Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review

OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models. MATERIALS AND METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling. RESULTS: Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias. DISCUSSION: This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons. CONCLUSION: A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities

An End-to-End Machine Learning Solution for Anxiety and Depressive Disorder Symptom Occurrence During COVID-19: A New York Case Study

Anxiety and depression during the COVID-19 pandemic have heightened as evidenced by the rapidly growing corpus of research articles suggesting a link between the pandemic and mental health. This paper proposes a unique end-to-end user-centric machine learning (ML) architecture, capable of assessing the quality of ML predictions about the occurrence of anxiety and/or depression symptoms. A case study is presented using official New York State COVID-19 data, highlighting the plug-and-play capabilities of this architecture for both external features, and newer ML models. This is demonstrated through the formal design of a custom weighted clustering algorithm which outperforms conventional unsupervised techniques in grouping symptomatic cases. The ability to augment external sentiment data mined from social media platforms like Twitter, increases the predictive power of this architecture. This work serves as a blueprint to build a practical ML solution to better gauge the effect of future pandemic waves on mental health

Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care

Background and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the PREVENT - Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes

Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders

Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers

Mental Health and Wellbeing in England: the Adult Psychiatric Morbidity Survey 2014

This report presents findings of a survey of mental illness and wellbeing among people aged 16 and over living in private households in England. The survey was commissioned by NHS Digital and funded by the Department of Health, and is the fourth in a series of surveys of adult mental health

The Relationship between Cardiometabolic Disorders and Schizophrenia: From Early-Life Origins to the Development of a Cardiometabolic Risk Prediction Algorithm for Young People with Psychosis

My thesis considers the theme of comorbidity between cardiometabolic disorders and schizophrenia by focussing on three key aspects: the nature of association between cardiometabolic disorders and schizophrenia; the potential for common underlying biological mechanisms for the comorbidity; and the prediction of cardiometabolic risk in young adults with psychosis. On the nature of association between cardiometabolic disorders and schizophrenia, using longitudinal repeat measure data from a large birth cohort, I found that disruption to glucose-insulin homeostasis through childhood/adolescence is associated with increased risk of psychosis in early-adulthood; may not be fully explained by common sociodemographic and lifestyle factors; and may be specific to it. On the mechanisms of association between cardiometabolic disorders and schizophrenia, I used a range of genetic and observational epidemiological methods to examine whether inflammation and shared genetic liability may be common underlying biological mechanisms for the comorbidity. Using birth cohort data, I show that genetic risk for type 2 diabetes is associated with psychosis-risk in adulthood, and vice versa. I also show that genetic risk for type 2 diabetes may influence psychosis risk by increasing systemic inflammation. Using summary data from large genome-wide association studies (GWAS), I show a thread of evidence for shared genetic overlap between schizophrenia, cardiometabolic and inflammatory traits. Finally, using Mendelian randomization, I show evidence supporting that inflammation may be a common cause for insulin resistance and schizophrenia. On the prediction of cardiometabolic risk in young adults with psychosis, I performed a systematic review of cardiometabolic risk prediction algorithms and explored their predictive performance in a sample of young people at risk of developing psychosis. In doing so, I show that none are likely to be suitable for this population. Then, using patient data, I developed and externally validated the Psychosis Metabolic Risk Calculator (PsyMetRiC), the first cardiometabolic risk prediction algorithm specifically tailored for young people with psychosis. Together, my work suggests that cardiometabolic disorders and schizophrenia share aetiologic mechanisms, namely inflammation and shared genetic liability. I have shown that it is possible to accurately predict cardiometabolic risk in young people with psychosis using a tool tailored for the population. Such tools can in future become valuable resources for clinicians to reduce the risk of long-term cardiometabolic morbidity and mortality in people with schizophrenia.National Institute for Health Research (NIHR) Doctoral Research Fellowshi

Generational Association Studies of Dopaminergic Genes in Reward Deficiency Syndrome (RDS) Subjects: Selecting Appropriate Phenotypes for Reward Dependence Behaviors

Abnormal behaviors involving dopaminergic gene polymorphisms often reflect an insufficiency of usual feelings of satisfaction, or Reward Deficiency Syndrome (RDS). RDS results from a dysfunction in the “brain reward cascade,” a complex interaction among neurotransmitters (primarily dopaminergic and opioidergic). Individuals with a family history of alcoholism or other addictions may be born with a deficiency in the ability to produce or use these neurotransmitters. Exposure to prolonged periods of stress and alcohol or other substances also can lead to a corruption of the brain reward cascade function. We evaluated the potential association of four variants of dopaminergic candidate genes in RDS (dopamine D1 receptor gene [DRD1]; dopamine D2 receptor gene [DRD2]; dopamine transporter gene [DAT1]; dopamine beta-hydroxylase gene [DBH]). Methodology: We genotyped an experimental group of 55 subjects derived from up to five generations of two independent multiple-affected families compared to rigorously screened control subjects (e.g., N = 30 super controls for DRD2 gene polymorphisms). Data related to RDS behaviors were collected on these subjects plus 13 deceased family members. Results: Among the genotyped family members, the DRD2 Taq1 and the DAT1 10/10 alleles were significantly (at least p < 0.015) more often found in the RDS families vs. controls. The TaqA1 allele occurred in 100% of Family A individuals (N = 32) and 47.8% of Family B subjects (11 of 23). No significant differences were found between the experimental and control positive rates for the other variants. Conclusions: Although our sample size was limited, and linkage analysis is necessary, the results support the putative role of dopaminergic polymorphisms in RDS behaviors. This study shows the importance of a nonspecific RDS phenotype and informs an understanding of how evaluating single subset behaviors of RDS may lead to spurious results. Utilization of a nonspecific “reward” phenotype may be a paradigm shift in future association and linkage studies involving dopaminergic polymorphisms and other neurotransmitter gene candidates

Predictors of Veteran PTSD Symptom Reduction by Use of Accelerated Resolution Therapy

Despite 30 years of research advancements, PTSD treatment remains a trial-and-error process as 22 veterans per day commit suicide to relieve their symptoms. Foa and Kozak\u27s emotional processing theory informed this correlational study which included secondary data consisting of participants\u27 self-rated scale scores to examine whether the independent variables number of deployments, guilt, depression, and anxiety predicted the dependent variable PTSD symptom reduction in a veteran sample with combat deployments and associated PTSD symptoms who completed accelerated resolution therapy (ART). An analysis of whether mean PTSD symptom reduction amounts differed by symptom severity levels was also completed. The study aimed to identify the first predictive treatment-matching model for PTSD symptom reduction by use of ART. A multiple regression analysis to determine whether the predictor variables predicted PTSD symptom reduction by use of ART resulted in nonsignificant findings (p = .517). A Welch ANOVA test to determine if mean PTSD symptom reduction differed among the low, moderate, and high PTSD symptom severity groups showed significant results (p = .002). Games-Howell post hoc analysis showed that mean differences in PTSD symptom reduction from the low to high PTSD symptom severity group was significant (p = .001) with a 26.1 point mean reduction for the high symptom severity group and a greater than 10-point mean PTSD symptom reduction for the low and moderate symptom severity groups. The findings confirmed a need for treatment-matching algorithm studies to predict which PTSD interventions most benefit veterans suffering with PTSD to reduce trial-and-error treatment approaches, associated comorbidities, and high rates of suicides