Research Article
Anticipatory Healthcare Analytics: Inferring Latent Disease Dynamics from Noisy Clinical Observations
Philip de Melo*
Issue:
Volume 12, Issue 1, March 2026
Pages:
1-13
Received:
24 March 2026
Accepted:
7 April 2026
Published:
28 April 2026
Abstract: Artificial intelligence (AI) in healthcare is predominantly built on observational data that provide incomplete, delayed, and noisy representations of underlying biological processes. Such limitations constrain current predictive models, which often remain reactive and fail to capture the intrinsic dynamics of disease evolution. In this study, we introduce a novel AI-driven framework based on latent-state reconstruction, designed to infer hidden disease trajectories from partial clinical and population-level observations and to generate dynamic, forward-looking risk estimates. The proposed approach departs fundamentally from traditional methods by explicitly modeling healthcare systems as partially observed complex adaptive systems. It reconstructs latent health states that evolve over time and gives rise to observable clinical measurements subject to stochastic variability. Drawing a conceptual parallel to quantum mechanics, where a system’s true state is described by a wave function that governs probabilistic observations, our framework treats the latent health state as the primary object of inference rather than the observed data alone. This shift enables a transition from descriptive analytics to anticipatory intelligence. By deriving hazard functions from reconstructed latent trajectories, the framework provides earlier and more accurate detection of disease progression, outbreak dynamics, and systemic instability. Empirical and theoretical analysis demonstrates that this approach captures underlying population heterogeneity and temporal dynamics that are inaccessible to conventional models. This work establishes a new paradigm for AI in healthcare, where prediction is grounded in the reconstruction of hidden system dynamics, enabling proactive intervention and more reliable decision-making in complex, high-dimensional environments.
Abstract: Artificial intelligence (AI) in healthcare is predominantly built on observational data that provide incomplete, delayed, and noisy representations of underlying biological processes. Such limitations constrain current predictive models, which often remain reactive and fail to capture the intrinsic dynamics of disease evolution. In this study, we i...
Show More
Research Article
Assessment of Insecticide-Treated Nets Coverage and Utilization for Malaria Control Among High-Risk Groups in Mettu Woreda,South West Ethiopia
Dagim Dagne*
Issue:
Volume 12, Issue 1, March 2026
Pages:
14-26
Received:
22 April 2026
Accepted:
27 May 2026
Published:
28 July 2026
DOI:
10.11648/j.jfmhc.20261201.12
Downloads:
Views:
Abstract: Malaria remains a major public health challenge in southwestern Ethiopia, disproportionately affecting vulnerable populations. This community-based cross-sectional study assessed the coverage and utilization of insecticide-treated nets (ITNs) among high-risk groups, specifically pregnant women and children under five years of age, in Mettu Woreda, South West Ethiopia. A multi-stage sampling technique was employed to select 422 households with high-risk individuals. Data were collected using a pre-tested structured questionnaire and observational checklists, and analyzed using descriptive and logistic regression methods. The findings revealed that while household ITN ownership (coverage) was relatively high at 82.4%, the actual utilization rate among the high-risk individuals during the night preceding the survey was significantly lower at 58.7%. Predictors significantly associated with ITN utilization included health education exposure (AOR = 2.4, 95% CI: 1.5–3.8), primary education level of the household head (AOR = 1.9, 95% CI: 1.1–3.3), and family size of less than five members (AOR = 2.1, 95% CI: 1.3–3.5). The study concludes that a substantial gap exists between ITN ownership and actual utilization among high-risk groups in the study area. To maximize the impact of malaria control programs, local health sectors should shift focus from mere distribution to intensive behavioral change communication (BCC) strategies targeting consistent net usage.
Abstract: Malaria remains a major public health challenge in southwestern Ethiopia, disproportionately affecting vulnerable populations. This community-based cross-sectional study assessed the coverage and utilization of insecticide-treated nets (ITNs) among high-risk groups, specifically pregnant women and children under five years of age, in Mettu Woreda, ...
Show More
