Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records

Posada, Jose; Barda, Amie J.; Shi, Lei; Xue, Diyang; Ruiz, Víctor; Kuan, Pei-Han; Ryan, Neal D.; Tsui, Fu-Chiang

doi:10.1016/j.jbi.2017.05.019

Cited by 16 publications

(12 citation statements)

References 18 publications

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“…data sharing (Dluhoš et al ., 2017; Zhu et al ., 2017), participant selection (Geraci et al ., 2017), and analysis (Guan et al ., 2015; Squarcina et al ., 2015 a ; Khondoker et al ., 2016; Dipnall et al ., 2016 a )]; and, (iii) extracting mental health symptoms from existing sources (e.g. research publications, clinical notes and databases [Ghafoor et al ., 2015; Hu and Terrazas, 2016; Caballero et al ., 2017; Posada et al ., 2017; Zhang et al ., 2017 b ; Karystianis et al ., 2018)] (see Table 5). The studies identified in this category demonstrate several benefits of ML for mental health administration.…”

Section: Resultsmentioning

confidence: 99%

Machine learning in mental health: a scoping review of methods and applications

2019

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BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.MethodsWe employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.ResultsThree hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.ConclusionsOverall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

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Section: Resultsmentioning

confidence: 99%

Machine learning in mental health: a scoping review of methods and applications

2019

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“…The use of unstructured data can be of value to predict suicide risk, 17 especially because approximately 80% of EHR data are locked in narrative form. 18 NLP can identify suicide attempt predictors from clinical notes, 19 , 20 such as clinician positive valence assessments 21 and social determinants of health (SDOH). 22 , 23 SDOH are nonmedical factors, such as housing, employment, and family support, which have profound influences on health outcomes.…”

Section: Introductionmentioning

confidence: 99%

Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts

et al. 2021

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Objective Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data. Methods This case-control study included patients aged 10–75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical EHR data prior to suicide attempt or control index visits, with prediction windows from 7 to 730 days. Patients without any historical notes were excluded. Model evaluation on accuracy and robustness was performed on a blind dataset (30% cohort). Results The study cohort included 45 238 patients (5099 cases, 40 139 controls) comprising 54 651 variables from 5.7 million structured records and 798 665 notes. Using both unstructured and structured data resulted in significantly greater accuracy compared to structured data alone (area-under-the-curve [AUC]: 0.932 vs. 0.901 P < .001). The best-predicting model utilized 1726 variables with AUC = 0.932 (95% CI, 0.922–0.941). The model was robust across multiple prediction windows and subgroups by demographics, points of historical most recent clinical contact, and depression diagnosis history. Conclusions Our large data-driven approach using both structured and unstructured EHR data demonstrated accurate and robust first-time suicide attempt prediction, and has the potential to be deployed across various populations and clinical settings.

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“…Linear methods are favored when there are many samples in a high dimensional input space. In contrast, for low-dimensional problems with many training instances, nonlinear kernel methods may be more favorable.Apart from the models mentioned above, researchers have explored other methods such as random forests [112], decision trees [100, 113, 131, 132], and the Longitudinal Gamma Poisson Shrinker [133, 134] for computational phenotyping. DeLisle et al [102] implemented a conditional random field probabilistic classifier [135] to identify acute respiratory infections.…”

Section: Methods For Nlp-based Computational Phenotypingmentioning

confidence: 99%

Natural Language Processing for EHR-Based Computational Phenotyping

Zeng

Deng

et al. 2019

IEEE/ACM Trans. Comput. Biol. and Bioinf.

155

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This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI) and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.

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Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records

Cited by 16 publications

References 18 publications

Machine learning in mental health: a scoping review of methods and applications

Machine learning in mental health: a scoping review of methods and applications

Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts

Natural Language Processing for EHR-Based Computational Phenotyping

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