Refactor DeepHealth indices around disease expression
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\documentclass[11pt]{article}
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\usepackage[margin=1in]{geometry}
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\usepackage{amsmath, amssymb, amsfonts}
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\usepackage{bm}
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\usepackage{amsmath, amssymb}
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\usepackage{booktabs}
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\usepackage{enumitem}
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\usepackage{hyperref}
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\title{DeepHealth Burden Indices: Dynamic Organ and Functional Burden}
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\title{DeepHealth Disease Expression, Organ Involvement, and Frailty Risk Indices}
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\author{}
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\date{}
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@@ -16,561 +15,147 @@
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\maketitle
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\begin{abstract}
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DeepHealth produces a query-time hidden representation \(h(t)\) and
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disease-specific future risk functions \(p_d(h,\Delta)\). These disease-level
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outputs are clinically granular but difficult to interpret directly as a
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patient-level health state. We therefore define Burden Indices (BI) that
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aggregate historical and predicted disease burden into higher-level,
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interpretable dimensions. The Organ Burden Index (OBI) maps diseases to
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anatomical systems, while the Functional Burden Index (FBI) maps diseases to
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function- and frailty-related burden domains, anchored by CIHI-HFRM-style
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diagnosis weights when available. For formed burden, we distinguish an
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observed-anchored version based on actual historical diagnoses and a
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model-weighted version based on DeepHealth's historical risk trajectory. The
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indices are burden measures, not direct health reserve measures, because the
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current model is supervised by disease events rather than direct functional
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outcomes such as ADL/IADL, gait speed, grip strength, cognition, or recovery
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capacity.
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DeepHealth provides a query-time hidden state \(h(t)\) and disease-specific
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risk functions \(p_d(h,\Delta)\). We use these outputs to define a continuous
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disease expression rate \(z_d(t)\). This quantity should be interpreted as how
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much disease \(d\) is model-implied to have formed or expressed by query time
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\(t\), not as true physiological damage. Based on \(z_d(t)\), we define two
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downstream indices: an organ involvement index, which summarizes whether an
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organ-age-inspired clinical system is involved by any related disease process,
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and a DeepHealth-HFRS frailty risk index, which is the original UK-HFRS weighted
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sum with binary disease occurrence replaced by continuous disease expression.
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\end{abstract}
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\section{Motivation}
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\section{Disease Expression Rate}
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At query time \(t\), DeepHealth produces a hidden state \(h(t)\) and
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disease-level risk predictions
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For a patient queried at time \(t\), let the historical readout times be
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\[
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p_d(h(t), \tau), \qquad d = 1,\ldots,D,
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t_0 < t_1 < \cdots < t_n \le t,\qquad t_{n+1}=t.
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\]
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where \(p_d(h(t),\tau)\) is the predicted probability of disease \(d\) occurring
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within future horizon \(\tau\). These outputs are useful for disease-specific
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risk prediction, but they do not directly answer patient-level questions such as
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where disease burden is concentrated or how much functional vulnerability is
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implied by the disease profile.
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We introduce Burden Indices to summarize disease-level predictions into
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interpretable state representations:
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For each interval \([t_i,t_{i+1}]\), DeepHealth produces a hidden state
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\(h_i=h(t_i)\) and an interval risk
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\[
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\text{disease-level risk}
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\quad \longrightarrow \quad
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\text{system-level burden}.
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q_{d,i}(t)=p_d(h_i,t_{i+1}-t_i).
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\]
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The indices combine two components:
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\begin{enumerate}[leftmargin=*]
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\item formed burden: disease burden already accumulated by query time \(t\);
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\item future expected burden: disease burden expected to newly form within
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horizon \(\tau\).
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\end{enumerate}
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At the current stage, these quantities should be called burden indices rather
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than health reserve or health state scores. The current model is trained and
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validated primarily on ICD disease events. Its directly verifiable semantics are
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disease occurrence and disease risk. Without direct functional labels or a
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calibrated healthy reference state, quantities such as \(100-\text{burden}\)
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cannot be rigorously interpreted as remaining health reserve.
