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* Predefined personal-research questions for the patient's own data (NLQ page).
* Three questions, each answered from the patient's own data — multi-year
* fitness / lab / nutrition trends PLUS the ePA (elektronische Patientenakte)
* events that shaped them (infections spike CRP, the knee surgery dips VO₂max,
* etc.). The events are also marked on the trend charts. EHDS Art. 3 / GDPR
* Art. 15 primary use, own records only. Synthetic · illustrative.
*/
import type { TrendSeries } from "@/lib/journey-config";
/** A discrete clinical event from the patient's ePA (electronic patient record). */
export interface EpaEvent {
date: string;
label: string;
type: "Infection" | "Surgery" | "Vaccination" | "Diagnosis";
}
export interface ResearchQA {
id: "sport" | "nutrition" | "breathing";
question: string;
answer: string;
/** provenance line — always "own data only" */
source: string;
/** keywords used to resolve a free-text question to this answer */
keywords: string[];
/** multi-year trend series (events from `events` are marked on them) */
trends: TrendSeries[];
/** ePA events shown beneath, and marked on, the trends */
events: EpaEvent[];
}
/** Fraction (0–1) of `date` within [from, to]. */
function frac(from: string, to: string, date: string): number {
const f = Date.parse(from);
const t = Date.parse(to);
return (Date.parse(date) - f) / (t - f);
}
/**
* Generate `n` evenly-spaced points from `start` → `end`, adding a transient
* bump near each event date (positive = spike, negative = dip), so the curve
* reflects the ePA events.
*/
function gen(
from: string,
to: string,
start: number,
end: number,
n: number,
bumps: { date: string; mag: number }[] = [],
decimals = 0,
): number[] {
const f = Math.pow(10, decimals);
const out: number[] = [];
for (let i = 0; i < n; i++) {
const x = i / (n - 1);
let v = start + (end - start) * x;
for (const b of bumps) {
const dist = Math.abs(x - frac(from, to, b.date)) * (n - 1);
v += b.mag * Math.max(0, 1 - dist / 1.5);
}
out.push(Math.round(Math.max(0, v) * f) / f);
}
return out;
}
const SPORT_FROM = "2020-06-01";
const SPORT_TO = "2026-03-01";
const NUTR_FROM = "2023-06-01";
const NUTR_TO = "2026-03-01";
const BREATH_FROM = "2021-06-01";
const BREATH_TO = "2026-03-01";
export const personalResearchQA: ResearchQA[] = [
{
id: "sport",
question:
"I increased my daily sport routine over the last 3 months — do you see any effect?",
answer:
"Yes — your cardio-fitness improved: VO₂max recovered after your 2020 knee arthroscopy and has climbed to 47 ml/kg/min, with heart-rate variability up too. The dip on the graph marks the surgery; you're now fitter than before it.",
source:
"From your fitness band + ePA (elektronische Patientenakte) — your own data only.",
keywords: [
"sport",
"exercise",
"run",
"running",
"training",
"workout",
"fitness",
"vo2",
"vo₂",
"cardio",
"heart rate",
"resting hr",
"gym",
"active",
"steps",
"knee",
],
trends: [
{
label: "VO₂max",
unit: "ml/kg/min",
color: "#CA6F1E",
current: "47",
goodDirection: "up",
from: SPORT_FROM,
to: SPORT_TO,
points: gen(SPORT_FROM, SPORT_TO, 43, 47, 18, [
{ date: "2020-09-22", mag: -6 },
]),
},
{
label: "HRV",
unit: "ms",
color: "#2471A3",
current: "48",
goodDirection: "up",
from: SPORT_FROM,
to: SPORT_TO,
points: gen(SPORT_FROM, SPORT_TO, 40, 48, 18, [
{ date: "2020-09-22", mag: -7 },
]),
},
],
events: [
{
date: "2020-09-22",
label: "Knee arthroscopy (meniscus repair)",
type: "Surgery",
},
],
},
{
id: "nutrition",
question:
"I changed my nutrition over 3 months — how are my cardiovascular and pre-diabetes markers?",
answer:
"Trending the right way — inflammation (CRP) is down and your Mediterranean-plan adherence reached 86%. The markers show when your pre-diabetes (2023) and mixed hyperlipidaemia (2024) were diagnosed; both are improving since you changed your nutrition.",
