"We are inviting our international colleagues to help continue development of these valuable tools", stated Christopher Chute, M.D., Dr.P.H., Mayo Clinic bioinformatics expert and senior consultant on the project. "By making it an open-source initiative, we hope to enable wide use of these NLP tools so medical advancements can happen faster and more efficiently."
"The recently passed American Recovery and Reinvestment Act promises to accelerate the adoption of electronic medical records", stated Dan Pelino, IBM's general manager, Global Healthcare & Life. "Because the success of such reform rides on delivering interconnected and intelligent information to health care professionals everywhere, Mayo and IBM are tapping into the collaborative power of the open-source community to speed the development of Natural Language Processing (NLP). Adoption of this technology will provide physicians with insights into each patient's condition, allowing them to electronically retrieve the exact knowledge they seek from patient health records rather than reading through every record provided, as they must do today." Patient privacy is a main concern and consideration; all current and any future required safeguards and regulations will be adhered to strictly.
NLP is a relatively new and specialized area within computer science dealing with computational methods for understanding human language. In medicine, clinical NLP systems process the vast repositories of text generated by patient-clinician interactions. Such systems categorize and structure it according to standard nomenclature - in this case focusing on terms used in a range of medical specialties - that will ultimately speed data searches for both diagnoses and medical research. These NLP platforms or "pipelines" aid indexing and searching electronic medical records within institutions to quickly find similar cases or conditions, so physicians are not reliant solely on their own clinical experience in analysing a problem. Researchers may also use these tools to aid retrospective epidemiological studies or do groundwork for new clinical trials.
"Large-scale information extraction from the clinical narrative is a vital component in advancing translational research and patient care", added Guergana Savova, Ph.D., medical informatics specialist and Mayo's NLP lead on the project. "It 'unlocks' the clinical textual data that resides in huge repositories. Such technology would allow for large-scale data aggregation, analyses and usage - just imagine the power of data from millions of patients."
"There is a treasure trove of historical unstructured data that provides essential information for the study of disease progression, treatment effectiveness and long-term outcome which NLP systems make available to clinicians and researchers", stated Anni Coden Ph.D., IBM's NLP principal on the project. "Such data can provide guidance for prospective studies and furthermore facilitate the integration of data from multi-modal data sources."
As an increasing percentage of health care and academic medical centres adopt electronic medical records, searching and extracting information from them in an automated fashion becomes essential. Mayo Clinic and IBM jointly developed a system for extracting information from more than 25 million free-text clinical notes based on IBM's open-source Unstructured Information Management Architecture (UIMA). As part of the system, developers build strings of "annotators" that become a pipeline, allowing physicians to mine the text for references of specific conditions, drugs, diseases, signs and symptoms; anatomical areas or organs; or treatment procedures.
IBM and Mayo Clinic also developed a system to extract cancer disease characteristics from unstructured pathology reports to facilitate "consistent retrieval and transmission of cancer cases". The system extracts tumour characteristics, lymph node status and metastatic disease information enabling the automatic computation of cancer stage, which is critical to determine optimal treatment.
The two clinical text solutions released open-source by Mayo Clinic and IBM aim at processing two specific types of notes. Clinical notes describe patient-physician encounters, while pathology reports center around tissue findings. Both options are already adding value for Mayo and its patients:
- Physicians can research past records to examine earlier cases of rare conditions, thereby "conferring" with their colleagues across time to aid diagnosis and treatment decisions.
- Retrospective studies of tissue samples can propel new research findings, as happened with a major breast cancer finding at Mayo in 2008.
- Enhanced ability to mine data and determine potential study factors or participants has already enabled individualized medicine treatments in psychiatric care.