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\section{Two Burden Spaces}
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We define two complementary burden spaces.
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\subsection{Organ Burden Index}
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The Organ Burden Index (OBI) maps disease burden to anatomical systems. It
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answers:
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The model-implied disease expression rate is defined by noisy-or accumulation:
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\[
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\text{Which organs or anatomical systems carry the largest pathological burden?}
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\]
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Typical dimensions may include heart/vascular, brain/neurological, kidney,
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lung, liver/digestive, metabolic/endocrine, musculoskeletal, hematologic, and
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malignancy-related systems. The mapping matrix is denoted
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\[
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A^{\mathrm{organ}} \in \mathbb{R}_{\ge 0}^{K_o \times D},
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\]
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where \(A^{\mathrm{organ}}_{k,d}\) is the contribution weight from disease
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\(d\) to organ dimension \(k\).
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\subsection{Functional Burden Index}
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The Functional Burden Index (FBI) maps disease burden to function- and
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frailty-related diagnostic burden domains. It answers:
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\[
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\text{How much functional vulnerability is implied by the disease burden?}
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\]
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Candidate dimensions include mobility burden, cognition burden, mood burden,
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sensory burden, nutrition burden, infection or immune vulnerability burden,
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functional dependence burden, and comorbidity burden.
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When CIHI-HFRM or another validated hospital frailty risk measure code list is
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available, it should be used as the primary anchor for FBI. The mapping matrix
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is denoted
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\[
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A^{\mathrm{func}} \in \mathbb{R}_{\ge 0}^{K_f \times D},
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\]
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where \(A^{\mathrm{func}}_{k,d}\) is the contribution weight from disease \(d\)
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to functional burden dimension \(k\).
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OBI and FBI are not redundant. OBI describes where pathology is concentrated,
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whereas FBI describes how disease burden may translate into functional
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vulnerability. For example, stroke contributes primarily to brain/vascular
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burden in OBI, but may contribute to mobility, cognition, sensory, and
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functional dependence burden in FBI.
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\section{Model Outputs and Disease Risk Function}
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For each hidden state \(h\), DeepHealth defines a disease-specific future risk
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function
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\[
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p_d(h,\Delta),
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\]
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where \(\Delta \ge 0\) is the time horizon. The risk function is produced by the
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all-future model. Let
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\[
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\eta_d(h) = \operatorname{risk\_head}(h)_d,
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\qquad
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\lambda_d(h) = \operatorname{softplus}(\eta_d(h)).
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\]
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For the exponential all-future model,
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\[
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p_d(h,\Delta)
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=
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1-\exp[-\lambda_d(h)\Delta].
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\]
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For the Weibull all-future model, with
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\[
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\rho_d(h)=\operatorname{softplus}(\operatorname{rho\_head}(h)_d),
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\]
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the risk function is
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\[
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p_d(h,\Delta)
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=
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1-\exp[-\lambda_d(h)\Delta^{\rho_d(h)}].
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\]
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The burden formulation below only assumes access to \(p_d(h,\Delta)\); the
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exponential and Weibull cases are specializations.
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\section{Formed Burden}
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For a patient queried at time \(t\), let the available historical readout times
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be
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\[
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t_0 < t_1 < \cdots < t_n \le t.
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\]
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For notational convenience, define
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\[
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t_{n+1}=t.
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\]
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The historical trajectory is partitioned into adjacent, non-overlapping
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intervals
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\[
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[t_i,t_{i+1}], \qquad i=0,\ldots,n.
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\]
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Let
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\[
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h_i = h(t_i), \qquad \Delta_i = t_{i+1}-t_i.
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\]
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The interval-level model-implied probability for disease \(d\) is
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\[
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q_{d,i}(t) = p_d(h_i,\Delta_i).
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\]
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\subsection{Model-Weighted Formed Burden}
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The model-weighted formed burden uses DeepHealth's own historical risk
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trajectory to quantify how strongly disease \(d\) is represented as formed
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burden by time \(t\). It is defined by noisy-or accumulation over historical
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intervals:
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\[
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z^{\mathrm{model}}_d(t)
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z_d(t)
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=
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1-\prod_{i=0}^{n}\left[1-q_{d,i}(t)\right].
