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| On the Concepts and Approaches of Computable Knowledge in Biomedical and Health Sciences |
| Du Jian1, Kong Guilan1, Li Pengfei1,2, Bai Yongmei1, Zhang Luxia1 |
1.National Institute of Health Data Science, Peking University, Beijing 100191
2.Advanced Institute of Information Technology, Peking University, Hangzhou 226019 |
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Abstract “Computable knowledge” focuses on transforming human-readable knowledge into machine-executable forms by extracting and programming processes on digital knowledge objects. It can be regarded as the “keystone” in supporting the massive knowledge application in the cycle of learning health systems, i.e., “from data to knowledge, from knowledge to practice, and then from practice to data.” This concept has become a new field of research in health data science, and it also provides a new paradigm for digital library and knowledge computation research in the field of library and information science. This study proposes two approaches to making medical knowledge computable. One is a data-mining-driven approach. Computable knowledge can be extracted from the data in tables of medical literature, expressed in code, and managed in the Knowledge Grid (K-Grid). For example, a machine-executable version of the predictive model can be encoded in any appropriate computer language. When given an instance of data about an individual, this encoded model can quickly and accurately generate a risk prediction or useful advice. The second is a text-mining-driven approach that extracts Subject-Predicate-Object (SPO) triples from an unstructured text, such as the assertions in clinical guidelines and medical literature. By incorporating the evidence and data into a given SPO triple, we can calculate the confidence score for such a knowledge unit. The SPO triples can be stored in graph databases (K-Graph) for automatic question answering for a specific condition, such as treatment recommendations ranked by the confidence level to support medical intervention decision-making. Several challenges for the development and application of computable medical knowledge have been discussed. We hope to introduce an interdisciplinary approach to investigating computable medical knowledge and provide conceptual and technological preparations for the learning health system in China.
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Received: 01 February 2021
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