AI-Driven Analysis of Diagnostic Profiles in COVID-19 Patients: Implications for Healthcare Interventions

The COVID-19 crisis has strained global healthcare systems, highlighting the significance of investigating comorbidities and secondary diagnoses in patients. Harnessing of data-driven insights, as facilitated by artificial intelligence (AI), has shown remarkable promise in enhancing the efficacy of healthcare strategies and ameliorating patient outcomes. PURPOSE: To identify diagnostic profiles in COVID-19 patients via AI-driven analysis, focusing on comorbidities and secondary diagnoses. METHODS: The analytical groundwork was established upon the scrutiny of 42,974 patients with PCR-confirmed COVID-19 diagnosis. Each record was characterized by 850 diagnostic indicators encompassing a spectrum of ailments, such as demyelinated diseases, seizure disorders, and various additional comorbidities. The predominant racial composition of the sample was White (n = 31,329, 73%). A majority of patients were of the female gender (n = 23,534, 55%). Data were collected using Electronic Medical Records through the Cerner system from 31 hospitals in a large health system. Finite mixture modeling, a form of model-based unsupervised machine learning, was employed to ascertain the presence of latent, distinguishable patterns among secondary diagnoses. Of the approximately 850 secondary diagnoses considered, 221 exhibited prevalence in over 50 patients. A sequence of mixture models was estimated, incrementally augmenting the number of latent profiles via maximum likelihood estimation with robust standard errors. Model solutions were subjected to rigorous evaluation, culminating in the selection of three diagnostic profiles predicated on statistical model-data fit, parsimony, and interpretability. RESULTS: The selected model revealed the presence of three distinct diagnostic profiles. These profiles were characterized by patients who: (1) exhibited a notably low likelihood of presenting with secondary diagnoses, (2) demonstrated heightened probabilities of manifesting commonly observed diagnoses within the United States, such as hypertension, hyperlipidemia, and a history of tobacco use, or (3) displayed elevated probabilities of harboring multiple comorbid diagnoses, spanning domains such as lung, heart, and kidney-related conditions. The initial profile encompassed 27,002 patients (63%), followed by the second profile comprising 11,419 patients (27%), and the third profile, accounting for 4,553 patients (11%). Patients were individually assigned probabilities denoting their affiliation with each profile, with respective average classification probabilities of .98, .89, and .94, signifying a high degree of classification confidence. CONCLUSION: Our findings demonstrate the potential application of AI in informing healthcare interventions, such as tailored treatment plans, early intervention, resource allocation, patient education, research and development, and healthcare policy

Examining the Relationship Between Trait Energy and Fatigue and Feelings of Depression in Young Healthy Adults

Depression is usually co-morbid with fatigue. However, we are unaware of studies exploring the relationship between trait energy and fatigue and feelings of depression. Recent evidence suggests that energy and fatigue are two distinct moods. PURPOSE: To examine the association between trait mental and physical energy and fatigue and feelings of depression, within an otherwise healthy young adult cohort. METHODS: Using a cross-sectional design, healthy respondents (n=495) completed a series of self-reported surveys measuring depression, lifestyle factors (sleep, diet, physical activity), and trait mental and physical energy and fatigue. Using a step-wise regression, we controlled for demographics and lifestyle and added trait mental and physical energy and fatigue to the second model. RESULTS: When trait mental and physical energy and fatigue were added to the models, the adjusted R2 increased by 5% (R2 = .112, F(13, 457) = 4.455, p \u3c .001). In our second model, trait mental fatigue was the only significant predictor of depressive mood states (Β = .159, t (457) = 2.512, p = 0.01). CONCLUSION: Young adults, who struggle with high mental fatigue, may also be more likely to report feeling depressed suggesting that fatigue and depression are co-morbid, while low energy and depression are not. Future research should aim to identify epigenetic/genetic factors that influence mental fatigue and how those may be associated with feelings of depression

Association between Self-Reported Prior Nights’ Sleep and Single-Task Gait in Healthy Young Adults: An Exploratory Study Using Machine Learning

Failure to obtain 7-9 hours of sleep has been associated with decreased gait speed in young adults. While Machine Learning (ML) has been used to identify sleep quality in young adults, there are no current studies that have used ML to identify prior night’s sleep in a sample of young adults. PURPOSE: To use ML to identify prior night’s sleep in healthy young adults using single-task walking gait. METHODS: Participants (n=126, age 24.3±4.0yrs; 65% female) completed a survey on their prior night’s sleep and performed a 2-minute walk around a 6m track. Gait data were collected using inertial sensors. Participants were split into 2 groups (\u3c7hs or \u3e9hs: poor sleepers; 7-9hs: good sleepers) and gait characteristics were used to classify participants into each group using ML models via a 10-fold cross validation. A post-hoc ANCOVA was used to assess gait differences. RESULTS: Using Random Forest Classifiers (RFC), top 9 features were extracted. Classification results suggest a 0.79 correlation between gait parameters and prior night’s sleep. The RFC models had a 65.03% mean classification accuracy rate. Top 0.3% of the models had 100% classification accuracy rate. The top 9 features were primarily characteristics that measured variance between lower limb movements. Post-hoc analyses suggest significantly greater variances between lower limb characteristics. CONCLUSION: Good sleepers had more asymmetrical gait patterns (faster gait speed, less trunk motion). Poor sleepers had trouble maintaining gait speed (increased variance in cadence, larger stride lengths, and less time spent in single leg support time). Although the mechanisms of these gait changes are unknown, these findings provide evidence that gait is different for individuals who not receive 7-9 hours of sleep the night before. As evidenced by the high correlation co-efficient of our classification models, gait may be a good way of identifying prior night’s sleep