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\]
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Equivalently,
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Informally, \(z_d(t)\) is the degree to which disease \(d\) is expressed in the
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patient by time \(t\). Unlike a raw diagnosis indicator, it is continuous and
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can reflect heterogeneity within the same ICD label.
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\section{Organ Involvement Index}
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The organ index is not a frailty score, health reserve score, or organ age. It
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is an organ involvement index. Let \(\mathcal{D}_k\) be the set of diseases
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assigned to organ/system \(k\). Define disease expression intensity as
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\[
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z^{\mathrm{model}}_d(t)
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\Lambda_d(t)=-\log\left[1-z_d(t)\right].
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\]
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The equal-weight organ involvement index is
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\[
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O_k(t)
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=
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1-\prod_{i=0}^{n}
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\left[
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1-p_d\!\left(h(t_i),t_{i+1}-t_i\right)
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\right],
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\qquad t_{n+1}=t.
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\]
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This definition uses each segment of the historical trajectory exactly once and
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therefore avoids repeatedly counting overlapping predictions from multiple
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historical states to the same query time.
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\subsection{Observed-Anchored Formed Burden}
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The observed-anchored formed burden treats historical diagnoses as factual
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evidence. Define the observed historical disease indicator
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\[
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o_d(t)
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=
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\mathbb{I}\left\{
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\exists j:\; \mathrm{event}_j=d,\; \mathrm{time}_j\le t
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\right\}.
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\]
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The observed-anchored version is
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\[
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z^{\mathrm{obs}}_d(t)=o_d(t).
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\]
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This version is closest to diagnosis-code burden measures such as HFRM: once a
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disease code has appeared before query time \(t\), the corresponding disease
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burden component is considered present. It is maximally auditable and aligned
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with code-based burden definitions, but it does not distinguish severity,
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recency, or residual impact among patients with the same historical diagnosis.
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\subsection{Choice of Formed Burden}
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The two definitions represent different semantics:
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\[
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z^{\mathrm{obs}}_d(t)
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=
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\text{observed diagnostic burden},
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\]
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\[
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z^{\mathrm{model}}_d(t)
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=
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\text{model-weighted state burden}.
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\]
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The observed-anchored version should be used when the goal is to reproduce or
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extend diagnosis-code burden measures. The model-weighted version should be used
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when the goal is to let DeepHealth assign a continuous burden strength based on
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the historical hidden-state trajectory. In the formulas below, \(z_d(t)\)
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denotes either \(z^{\mathrm{obs}}_d(t)\) or \(z^{\mathrm{model}}_d(t)\), depending
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on the selected BI variant.
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\subsection{Observed-Anchored versus Model-Weighted Burden}
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The observed-anchored and model-weighted definitions share the same purpose:
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both quantify disease burden already formed by query time \(t\), before adding
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future expected burden. They also use the same downstream BI equations; the only
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difference is the definition of \(z_d(t)\).
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Their key difference is the evidence treated as primary. The observed-anchored
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version treats diagnosis occurrence as the primary unit of evidence:
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\[
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z^{\mathrm{obs}}_d(t)=1
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\quad\text{once disease } d \text{ has been observed before } t.
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\]
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This is appropriate when the burden index is intended to remain close to
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diagnosis-code measures such as HFRM. It is transparent and robust to model
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miscalibration, but it treats all historical occurrences of the same disease as
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equally formed burden.
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The model-weighted version treats the DeepHealth risk trajectory as the primary
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unit of evidence:
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\[
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z^{\mathrm{model}}_d(t)
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=
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1-\prod_i
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\left[
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1-p_d\!\left(h(t_i),t_{i+1}-t_i\right)
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\right].
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\]
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This version can assign different burden strengths to the same observed disease
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depending on timing, surrounding history, extra-info context, and the hidden
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state trajectory. It may better reflect state-dependent burden intensity, but it
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can also downweight a disease that was truly observed if the model assigns low
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historical probability.
