-SF (risque relatif pour les femmes : 1,80 vs. 2,03 respectivement; âge < 58 ans: 1,53 vs 1,40; absence de formation universitaire : 1,52 vs 1,28; détresse psychologique : 2,21 vs 2,65). 2. L'incidence de la dépression chez les femmes était de 33,1 par 1000 personnes-années et de 16,8 chez les hommes. Parmi eux, un faible statut socioéconomique était un facteur de risque pour la dépression [faible éducation : rapport de taux 1,72, (intervalle de confiance à 95% 1,08-2,73); faible revenu familial : 1,67 (1,04-2,67); type d'occupation moins prestigieuse: 2,13 (1,08-4,19). Pour la population entière, l'exposition aux contraintes psychosociales au travail était associée à un risque accru de dépression [job strain : 1,42 (1,14-1,78); déséquilibre effort-reconnaissance (DER) : 1,73 (1,41-2,12)]. Les effets indirects estimés des indicateurs socioéconomiques sur la dépression médiée par le job strain variaient de 1,01 (0,99-1,03) à 1,04 (0,98 - 1,10). 3. La fraction populationnelle estimée de cas de dépression attribuables au job strain était de 15,9% (3,8-28,0%) et au DER de 21,9% (9,5-34,3%). Conclusion 1. Bien que les algorithmes de cas administratifs saisissent une dimension différente de la dépression que les cas CIDI-SF, aucune des deux sources de données est supérieure à l'autre pour identifier et quantifier les facteurs de risque de dépression dans de grandes études épidémiologiques. 2. Chez les hommes, un faible niveau de scolarité, un faible revenu familial et une occupation moins prestigieuse étaient des facteurs de risque notables pour l'incidence de la dépression. Les contraintes psychosociales au travail étaient aussi associées à une incidence plus élevée de dépression chez les hommes et les femmes. Cependant, les indicateurs de statut socioéconomique et les contraintes psychosociales au travail ne semblent pas fonctionner sur une voie causale commune vers la dépression, ce qui suggère plutôt une indépendance de leurs effets. Seul le job strain a montré une légère tendance à médier le gradient socio-économiquede la dépression. 3. Les contraintes psychosociales au travail, principalement le DER, pourraient être responsables de plus de 20 % de tous les cas de dépression survenus dans notre cohorte au cours d'un suivi de 3 ans.Background: The number of people afflicted with depressive disorders is estimated to be over 300 million people worldwide, which makes them the largest contributor to years lived with a mental disorder. The underlying mechanisms of depression are poorly understood, but recognition of the importance of socioeconomic and psychosocial factors is growing. Previous studies have observed higher risks of depression in people with low socioeconomic status and in those exposed to psychosocial stressors at work. However, there are several knowledge gaps regarding: 1. the validity of administrative data to measure depression in working populations; 2. the causal pathways linking socioeconomic inequality and psychosocial stressors at work to the incidence of depression; and 3. prospective studies on the proportion of cases of depression due to exposure to psychosocial stressors at work. Objectives: 1. Assess the validity of depression measures based on administrative data from the Régie d'Assurance Maladie du Québec. 2. Estimate the effects of socioeconomic indicators (education, family income and type of occupation) and psychosocial stressors at work (job strain and effort-reward imbalance) on the incidence of depression, and the contribution of the stressors to the socioeconomic gradient of depression. 3. Estimate the population fractions of cases of depression attributable to exposure to psychosocial stressors at work. The objectives were investigated separately for men and women. Methods: A prospective study was realized with initially 9 188 white-collar workers from Quebec. The validity of the administrative data was assessed by its sensitivity, specificity, and concordance with data from the Composite International Diagnostic Interview - Short Form. Randomized interventional analogues of the direct effects of socioeconomic indicators and of their indirect effects mediated by psychosocial stressors at work were estimated. Population attributable fractions were estimated using a weighted Kaplan-Meier method. Results: 1. Administrative depression data have specificity ≥ 96%, sensitivity of 19-32%, and concordance (Cohen's κ) of 0.21-0.25 with CIDI-SF questionnaire data. In known groups analysis, administrative cases of depression were comparable to CIDI-SF cases (relative risk for women: 1.80 vs. 2.03, respectively; age < 58 years: 1.53 vs. 1.40; no university degree: 1.52 vs 1.28, psychological distress: 2.21 vs 2.65). 2. The incidence of depression in women was 33.1 per 1000 person-years, and in men, 16.8. In men, [low education: hazard ratio 1.72, (95% confidence interval: 1.08-2.73); low family income: 1.67 (1.04-2.67); less prestigious occupation: 2.13 (1.08-4.19)]. In the entire population, exposure to psychosocial stressors at work was associated with increased risk of depression [job strain: 1.42 (1.14-1.78); effort-reward imbalance (ERI) 1.73 (1.41-2.12)]. The estimated indirect effects of socioeconomic indicators on depression mediated through job strain ranged from 1.01 (0.99-1.03) to 1.04 (0.98-1.10). 3. The estimated population fraction of cases of depression attributable to job strain was 15.9% (3.8-28.0%) and to ERI 21.9% (9.5-34.3%). Conclusion 1. Although administrative case algorithms capture a different dimension of depression than do CIDI-SF cases, neither of these data sources is superior to the other in the context of large epidemiological studies aiming to identify and quantify risk factors for depression. 2. Among men, low education, low family income and less prestigious occupation were notable risk factors for the incidence of depression. Psychosocial stressors at work were also associated with a higher incidence of depression in both men and women. However, indicators of socioeconomic status and psychosocial stressors at work do not seem to lie on a common causal path towards depression, which suggests rather that their effects are independent. Only job strain showed a slight tendency to mediate the socioeconomic gradient of depression. 3. Psychosocial stressors at work, mainly ERI, may be responsible for more than 20% of all cases of depression occurring in our cohort during a 3-year follow-up

Predicting And Characterizing The Health Of Individuals And Communities Through Language Analysis Of Social Media

A large and growing fraction of the global population uses social media, through which users share their thoughts, feelings, and behaviors, predominantly through text. To quantify the expression of psychological constructs in language, psychology has evolved a set of “closed-vocabulary” methods using pre-determined dictionaries. Advances in natural language processing have made possible the development of “open-vocabulary” methods to analyze text in data-driven ways, and machine learning algorithms have substantially improved prediction performances. The first chapter introduces these methods, comparing traditional methods of text analysis with newer methods from natural language processing in terms of their relative ability to predict and elucidate the language correlates of age, gender and the personality of Facebook users (N = 65,896). The second and third chapters discuss the use of social media to predict depression in individuals (the most prevalent mental illness). The second chapter reviews the literature on detection of depression through social media and concludes that no study to date has yet demonstrated the efficacy of this approach to screen for clinician-reported depression. In the third chapter, Facebook data was collected and connected to patients’ medical records (N = 683), and prediction models based on Facebook data were able to forecast the occurrence of depression with fair accuracy–about as well as self-report screening surveys. The fourth chapter applies both sets of methods to geotagged Tweets to predict county-level mortality rates of atherosclerotic heart disease mortality (the leading cause of death in the U.S.) across 1,347 counties, capturing 88% of the U.S. population. In this study, a Twitter model outperformed a model combining ten other leading demographic, socioeconomic and health risk factors. Across both depression and heart disease, associated language profiles identified fine-grained psychological determinants (e.g., loneliness emerged as a risk factor for depression, and optimism showed a protective association with heart disease). In sum, these studies demonstrate that large-scale text analysis is a valuable tool for psychology with implications for public health, as it allows for the unobtrusive and cost-effective monitoring of disease risk and psychological states of individuals and large populations