HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology

Deep phenotyping: Embracing complexity and temporality—Towards scalability, portability, and interoperability

“…Datta, S A systematic review of NLP on cancer notes [15] Liu, Q Symptom extraction for patient stratification [16] Lyudovyk, O NLP on pathology notes for subtyping [17] Liu, C Ensemble of NLP for better portability Requirement 2: Standardization [18] Hong, N A FHIR-based EHR phenotyping framework [19] Shang, N An empirical study of "making phenotyping work visible" that demonstrates the need for standardized processes [20] Hripcsak, G Demonstrate OMOP's value in improving phenotyping algorithms' portability [21] Ostropolets, A Adapting EHR phenotypes to claims data using OMOP Common Data Model [22] Reps, J OMOP CDM-based probabilistic phenotyping algorithms using self-reported data [23] Swerdel, J OMOP CDM-based standardized phenotype evaluation algorithms [24] Warner, J Expansion of OMOP CDM to cancer phenotypes [25] Shen, F Extension of HPO using embedding of phenotype knowledge resources Existing standards often have limitations in their content coverage. Warner et al extended the widely adopted OMOP CDM to expand coverage within the cancer domain by defining a new standard vocabulary for chemotherapy regimen [24].…”

“…Warner et al extended the widely adopted OMOP CDM to expand coverage within the cancer domain by defining a new standard vocabulary for chemotherapy regimen [24]. Similarly, Shen et al developed a scalable knowledge engineering method to enrich node embedding for HPO [25]. The authors parsed disease-phenotype associations contained in heterogeneous knowledge resources such as OMIM and Orphanet to enrich non-inheritance relationships among phenotypic nodes in HPO.…”

“…First author NLP STND DL SRC SUB Disease Focus eMERGE OHDSI [14] Datta, S X [15] Liu, Q X X [16] Lyudovyk, O X X X Cancer X [17] Liu, C X X X [18] Hong, N X [19] Shang, N X X X [20] Hripcsak, G X X X [21] Ostropolets, A X X X [22] Reps, J X X X [23] Swerdel, J X X [24] Warner, J X Cancer X [25] Shen, F X X X X [26] Trace, JM X Parkinson's [27] Mate, S X [28] Meng, W Cancer [29] Zhao, J X X Cardiovascular X [30] Chen, X X X Rare disease [31] Xu, Z X X X Acute kidney injury [32] Zhang, L X X [33] Chen, P X TOTAL 6 12 6 6 3 5 6…”

Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions

“…In some cases, additional works are cited to provide context. A total of 15 articles were finally selected for inclusion [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

“…Vector embedding approaches are being adopted in the translational research community. For instance, HPO2Vec+ embeds the HPO with disease phenotype associations and was used to stratify rare disease patients in EHR data at the Mayo Clinic [11]. Another approach to ontology-guided graph embedding uses a convolutional neural network to encode input phrases and then rank medical concepts based on the similarity in that space.…”