Decision support system for the diagnosis of schizophrenia disorders

Adolescent, parent, and provider attitudes toward a machine learning based clinical decision support system for selecting treatment for youth depression

BACKGROUND

Machine learning based clinical decision support systems (CDSSs) have been proposed as a means of advancing personalized treatment planning for disorders, such as depression, that have a multifaceted etiology, course, and symptom profile. However, machine learning based models for treatment selection are rare in the field of psychiatry. They have also not yet been translated for use in clinical practice. Understanding key stakeholder attitudes toward machine learning based CDSSs is critical for developing plans for their implementation that promote uptake by both providers and families.

METHODS

In Study 1, a prototype machine learning based Clinical Decision Support System for Youth Depression (CDSS-YD) was demonstrated to focus groups of adolescents with a diagnosis of depression (n = 9), parents (n = 11), and behavioral health providers (n = 8). Qualitative analysis was used to assess their attitudes towards the CDSS-YD. In Study 2, behavioral health providers were trained in the use of the CDSS-YD and they utilized the CDSS-YD in a clinical encounter with 6 adolescents and their parents as part of their treatment planning discussion. Following the appointment, providers, parents, and adolescents completed a survey about their attitudes regarding the use of the CDSS-YD.

RESULTS

All stakeholder groups viewed the CDSS-YD as an easy to understand and useful tool for making personalized treatment decisions, and families and providers were able to successfully use the CDSS-YD in clinical encounters. Parents and adolescents viewed their providers as having a critical role in the use the CDSS-YD, and this had implications for the perceived trustworthiness of the CDSS-YD. Providers reported that clinic productivity metrics would be the primary barrier to CDSS-YD implementation, with the creation of protected time for training, preparation, and use as a key facilitator.

CONCLUSIONS

Machine learning based CDSSs, if proven effective, have the potential to be widely accepted tools for personalized treatment planning. Successful implementation will require addressing the system-level barrier of having sufficient time and energy to integrate it into practice.

Predicting treatment outcome in depression: an introduction into current concepts and challenges

Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.

An AI-based Decision Support System for Predicting Mental Health Disorders

Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants' answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

Artificial Intelligence-Based Differential Diagnosis: Development and Validation of a Probabilistic Model to Address Lack of Large-Scale Clinical Datasets

Background

Machine-learning or deep-learning algorithms for clinical diagnosis are inherently dependent on the availability of large-scale clinical datasets. Lack of such datasets and inherent problems such as overfitting often necessitate the development of innovative solutions. Probabilistic modeling closely mimics the rationale behind clinical diagnosis and represents a unique solution.

Objective

The aim of this study was to develop and validate a probabilistic model for differential diagnosis in different medical domains.

Methods

Numerical values of symptom-disease associations were utilized to mathematically represent medical domain knowledge. These values served as the core engine for the probabilistic model. For the given set of symptoms, the model was utilized to produce a ranked list of differential diagnoses, which was compared to the differential diagnosis constructed by a physician in a consult. Practicing medical specialists were integral in the development and validation of this model. Clinical vignettes (patient case studies) were utilized to compare the accuracy of doctors and the model against the assumed gold standard. The accuracy analysis was carried out over the following metrics: top 3 accuracy, precision, and recall.

Results

The model demonstrated a statistically significant improvement (P=.002) in diagnostic accuracy (85%) as compared to the doctors’ performance (67%). This advantage was retained across all three categories of clinical vignettes: 100% vs 82% (P<.001) for highly specific disease presentation, 83% vs 65% for moderately specific disease presentation (P=.005), and 72% vs 49% (P<.001) for nonspecific disease presentation. The model performed slightly better than the doctors’ average in precision (62% vs 60%, P=.43) but there was no improvement with respect to recall (53% vs 56%, P=.27). However, neither difference was statistically significant.

Conclusions

The present study demonstrates a drastic improvement over previously reported results that can be attributed to the development of a stable probabilistic framework utilizing symptom-disease associations to mathematically represent medical domain knowledge. The current iteration relies on static, manually curated values for calculating the degree of association. Shifting to real-world data–derived values represents the next step in model development.

