An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support

Tracking and Profiling Repeated Users Over Time in Text-Based Counseling: Longitudinal Observational Study With Hierarchical Clustering

BACKGROUND

Due to their accessibility and anonymity, web-based counseling services are expanding at an unprecedented rate. One of the most prominent challenges such services face is repeated users, who represent a small fraction of total users but consume significant resources by continually returning to the system and reiterating the same narrative and issues. A deeper understanding of repeated users and tailoring interventions may help improve service efficiency and effectiveness. Previous studies on repeated users were mainly on telephone counseling, and the classification of repeated users tended to be arbitrary and failed to capture the heterogeneity in this group of users.

OBJECTIVE

In this study, we aimed to develop a systematic method to profile repeated users and to understand what drives their use of the service. By doing so, we aimed to provide insight and practical implications that can inform the provision of service catering to different types of users and improve service effectiveness.

METHODS

We extracted session data from 29,400 users from a free 24/7 web-based counseling service from 2018 to 2021. To systematically investigate the heterogeneity of repeated users, hierarchical clustering was used to classify the users based on 3 indicators of service use behaviors, including the duration of their user journey, use frequency, and intensity. We then compared the psychological profile of the identified subgroups including their suicide risks and primary concerns to gain insights into the factors driving their patterns of service use.

RESULTS

Three clusters of repeated users with clear psychological profiles were detected: episodic, intermittent, and persistent-intensive users. Generally, compared with one-time users, repeated users showed higher suicide risks and more complicated backgrounds, including more severe presenting issues such as suicide or self-harm, bullying, and addictive behaviors. Higher frequency and intensity of service use were also associated with elevated suicide risk levels and a higher proportion of users citing mental disorders as their primary concerns.

CONCLUSIONS

This study presents a systematic method of identifying and classifying repeated users in web-based counseling services. The proposed bottom-up clustering method identified 3 subgroups of repeated users with distinct service behaviors and psychological profiles. The findings can facilitate frontline personnel in delivering more efficient interventions and the proposed method can also be meaningful to a wider range of services in improving service provision, resource allocation, and service effectiveness.

Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review

(1) Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Machine learning and artificial intelligence such as natural language processing has the potential to become a major technique for the detection, diagnosis, and treatment of people. (2) Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. (3) Result: The preliminary search of 5 databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. (4) Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Additionally, to reduce suicides, people with mental problems must be continuously and passively monitored. (5) Conclusions: The use of AI&ML opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review's analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention.

Suicidal vulnerability in older adults and the elderly: study based on risk variables

BACKGROUND

Predicting suicidal vulnerability based on previous risk factors remains a challenge for mental health professionals, especially in specific subpopulations.

AIMS

This study aimed to use structural equation modelling to assess which sociodemographic and clinical variables are most predictive and modulating of repeated self-injury or reattempts at suicide in older adults and the elderly with previous attempts.

METHOD

We obtained digital data for 619 people (N = 342; 55.3% women), aged 50-96 years (mean 71.2 years, s.d. 3.65), who presented to the emergency department with a repeated self-injury or suicide attempt. Data were collected from several public and private hospitals in southern Spain.

RESULTS

There were different sociodemographic and clinical profiles between people who repeat self-injury and those who reattempt suicide. In addition, we show that outcome variables may directly or indirectly modulate these behaviours.

CONCLUSIONS

The study findings provide only a limited insight into suicidal vulnerability in older people, and there is an urgent need for specific care protocols for the prevention of repeated self-injury or reattempts at suicide that are adapted to the psychosocial characteristics of this age group. There is also a need to improve social and health alert actions for older adults and the elderly who present with suicide risk profiles, and the presence of mental health professionals in hospital emergency departments should be improved.

iHealth: The ethics of artificial intelligence and big data in mental healthcare

The concept of intelligent health (iHealth) in mental healthcare integrates artificial intelligence (AI) and Big Data analytics. This article is an attempt to outline ethical aspects linked to iHealth by focussing on three crucial elements that have been defined in the literature: self-monitoring, ecological momentary assessment (EMA), and data mining. The material for the analysis was obtained by a database search. Studies and reviews providing outcome data for each of the three elements were analyzed. An ethical framing of the results was conducted that shows the chances and challenges of iHealth. The synergy between self-monitoring, EMA, and data mining might enable the prevention of mental illness, the prediction of its onset, the personalization of treatment, and the participation of patients in the treatment process. Challenges arise when it comes to the autonomy of users, privacy and data security of users, and potential bias.

