Projects per year
Personal profile
Personal profile
Dr. Masayuki Nigo holds an M.D. and a Master of Science in Biomedical Informatics. He began his career in medicine after graduating from Fukui University, Japan, in 2005. Dr. Nigo honed his medical skills through a residency in Japan, followed by an internal medicine residency at Beth Israel Medical Center in New York, completed in 2013.
His expertise in infectious diseases was further cultivated at UTHealth McGovern Medical School, where he completed a fellowship in infectious diseases, and an advanced fellowship focusing on Transplant Infectious Diseases. Dr. Nigo's academic career took a significant stride forward at UTHealth McGovern Medical School, where he was appointed as an Assistant Professor in the Division of Infectious Diseases in 2016 and later elevated to Associate Professor in 2022.
In addition to his clinical and teaching roles, Dr. Nigo expanded his academic horizon by obtaining a Master of Science from the McWilliams School of Biomedical Informatics at UTHealth Houston.
In January 2023, Dr. Nigo embarked on a new chapter in his career by joining the Division of Infectious Diseases, Department of Medicine at Houston Methodist. Here, he continues his journey as an Associate Professor of Clinical Medicine, contributing his extensive knowledge and experience to the field of infectious diseases.
Research interests
Dr. Nigo’s primary research interest lies in harnessing the vast potential of electronic health record (EHR) datasets to address critical clinical questions encountered at the patient's bedside. Through advanced coding and data analysis techniques, Dr. Nigo's lab explores a broad spectrum of research queries, employing both local and de-identified EHR datasets.
A key focus of his research is in the realm of precision medicine, applying artificial intelligence—particularly deep-learning models—to optimize antimicrobial therapy. His work aims to tailor treatment for high-risk patient populations, focusing on antimicrobial pharmacokinetics and the prediction of drug-resistant bacteria. This is achieved by integrating a wide range of patient-specific features extracted from electronic health records. Dr. Nigo's cutting-edge research is supported by NIH funding.
The Nigo Lab is a collaborative environment, boasting a diverse team of informaticians, fellows, residents, and students from both medical and bioinformatics disciplines. Dr. Nigo places a high value on mentorship and collaboration, fostering a rich learning atmosphere. This has led to successful projects, culminating in publications in peer-reviewed journals.
External positions
Adjunct Associate Professor, UT Health
Jul 1 2016 → …
Research Area Keywords
- Infectious Disease & Pathology
- Clinical Care
Free-text keywords
- Transplant Infectious Diseases
- Immunocompromised patients
- Drug Resistant Bacteria
- Antimicrobial Pharmacokinetics
- Artificial Intelligence
- Biomedical Informatics
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Collaborations and top research areas from the last five years
Projects
- 2 Active
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A Phase III, adjudicator-blinded, randomised study to evaluate the efficacy and safety of treatment with olorofim versus treatment with AmBisome® followed by standard of care (SOC) in patients with invasive fungal disease (IFD) caused by Aspergillus species
Grimes, K. A., Lin, J., Malik, A. I. & Nigo, M.
11/16/23 → …
Project: Clinical Trial
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PK-RNN-V - Deep-Learning Based Pharmacokinetic Model for Vancomycin
9/21/23 → 7/31/28
Project: Federal Funding Agencies
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Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records
Nigo, M., Rasmy, L., Mao, B., Kannadath, B. S., Xie, Z. & Zhi, D., Dec 2024, In: Nature Communications. 15, 1, 2036.Research output: Contribution to journal › Article › peer-review
Open Access -
Use of Real-World EMR Data to Rapidly Evaluate Treatment Effects of Existing Drugs for Emerging Infectious Diseases: Remdesivir for COVID-19 Treatment as an Example
Zhang, C., Nigo, M., Patel, S., Yu, D., Septimus, E. & Wu, H., 2024, (Accepted/In press) In: Statistics in Biosciences.Research output: Contribution to journal › Article › peer-review
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A Deep-Learning-based Two-Compartment Predictive Model (PKRNN-2CM) for Vancomycin Therapeutic Drug Monitoring
Mao, B., Xie, Z., Rasmy, L., Nigo, M. & Zhi, D., 2023, Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023. Institute of Electrical and Electronics Engineers Inc., p. 484 1 p. (Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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COVID-19 Vaccine Seroresponse Based on The Timing of The Primary Series; Pre- versus Post-Renal Transplantation
Weinberg, A. R., Caeg, C. O., DePalma, R., Hernandez, F., Rogers, J. H., Ibrahim, H. N., Bynon, S. J. & Nigo, M., Nov 2023, In: Clinical Transplantation. 37, 11, p. e15072 e15072.Research output: Contribution to journal › Article › peer-review
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International Multicenter Study Comparing COVID-19 in Patients With Cancer to Patients Without Cancer: Impact of Risk Factors and Treatment Modalities on Survivorship
Data-Driven Determinants for COVID-19 Oncology Discovery Effort (D3CODE) Team, Jan 30 2023, In: eLife. 12, e81127.Research output: Contribution to journal › Article › peer-review
Open Access2 Scopus citations