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Thus the two variants answer related but non-identical questions:
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\[
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z^{\mathrm{obs}}_d(t):
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\text{Has disease } d \text{ been recorded as part of the patient's history?}
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\]
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\[
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z^{\mathrm{model}}_d(t):
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\text{How strongly does the model-implied trajectory support burden from }
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d \text{ by time } t?
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\]
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For this reason, both should be considered useful sensitivity variants. The
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observed-anchored version is preferable for auditability and alignment with
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existing code-based indices. The model-weighted version is preferable when the
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goal is to use DeepHealth as a continuous state model and allow the learned
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trajectory to modulate burden strength.
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\subsection{Cumulative Intensity Form}
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Define the interval cumulative intensity
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\[
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\ell_d(h_i,\Delta_i)
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=
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-\log\left[1-p_d(h_i,\Delta_i)\right],
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\]
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and the accumulated historical intensity
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\[
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\Lambda^{\mathrm{model}}_d(t)=\sum_{i=0}^{n}\ell_d(h_i,\Delta_i).
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\]
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Then
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\[
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z^{\mathrm{model}}_d(t)=1-\exp[-\Lambda^{\mathrm{model}}_d(t)].
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\]
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For the exponential model,
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\[
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\ell_d(h_i,\Delta_i)=\lambda_d(h_i)\Delta_i,
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\]
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so
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\[
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z^{\mathrm{model}}_d(t)
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=
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1-\exp\left[
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-\sum_{i=0}^{n}
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\lambda_d\!\left(h(t_i)\right)(t_{i+1}-t_i)
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\right].
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\]
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For the Weibull model,
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\[
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\ell_d(h_i,\Delta_i)
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=
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\lambda_d(h_i)\Delta_i^{\rho_d(h_i)},
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\]
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so
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\[
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z^{\mathrm{model}}_d(t)
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=
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1-\exp\left[
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-\sum_{i=0}^{n}
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\lambda_d\!\left(h(t_i)\right)
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(t_{i+1}-t_i)^{\rho_d(h(t_i))}
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\right].
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\]
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\section{Future Expected Burden}
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The selected formed burden \(z_d(t)\) represents disease burden already formed
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by query time \(t\). It can be either the observed-anchored burden
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\(z^{\mathrm{obs}}_d(t)\) or the model-weighted burden
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\(z^{\mathrm{model}}_d(t)\). The current future risk from query time \(t\) to
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horizon \(\tau\) is
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\[
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p_d(h(t),\tau).
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\]
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The future expected newly formed burden for disease \(d\) is defined as
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\[
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f_d(t,\tau)
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=
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\left[1-z_d(t)\right]p_d(h(t),\tau).
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\]
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This term counts only the portion of disease burden that has not already formed
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by time \(t\). The total dynamic disease burden contribution is
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\[
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b_d(t,\tau)
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=
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z_d(t)+f_d(t,\tau).
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1-\exp\left(
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-\sum_{d\in\mathcal{D}_k}\Lambda_d(t)
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\right).
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\]
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Equivalently,
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\[
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b_d(t,\tau)
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O_k(t)
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=
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1-
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\left[1-z_d(t)\right]
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\left[1-p_d(h(t),\tau)\right].
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\prod_{d\in\mathcal{D}_k}
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\left[1-z_d(t)\right].
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\]
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Thus \(b_d(t,\tau)\) can be interpreted as the probability that disease burden
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for \(d\) has formed by time \(t\) or will newly form within the future horizon
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\(\tau\).
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\section{Burden Index Definition}
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Let \(A \in \mathbb{R}_{\ge 0}^{K \times D}\) be a disease-to-burden mapping
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matrix. The historical, future, and total burden indices for dimension \(k\)
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are
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Thus \(O_k(t)\in[0,1]\) is the probability-like degree to which organ/system
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\(k\) is involved by at least one related disease process. In the current
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version all diseases assigned to the same organ are equally weighted; this is a
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first-stage structural definition. Future versions can introduce
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organ-specific disease weights \(\alpha_{k,d}\):
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\[
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\operatorname{BI}^{\mathrm{hist}}_k(t)
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O_k(t)
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=
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\sum_{d=1}^{D} A_{k,d} z_d(t),
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\]
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\[
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\operatorname{BI}^{\mathrm{future}}_k(t,\tau)
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=
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\sum_{d=1}^{D}
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A_{k,d}
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\left[1-z_d(t)\right]p_d(h(t),\tau),
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\]
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and
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\[
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\operatorname{BI}^{\mathrm{total}}_k(t,\tau)
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=
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\operatorname{BI}^{\mathrm{hist}}_k(t)
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+
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\operatorname{BI}^{\mathrm{future}}_k(t,\tau).