Artificial intelligence and suicide prevention: a systematic review

Background

Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide.

Methods

A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords.

Results

Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings.

Conclusions

AI appears to have a high potential for identifying patients at risk of suicide. The precise use of these algorithms in clinical situations, as well as the ethical issues it raises, remain to be clarified.

Acceptability and satisfaction with emma, a smartphone application dedicated to suicide ecological assessment and prevention

BACKGROUND

As mHealth may contribute to suicide prevention, we developed emma, an application using Ecological Momentary Assessment and Intervention (EMA/EMI).

OBJECTIVE

This study evaluated emma usage rate and acceptability during the first month and satisfaction after 1 and 6 months of use.

METHODS

Ninety-nine patients at high risk of suicide used emma for 6 months. The acceptability and usage rate of the EMA and EMI modules were monitored during the first month. Satisfaction was assessed by questions in the monthly EMA (Likert scale from 0 to 10) and the Mobile App Rating Scale (MARS; score: 0-5) completed at month 6. After inclusion, three follow-up visits (months 1, 3, and 6) took place.

RESULTS

Seventy-five patients completed at least one of the proposed EMAs. Completion rates were lower for the daily than weekly EMAs (60 and 82%, respectively). The daily completion rates varied according to the question position in the questionnaire (lower for the last questions, LRT = 604.26, df = 1, p-value < 0.0001). Completion rates for the daily EMA were higher in patients with suicidal ideation and/or depression than in those without. The most used EMI was the emergency call module (n = 12). Many users said that they would recommend this application (mean satisfaction score of 6.92 ± 2.78) and the MARS score at month 6 was relatively high (overall rating: 3.3 ± 0.87).

CONCLUSION

Emma can target and involve patients at high risk of suicide. Given the promising users' satisfaction level, emma could rapidly evolve into a complementary tool for suicide prevention.

Can existing electronic medical records be used to quantify cardiovascular risk at point of care?

BACKGROUND

Using electronic data for cardiovascular risk stratification could help in prioritising healthcare access and optimise cardiovascular prevention.

AIMS

To determine whether assessment of absolute cardiovascular risk (Australian Absolute Cardiovascular Disease Risk, ACVDR) and short-term ischaemic risk (HEART Score) are possible from available data in Electronic Medical Record (EMR) and My Health Record (MHR) of patients presenting with acute cardiac symptoms to a Rapid Access Cardiology Clinic (RACC).

METHODS

Audit of EMR and MHR on 200 randomly selected adults who presented to RACC between 1st of March 2017 and 4th February 2020. The main outcomes were the proportion of patients for which an ACVDR and HEART Score could be calculated.

RESULTS

Mean age was 55.2 ± 17.8 years and 43% were female. Most were referred from Emergency (85%) for chest pain (52%). 46% had hypertension, 35% obesity, 20% diabetes mellitus, 17% ischaemic heart disease, and 18% were current smokers. There was no significant difference in MHR accessibility with age, gender, and number of comorbidities. ACVDR could be estimated for 17.5% (EMR) and 0% (MHR) of patients. None had complete data to estimate HEART Score in either EMR or MHR. Most commonly missing variables for ACVDR were blood pressure (MHR) and HDL-C (EMR), and for HEART Score were body mass index and comorbidities (MHR and EMR).

CONCLUSIONS

Significant gaps are apparent in electronic medical data capture of key variables to perform cardiovascular risk assessment. Medical data capture should prioritise the collection of clinically important data to help address gaps in cardiovascular management. This article is protected by copyright. All rights reserved.

Machine Learning and Natural Language Processing in Mental Health: Systematic Review

BACKGROUND

Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.

OBJECTIVE

The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice.

METHODS

This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.