source: "From your lab panel, nutrition plan + ePA — your own data only.",
keywords: [
"nutrition",
"diet",
"food",
"eat",
"eating",
"meal",
"cardiovascular",
"cardio risk",
"diabetes",
"pre-diabetes",
"prediabetes",
"cholesterol",
"ldl",
"hyperlipidaemia",
"weight",
"mediterranean",
"vitamin d",
],
trends: [
{
label: "CRP",
unit: "mg/l",
color: "#7D3C98",
current: "0.8",
goodDirection: "down",
from: NUTR_FROM,
to: NUTR_TO,
points: gen(NUTR_FROM, NUTR_TO, 1.8, 0.8, 14, [], 1),
},
{
label: "Adherence",
unit: "%",
color: "#1E8449",
current: "86",
goodDirection: "up",
from: NUTR_FROM,
to: NUTR_TO,
points: gen(NUTR_FROM, NUTR_TO, 68, 86, 14),
},
],
events: [
{
date: "2023-10-02",
label: "Pre-diabetes (impaired fasting glucose)",
type: "Diagnosis",
},
{
date: "2024-03-15",
label: "Mixed hyperlipidaemia",
type: "Diagnosis",
},
],
},
{
id: "breathing",
question:
"I've been doing daily breathing exercises — any effect on stress and inflammation?",
answer:
"Likely yes — CRP fell to 0.8 mg/L and your HRV improved. The spikes on the graph line up with your ePA infections (COVID-19 in 2022, bronchitis in 2023, flu in 2024); between them, and since the breathing exercises, your inflammation is low. Your seasonal influenza vaccinations are up to date.",
source: "From your lab panel, fitness band + ePA — your own data only.",
keywords: [
"breath",
"breathing",
"stress",
"inflammation",
"crp",
"oxygen",
"spo2",
"meditation",
"relax",
"calm",
"anxiety",
"hrv",
"recovery",
"asthma",
"respiratory",
"infection",
"infections",
"covid",
"flu",
"bronchitis",
"vaccine",
"vaccination",
"immunization",
],
trends: [
{
label: "CRP",
unit: "mg/l",
color: "#7D3C98",
current: "0.8",
goodDirection: "down",
from: BREATH_FROM,
to: BREATH_TO,
points: gen(
BREATH_FROM,
BREATH_TO,
1.4,
0.8,
20,
[
{ date: "2022-11-08", mag: 4.5 },
{ date: "2023-03-20", mag: 3.5 },
{ date: "2024-12-03", mag: 2.2 },
],
1,
),
},
{
label: "HRV",
unit: "ms",
color: "#2471A3",
current: "48",
goodDirection: "up",
from: BREATH_FROM,
to: BREATH_TO,
points: gen(BREATH_FROM, BREATH_TO, 36, 48, 20, [
{ date: "2022-11-08", mag: -7 },
{ date: "2024-12-03", mag: -4 },
]),
},
],
events: [
{
date: "2022-11-08",
label: "COVID-19, mild — recovered",
type: "Infection",
},
{
date: "2023-03-20",
label: "Acute bronchitis — resolved",
type: "Infection",
},
{
date: "2024-12-03",
label: "Influenza (seasonal) — resolved",
type: "Infection",
},
{
date: "2025-10-15",
label: "Influenza vaccine 2025/26",
type: "Vaccination",
},
{
date: "2021-09-09",
label: "Mild intermittent asthma",
type: "Diagnosis",
},
],
},
];
/**
* Resolve a free-text question to one of the predefined answers by keyword
* overlap (the static demo has no LLM). Returns the best match, or null.
*/
export function matchPersonalResearch(text: string): ResearchQA | null {
const t = text.toLowerCase();
// Word-boundary match so "run" → "running" but not "drunk", and "eat" does
// not match "weather".
const hit = (k: string) =>
new RegExp(`\\b${k.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")}`, "i").test(t);
let best: ResearchQA | null = null;
let bestHits = 0;
for (const qa of personalResearchQA) {
const hits = qa.keywords.filter(hit).length;
if (hits > bestHits) {
bestHits = hits;
best = qa;
}
}
return bestHits > 0 ? best : null;
}
/** Markers for an event list on a series with a date range (for TrendChart). */
export function eventMarkers(
s: TrendSeries,
events: EpaEvent[],
colorOf: (type: EpaEvent["type"]) => string,
) {
Iif (!s.from || !s.to) return [];
return events
.map((e) => ({
x: frac(s.from!, s.to!, e.date),
color: colorOf(e.type),
label: `${e.type}: ${e.label}`,
}))
.filter((m) => m.x >= -0.02 && m.x <= 1.02)
.map((m) => ({ ...m, x: Math.max(0, Math.min(1, m.x)) }));
}
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