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\]
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Equivalently,
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\[
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\operatorname{BI}^{\mathrm{total}}_k(t,\tau)
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=
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\sum_{d=1}^{D}
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A_{k,d}
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\left\{
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1-
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\left[1-z_d(t)\right]
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\left[1-p_d(h(t),\tau)\right]
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||||
\right\}.
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1-\exp\left(
|
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-\sum_{d\in\mathcal{D}_k}\alpha_{k,d}\Lambda_d(t)
|
||||
\right).
|
||||
\]
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For OBI, \(A=A^{\mathrm{organ}}\). For FBI, \(A=A^{\mathrm{func}}\).
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\section{Organ List}
|
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\section{Constructing the Mapping Matrices}
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|
||||
\subsection{Organ Mapping Matrix}
|
||||
|
||||
The organ mapping matrix should be constructed from code taxonomy or validated
|
||||
clinical grouping systems rather than manually assigned arbitrary weights. In
|
||||
the current ICD-token setting, the first version can use ICD chapters or
|
||||
predefined ICD ranges to construct a sparse disease-to-organ mask
|
||||
\[
|
||||
M^{\mathrm{organ}}_{k,d}\in\{0,1\}.
|
||||
\]
|
||||
Examples include:
|
||||
The organ/system categories are inspired by organ-age studies, especially
|
||||
organ-specific plasma proteomic aging models, and are adapted to ICD disease
|
||||
labels. The current list is:
|
||||
\begin{center}
|
||||
\begin{tabular}{ll}
|
||||
\toprule
|
||||
Dimension & Example ICD groups \\
|
||||
ID & Label \\
|
||||
\midrule
|
||||
Heart/vascular & I00--I99, optionally split into cardiac and vascular groups \\
|
||||
Brain/neurological & G00--G99, F00--F09, I60--I69 \\
|
||||
Kidney/urogenital & N00--N39, especially N17--N19 \\
|
||||
Lung/respiratory & J00--J99 \\
|
||||
Metabolic/endocrine & E00--E90 \\
|
||||
Liver/digestive & K00--K93, especially K70--K77 \\
|
||||
Musculoskeletal & M00--M99 \\
|
||||
brain\_neurologic & Brain and neurologic system \\
|
||||
heart & Heart \\
|
||||
artery\_vascular & Artery and vascular system \\
|
||||
immune & Immune and infection-related system \\
|
||||
intestine\_digestive & Intestine and digestive system \\
|
||||
kidney & Kidney and urinary system \\
|
||||
liver & Liver \\
|
||||
lung & Lung and respiratory system \\
|
||||
muscle\_musculoskeletal & Muscle and musculoskeletal system \\
|
||||
pancreas\_endocrine & Pancreas and endocrine system \\
|
||||
adipose\_metabolic & Adipose and metabolic system \\
|
||||
female\_reproductive & Female reproductive system \\
|
||||
male\_reproductive & Male reproductive system \\
|
||||
neoplasm & Neoplasm \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\end{center}
|
||||
The neoplasm category is retained as a disease-system category rather than
|
||||
forced into a single anatomical organ. Sex-specific reproductive diseases are
|
||||
separated into female and male reproductive systems.
|
||||
|
||||
The simplest organ weights are
|
||||
\section{DeepHealth-HFRS Frailty Risk Index}
|
||||
|
||||
The original UK-HFRS is a weighted sum over binary disease occurrence:
|
||||
\[
|
||||
A^{\mathrm{organ}}_{k,d}=M^{\mathrm{organ}}_{k,d}.