RESULTS

A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.

CONCLUSIONS

Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Clinical evaluation of an interoperable clinical decision-support system for the detection of systemic inflammatory response syndrome in critically ill children

Background

Systemic inflammatory response syndrome (SIRS) is defined as a non-specific inflammatory process in the absence of infection. SIRS increases susceptibility for organ dysfunction, and frequently affects the clinical outcome of affected patients. We evaluated a knowledge-based, interoperable clinical decision-support system (CDSS) for SIRS detection on a pediatric intensive care unit (PICU).

Methods

The CDSS developed retrieves routine data, previously transformed into an interoperable format, by using model-based queries and guideline- and knowledge-based rules. We evaluated the CDSS in a prospective diagnostic study from 08/2018–03/2019. 168 patients from a pediatric intensive care unit of a tertiary university hospital, aged 0 to 18 years, were assessed for SIRS by the CDSS and by physicians during clinical routine. Sensitivity and specificity (when compared to the reference standard) with 95% Wald confidence intervals (CI) were estimated on the level of patients and patient-days.

Results

Sensitivity and specificity was 91.7% (95% CI 85.5–95.4%) and 54.1% (95% CI 45.4–62.5%) on patient level, and 97.5% (95% CI 95.1–98.7%) and 91.5% (95% CI 89.3–93.3%) on the level of patient-days. Physicians’ SIRS recognition during clinical routine was considerably less accurate (sensitivity of 62.0% (95% CI 56.8–66.9%)/specificity of 83.3% (95% CI 80.4–85.9%)) when measurd on the level of patient-days. Evaluation revealed valuable insights for the general design of the CDSS as well as specific rule modifications. Despite a lower than expected specificity, diagnostic accuracy was higher than the one in daily routine ratings, thus, demonstrating high potentials of using our CDSS to help to detect SIRS in clinical routine.

Conclusions

We successfully evaluated an interoperable CDSS for SIRS detection in PICU. Our study demonstrated the general feasibility and potentials of the implemented algorithms but also some limitations. In the next step, the CDSS will be optimized to overcome these limitations and will be evaluated in a multi-center study.

Trial registration: NCT03661450 (ClinicalTrials.gov); registered September 7, 2018.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01428-7.

Identifying risk factors for suicidal ideation across a large community healthcare system

BACKGROUND

Suicide is the tenth leading cause of death in the United States. Several studies have leveraged electronic health record (EHR) data to predict suicide risk in veteran and military samples; however, few studies have investigated suicide risk factors in a large-scale community health population.

METHODS

Clinical data was queried for 9,811 patients from the Penn Medicine Health System who had completed a Patient Health Questionnaire-9 (PHQ-9) documented in the EHR between January 2017 and June 2019. Patient demographics, PHQ-9 scores, and psychiatric comorbidities were extracted from the EHR. Univariate and multivariable logistic regressions were applied to determine significant risk factors associated with suicide ideation responses from the PHQ-9.

RESULTS

One-quarter (25.8%% of patients endorsed suicide ideation. Univariate analysis found 22 risk factors of suicide ideation. Multivariable logistic regression found significant positive associations (Odds Ratio, (95% Confidence Interval)) with the following: younger ages less than 18 years: 2.1, (1.69, 2.60) and 19-24 years: 1.55, (1.29, 1.87)), single marital status (1.22, (1.08, 1.38)), African American (1.22, (1.08, 1.38)), non-commercial insurance (1.16, (1.03, 1.31)), multiple comorbidities (1 comorbidity (1.65, (1.32, 2.07); 2 comorbidities (2.07, (1.61, 2.64)), 3+ comorbidities (2.49, (1.87, 3.33))), bipolar disorders (Type I: 1.38, (1.14, 1.67) and Type II: 1.94, (1.52, 2.49)), depressive disorders (1.70, (1.49, 1.94)), obsessive compulsive disorder (OCD) (1.43, (1.08, 1.90)), and stress disorders (1.53, (1.33, 1.76)).

CONCLUSION

Community EHR information can be used to predict suicidal ideation. This information can be used to design tools for identifying patients at risk for suicide in real-time.