Mark Lachmann
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- Cardiac Valve Diseases and Treatments
- Cardiovascular Function and Risk Factors
Papers in
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- Cardiac Valve Diseases and Treatments 19
- Cardiovascular Function and Risk Factors 4
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- Aortic Disease and Treatment Approaches 2
- Pulmonary Hypertension Research and Treatments 2
- Coronary Artery Anomalies 1
- Co-authors
- Shinsuke Yuasa (6 shared papers)Karl‐Ludwig Laugwitz (3 shared papers)Keiichi Fukuda (1 shared paper)Mai Kimura (1 shared paper)Toshiomi Katsuki (1 shared paper)Yoshikazu Kishino (1 shared paper)Tomohisa Seki (1 shared paper)Dai Kusumoto (1 shared paper)
In The Last Decade
Mark Lachmann
29 papers receiving 465 citations
Peers
Comparison fields: 5 of 88
- Health Informatics 24
- Cardiology and Cardiovascular Medicine 185
- Biophysics 34
- Aging 4
- Critical Care and Intensive Care Medicine 10
Countries citing papers authored by Mark Lachmann
This map shows the geographic impact of Mark Lachmann's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark Lachmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Lachmann more than expected).
Fields of papers citing papers by Mark Lachmann
This network shows the impact of papers produced by Mark Lachmann. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark Lachmann. The network helps show where Mark Lachmann may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark Lachmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 79 | |
| 2 | 2021 | 40 | |
| 3 | 2024 | 34 | |
| 4 | 2002 | 33 | |
| 5 | 2022 | 33 | |
| 6 | 2023 | 33 | |
| 7 | 2017 | 29 | |
| 8 | 2022 | 22 | |
| 9 | 2022 | 21 | |
| 10 | 2021 | 20 | |
| 11 | 2023 | 20 | |
| 12 | 2023 | 17 | |
| 13 | 2022 | 14 | |
| 14 | 2023 | 13 | |
| 15 | 2021 | 9 | |
| 16 | 2022 | 9 | |
| 17 | 2025 | 9 | |
| 18 | 2015 | 8 | |
| 19 | 2023 | 7 | |
| 20 | 2023 | 6 |
About Mark Lachmann
Mark Lachmann is a scholar working on Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine, Epidemiology, Health Informatics and Geriatrics and Gerontology, having authored 37 papers that have together received 479 indexed citations. Recurring topics across this work include Cardiac Valve Diseases and Treatments (19 papers), Cardiovascular Function and Risk Factors (4 papers), Single-cell and spatial transcriptomics (2 papers), Infective Endocarditis Diagnosis and Management (2 papers), Aortic Disease and Treatment Approaches (2 papers), Pulmonary Hypertension Research and Treatments (2 papers), Coronary Artery Anomalies (1 paper) and Digital Imaging for Blood Diseases (1 paper). The work is most often cited by research in Health Informatics (24 citations), Cardiology and Cardiovascular Medicine (185 citations), Biophysics (34 citations), Aging (4 citations) and Critical Care and Intensive Care Medicine (10 citations). Mark Lachmann has collaborated with scholars based in Germany, Japan and Canada. Frequent co-authors include Shinsuke Yuasa, Karl‐Ludwig Laugwitz, Keiichi Fukuda, Mai Kimura, Toshiomi Katsuki, Yoshikazu Kishino, Tomohisa Seki, Dai Kusumoto, Jef Van den Eynde and Shelby Kutty. Their work appears in journals such as JACC: Cardiovascular Interventions, European Heart Journal, European Heart Journal - Cardiovascular Imaging, Nature Communications and Stem Cell Reports.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.