|
||||
\]
|
||||
If longitudinal organ endpoint labels are available, the weights can be learned
|
||||
under the mask:
|
||||
\[
|
||||
A^{\mathrm{organ}}_{k,d}\ge 0,
|
||||
\operatorname{HFRS}^{\mathrm{obs}}(t)
|
||||
=
|
||||
\sum_{d\in\mathcal{D}_{\mathrm{HFRS}}}
|
||||
w^{\mathrm{HFRS}}_d\,o_d(t),
|
||||
\qquad
|
||||
A^{\mathrm{organ}}_{k,d}=0
|
||||
\quad\text{if}\quad
|
||||
M^{\mathrm{organ}}_{k,d}=0.
|
||||
o_d(t)\in\{0,1\}.
|
||||
\]
|
||||
This keeps the projection clinically interpretable while allowing data-driven
|
||||
calibration.
|
||||
|
||||
\subsection{Functional Mapping Matrix}
|
||||
|
||||
The functional mapping matrix should be anchored by a validated frailty-related
|
||||
diagnosis code set whenever possible. CIHI-HFRM or a closely related Hospital
|
||||
Frailty Risk Measure provides a suitable starting point because it defines
|
||||
frailty burden from diagnosis codes and associated weights.
|
||||
|
||||
Let \(w^{\mathrm{HFRM}}_d\ge 0\) be the HFRM weight mapped to DeepHealth disease
|
||||
token \(d\). If the HFRM code list is more granular than the DeepHealth ICD
|
||||
token vocabulary, weights should be mapped by code prefix. For three-character
|
||||
ICD tokens, a conservative default is
|
||||
DeepHealth-HFRS keeps the published UK-HFRS weights and replaces the binary
|
||||
disease state with the continuous DeepHealth disease expression rate:
|
||||
\[
|
||||
w^{\mathrm{HFRM}}_d
|
||||
\operatorname{HFRS}^{\mathrm{DH}}(t)
|
||||
=
|
||||
\max_{c:\, c \text{ maps to token } d}
|
||||
w^{\mathrm{HFRM}}_c.
|
||||
\]
|
||||
|
||||
For total functional burden, the one-dimensional mapping is
|
||||
\[
|
||||
A^{\mathrm{func,total}}_{1,d}
|
||||
=
|
||||
w^{\mathrm{HFRM}}_d.
|
||||
\]
|
||||
For domain-specific functional burden, define a grouping mask
|
||||
\[
|
||||
G_{k,d}\in\{0,1\},
|
||||
\]
|
||||
where \(G_{k,d}=1\) means HFRM-associated disease token \(d\) belongs to
|
||||
functional burden domain \(k\). Then
|
||||
\[
|
||||
A^{\mathrm{func}}_{k,d}
|
||||
=
|
||||
G_{k,d} w^{\mathrm{HFRM}}_d.
|
||||
\]
|
||||
|
||||
Candidate functional domains include mobility, cognition, mood, sensory,
|
||||
nutrition, infection or immune vulnerability, functional dependence, and
|
||||
comorbidity burden. These domain labels should be treated as diagnostic-burden
|
||||
proxies unless direct functional measurements are available for calibration.
|
||||
|
||||
\section{Normalization and Reporting}
|
||||
|
||||
The raw burden index is an additive weighted burden:
|
||||
\[
|
||||
\operatorname{BI}_k(t,\tau)
|
||||
=
|
||||
\sum_d A_{k,d} b_d(t,\tau).
|
||||
\]
|
||||
For interpretability, the system should report the decomposition
|
||||
\[
|
||||
\operatorname{BI}^{\mathrm{hist}}_k(t),
|
||||
\sum_{d\in\mathcal{D}_{\mathrm{HFRS}}}
|
||||
w^{\mathrm{HFRS}}_d\,z_d(t),
|
||||
\qquad
|
||||
\operatorname{BI}^{\mathrm{future}}_k(t,\tau),
|
||||
\qquad
|
||||
\operatorname{BI}^{\mathrm{total}}_k(t,\tau).
|
||||
z_d(t)\in[0,1].
