Predictive medicine involves predicting the probability of disease and implementing measures to either prevent the disease altogether or significantly decrease its impact upon the patient. The outcomes of predictive medicine are often applied to the care of individual patients, but may also inform the deployment of resources to entire populations at high risk.

The goal of predictive medicine is to predict the probability of future disease so that healthcare professionals and the patient themselves can be proactive in implementing lifestyle modifications and increased physician surveillance, such as regular skin exams, mammograms, or colonoscopies. Predictive medicine changes the paradigm of medicine from being reactive to being proactive, and has the potential to significantly extend the duration of health and to decrease the incidence, prevalence and cost of diseases.

Much attention has been focused on the availability of genetic makers of vulnerability to specific illnesses. However the accurate capture of phenotypic (e.g. height and weight, blood pressure), environmental factors (e.g. smoking, alcohol consumption) and other lifestyle factors (e.g. exercise, nutrition, quality of life) is not to be overlooked. For example:

1. Patient is a smoker and has ischemic heart disease ? predict excess risk of myocardial infarction

2. Patient has BRCA1 gene and is a 40 year old woman ? predict (excess) risk of breast cancer

SNOMED CT can help to support predictive medicine by:

• Helping to identify clinical trial candidates

• Helping to analyze clinical data, such as family history, lifestyle and environmental findings, to improve predictive capabilities

• Providing a link between patient data and risk assessment rules, so that rules can be triggered based on subsumption of codes recorded in clinical data. For example, matching against patient records could be improved by defined the above rules as:

Risk: 22298006 |myocardial infarction| 2. Criteria: 412734009 |BRCA1 gene mutation positive| Risk: 254837009 |breast cancer|

With an ever increasing volume of medical literature and clinical reports, it is becoming increasingly important to be able to meaningfully search this information. A major application for Natural Language Processing technologies is to index collections of free text transcripts or documents such that topic specific searches may be run on them. The challenge is to move beyond the limitations of plain keyword searching strategies towards more advanced search techniques, which return ranked matches with high sensitivity and specificity. Clinical searches may be performed over documents within an electronic library, within medical records, or on the internet. Examples of searches include:

• "Show me articles on this website concerned with inflammatory bowel disease"

• "Does this patient have transcripts in their record suggesting a heart rhythm disturbance?"

SNOMED CT was used in techniques developed by Koopman to improve search performance by addressing vocabulary mismatch (using synonyms, e.g. hypertension vs high blood pressure), granularity mismatch (using hierarchical relationships, e.g. antipsychotic vs haloperidol), conceptual implication (using defining relationships, e.g. from renal cyst infer kidney) and inferences of similarity (e.g. using subset membership, e.g. comorbidities anxiety and depression). Koopman also assigned a measure of similarity to each SNOMED CT relationship type, and use this weighting to determine the relevance of each document.

Some commercial tools also provide semantic search, including Cerner's semantic search tool.

Clinical research is a branch of healthcare science that determines the safety and effectiveness of medications, devices, diagnostic products, and treatment regimens intended for human use. Clinical research may be used for the prevention, treatment, diagnosis or for relieving symptoms of a disease. In contrast to clinical practice, which applies established treatment regimes, clinical research collects evidence to extend knowledge and establish the value of novel treatments and other patient management practices.

Clinical research typically involves the analysis of data from well-defined and homogenous groups of patients with a specific disease, at a specific stage, receiving similar treatments and often without significant co-morbidities. The data may be captured prospectively or retrieved retrospectively.

SNOMED CT helps clinical research activities by assisting in the identification of clinical trial candidates, enabling the powerful analysis of trial data, supporting predictive medicine, and improving the effectiveness of semantic search over clinical knowledge.

In this section, we discuss three key aspects to clinical research that can benefit from the use of SNOMED CT: identification of clinical trial candidates, predictive medicine, and semantic search.

SNOMED CT can be used to assist the process of identifying clinical trial candidates for recruitment into formal clinical trials. Subsets of findings, procedures or medications can be used to filter trial candidates based on their clinical conditions or treatments. Subsumption techniques can be used to identify suitable candidates, irrespective of the level of granularity in which their clinical data is stored. SNOMED CT defining relationships can be used in a number of ways – for example, identifying patients with diseases of specific anatomical sites, with certain morphologies; patients who are taking medications with specific ingredients or dose forms; and patients who have had procedures on a specific body site.

Commercial tools, which can be used to support clinical research include Cerner's clinical research module (PowerTrials), which offers patient identification functionality.