How does syndromic analysis work?

July 30, 2022
By Vishal N. Patel, MD PhD


A syndromic analysis is predicated on 2 probabilistic assumptions:

1.      If you have an ailment – a sign, symptoms, disorder, or diagnosis – then you are likely to have other ailments.

2.      There is a non-random chance that some of your multiple ailments are related to each other.

The methodology below employs statistical inference to evaluate real-world evidence and establish whether a relationship exists between your ailments.

What is syndromic analysis?

To begin a syndromic analysis, we, first, gather your ailments, both present and past. I call these your “biomedical labels,” as they can include any signs, symptoms, disorders, or diagnoses.

  • Signs: high blood pressure, spiking heart rate, clammy hands
  • Symptoms: costochondral pain, palpitations, headaches, fatigue
  • Diagnoses: paroxysmal ventricular tachycardia, irritable bowel syndrome, rheumatoid arthritis

In addition to biomedical labels, we will also inquire about your phenotypic labels: your anthropometric characteristics (height &weight), age, sex, and any biomarkers you have access to.


Based on the array of biomedical and phenotypic labels, the next step is to attempt to “classify” you. Although there are no robust biomedical classification systems based on phenotypic data today, I elect to use the classification schema from Ayurveda, which classifies an individual into one of 3 subtypes, or doshas. Classification primarily supports inference: by grouping you with similar individuals, we can infer traits, characteristics, and behaviors you may possess. In addition, classification also provides important personal context when you are grappling with an ailment; while a single label (a sign, symptom, or disease) may not describe you, a multivariate class engenders feelings of identification.

Comorbidity Analysis

Next, we undertake a comorbidity analysis. Comorbidity analyses are based on reviewing empirical evidence for the co-occurrence of pairs of signs, symptoms, or diseases. Here, the comorbidity analysis serves to (1) identify missed phenotypic variables and (2) establish the validity of co-associations between phenotypic variables. We conduct a comorbidity analysis by searching the peer-reviewed literature. In addition, we leverage published comorbidity databases - amassed from thousands to millions of patient records - to find evidence of co-association.

Based on the comorbidity analysis, we will identify a subset of phenotypic variables that co-occurs frequently and more significantly than by chance alone; we call this your subnetwork. Then, we analyze this subnetwork to determine if a plausible physiological explanation exists. We also employ the parsimony principle here: we search for the simplest physiological explanation that explains the subnetwork most completely. When possible, we also offer explanations from Eastern medical traditions, as the Eastern etiologic theories can be intuitively grasped and can be motivating.


Next, when pertinent, we will also search for scientific evidence to identify phenotypic variables in the past and the future. Past variables – such as a significant association with childhood trauma – can help place current ailments in the context of one’s history, reducing the unfamiliarity that a new biomedical label often brings. Future variables – such as the risk of cardiovascular disease, or the risk of mortality – help answer questions about one’s disease course and what the future may hold.

Finally, we provide information on remedies applicable to your subnetwork. We focus on identifying remedies that have peer-reviewed evidence demonstrating their efficacy and safety.

What a syndromic analysis is not:

  • A syndromic analysis is not a diagnosis. Rather, it is an interpretation of a label – be it a diagnostic label, or a self-diagnosed symptom or disease – in the context of empirical evidence.
  • A syndromic analysis does not indicate a treatment. A syndromic analysis simply looks for evidence of co-occurrence amongst your biomedical labels and phenotypic variables. By identifying which signs, symptoms, and diseases are not independent, many people are motivated to find more parsimonious remedies that improve the state of their whole system, not just the individual parts.
  • A syndromic analysis is not new. This approach is inspired by the great syndromic traditions of Ayurveda and Traditional Chinese Medicine.

If you are interested, please book a syndromic analysis with me here.