Telepsychiatry clinical decision support system used by non-psychiatrists in remote areas: Validity & reliabilityof diagnostic module

Background & objectives:

A knowledge-based, logically-linked online telepsychiatric decision support system for diagnosis and treatment of mental disorders was developed and validated. We evaluated diagnostic accuracy and reliability of the application at remote sites when used by non-psychiatrists who underwent a brief training in its use through video-conferencing.

Methods:

The study was conducted at a nodal telepsychiatry centre, and three geographically remote peripheral centres. The diagnostic tool of application had a screening followed by detailed criteria-wise diagnostic modules for 18 psychiatric disorders. A total of 100 consecutive consenting adult outpatients attending remote telepsychiatry centres were included. To assess inter-rater reliability, patients were interviewed face to face by non-specialists at remote sites using the application (active interviewer) and simultaneously on online application via video-conferencing by a passive assessor at nodal centre. Another interviewer at the nodal centre rated the patient using Mini-International Neuropsychiatric Interview (MINI) for diagnostic validation.

Results:

Screening sub-module had high sensitivity (80-100%), low positive predictive values (PPV) (0.10-0.71) but high negative predictive value (NPV) (0.97-1) for most disorders. For the diagnostic sub-modules, Cohen's kappa was >0.4 for all disorders, with kappa of 0.7-1.0 for most disorders. PPV and NPV were high for most disorders. Inter-rater agreement analysis revealed kappa >0.6 for all disorders.

Interpretation & conclusions:

Diagnostic tool showed acceptable to good validity and reliability when used by non-specialists at remote sites. Our findings show that diagnostic tool of the telepsychiatry application has potential to empower non-psychiatrist doctors and paramedics to diagnose psychiatric disorders accurately and reliably in remote sites.

Clinical decision support systems in child and adolescent psychiatry: a systematic review

Psychiatric disorders are amongst the most prevalent and impairing conditions in childhood and adolescence. Unfortunately, it is well known that general practitioners (GPs) and other frontline health providers (i.e., child protection workers, public health nurses, and pediatricians) are not adequately trained to address these ubiquitous problems (Braddick et al. Child and Adolescent mental health in Europe: infrastructures, policy and programmes, European Communities, 2009; Levav et al. Eur Child Adolesc Psychiatry 13:395-401, 2004). Advances in technology may offer a solution to this problem with clinical decision support systems (CDSS) that are designed to help professionals make sound clinical decisions in real time. This paper offers a systematic review of currently available CDSS for child and adolescent mental health disorders prepared according to the PRISMA-Protocols (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols). Applying strict eligibility criteria, the identified studies (n = 5048) were screened. Ten studies, describing eight original clinical decision support systems for child and adolescent psychiatric disorders, fulfilled inclusion criteria. Based on this systematic review, there appears to be a need for a new, readily available CDSS for child neuropsychiatric disorder which promotes evidence-based, best practices, while enabling consideration of national variation in practices by leveraging data-reuse to generate predictions regarding treatment outcome, addressing a broader cluster of clinical disorders, and targeting frontline practice environments.

Nurses' psychosocial barriers to suicide risk management

Suicide remains a serious health care problem and a sentinel event tracked by The Joint Commission. Nurses are pivotal in evaluating risk and preventing suicide. Analysis of nurses' barriers to risk management may lead to interventions to improve management of suicidal patients. These data emerged from a random survey of 454 oncology nurses' attitudes, knowledge of suicide, and justifications for euthanasia. Instruments included a vignette of a suicidal patient and a suicide attitude questionnaire. Results. Psychological factors (emotions, unresolved grief, communication, and negative judgments about suicide) complicate the nurse's assessment and treatment of suicidal patients. Some nurses (n = 122) indicated that euthanasia was never justified and 11 were unsure of justifications and evaluated each case on its merits. Justifications for euthanasia included poor symptom control, poor quality of life, incurable illness or permanent disability, terminal illness, and terminal illness with inadequate symptom control or impending death, patient autonomy, and clinical organ death. The nurses indicated some confusion and misconceptions about definitions and examples of euthanasia, assisted suicide, and double effect. Strategies for interdisciplinary clinical intervention are suggested to identify and resolve these psychosocial barriers.