|
||||
\]
|
||||
This is a natural continuous extension of the original HFRS, so it can still be
|
||||
called a frailty risk index. The semantic change is not the HFRS weight system;
|
||||
the change is the disease state variable.
|
||||
|
||||
Optionally, a normalized burden can be reported as
|
||||
\section{Current Implementation}
|
||||
|
||||
The current code computes historical current-state indices only. No future
|
||||
horizon is used. For each landmark age \(t\), it outputs:
|
||||
\begin{itemize}[leftmargin=*]
|
||||
\item \(z_d(t)\) internally as model-implied disease expression;
|
||||
\item \(O_k(t)\) as equal-weight organ involvement;
|
||||
\item \(\operatorname{HFRS}^{\mathrm{DH}}(t)\) as DeepHealth-HFRS frailty
|
||||
risk.
|
||||
\end{itemize}
|
||||
The output table uses the columns
|
||||
\[
|
||||
\widetilde{\operatorname{BI}}_k(t,\tau)
|
||||
=
|
||||
\frac{
|
||||
\sum_d A_{k,d} b_d(t,\tau)
|
||||
}{
|
||||
\sum_d A_{k,d} + \epsilon
|
||||
},
|
||||
\texttt{index\_type},\quad
|
||||
\texttt{index\_id},\quad
|
||||
\texttt{index\_label},\quad
|
||||
\texttt{index\_value}.
|
||||
\]
|
||||
where \(\epsilon>0\) prevents division by zero. This normalized score lies on a
|
||||
dimension-comparable scale when \(b_d(t,\tau)\in[0,1]\) and \(A_{k,d}\ge 0\).
|
||||
|
||||
For cohort-level interpretation, an additional percentile score can be computed
|
||||
within age- and sex-specific reference strata:
|
||||
\[
|
||||
\operatorname{PercentileBI}_k(t,\tau)
|
||||
=
|
||||
\operatorname{rank}_{\mathrm{age,sex}}
|
||||
\left(
|
||||
\operatorname{BI}^{\mathrm{total}}_k(t,\tau)
|
||||
\right).
|
||||
\]
|
||||
This percentile is a relative burden ranking, not a health reserve percentage.
|
||||
|
||||
\section{Validation}
|
||||
|
||||
OBI and FBI should be validated against different endpoints.
|
||||
|
||||
For OBI, validation endpoints should be organ-system-specific future events, for
|
||||
example cardiac events for heart/vascular burden, stroke or dementia for
|
||||
brain/neurological burden, CKD progression for kidney burden, and respiratory
|
||||
events for lung burden.
|
||||
|
||||
For FBI, validation should use CIHI-HFRM-style frailty burden, frailty-related
|
||||
diagnosis endpoints, hospitalization, mortality, care dependence proxies, or
|
||||
direct functional outcomes if available. If direct functional labels such as
|
||||
ADL/IADL, gait speed, grip strength, cognitive tests, or recovery measures are
|
||||
not available, FBI should be reported as a diagnosis-risk-based functional
|
||||
burden proxy rather than a direct functional reserve measure.
|
||||
|
||||
\section{Summary}
|
||||
|
||||
DeepHealth Burden Indices transform disease-level risk predictions into
|
||||
interpretable burden representations. Formed burden can be defined either as
|
||||
observed-anchored burden \(z^{\mathrm{obs}}_d(t)\), which follows factual
|
||||
diagnosis history, or as model-weighted burden \(z^{\mathrm{model}}_d(t)\),
|
||||
which accumulates DeepHealth's predicted interval risks along the hidden-state
|
||||
trajectory. The future expected burden is the residual future risk among disease
|
||||
burden not already formed. OBI uses anatomical disease groupings to summarize
|
||||
where pathological burden is concentrated. FBI uses CIHI-HFRM-style
|
||||
frailty-related diagnosis weights to summarize functional vulnerability burden.
|
||||
Together, they provide two complementary views of disease burden while allowing
|
||||
the formed-burden semantics to be chosen explicitly.
|
||||
|
||||
\end{document}
|
||||
|
||||
Reference in New Issue
Block a user