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  1. Article ; Online: Finding a new balance between a genetics-first or phenotype-first approach to the study of disease.

    Kohane, Isaac S

    Neuron

    2021  Volume 109, Issue 14, Page(s) 2216–2219

    Abstract: Successes in neuroscience using a genetics-first approach to characterizing disorders such as autism have eclipsed the scientific and clinical value of a comprehensive phenotype-first-clinical or molecular-approach. Recent high-throughput phenotyping ... ...

    Abstract Successes in neuroscience using a genetics-first approach to characterizing disorders such as autism have eclipsed the scientific and clinical value of a comprehensive phenotype-first-clinical or molecular-approach. Recent high-throughput phenotyping techniques using machine learning, electronic medical records, and even administrative databases show the value of a synthesis between the two approaches.
    MeSH term(s) Autistic Disorder/diagnosis ; Autistic Disorder/genetics ; Electronic Health Records ; Genotype ; Humans ; Phenotype
    Language English
    Publishing date 2021-07-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 808167-0
    ISSN 1097-4199 ; 0896-6273
    ISSN (online) 1097-4199
    ISSN 0896-6273
    DOI 10.1016/j.neuron.2021.07.001
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  2. Book: Biomedical informatics: one discipline

    Kohane, Isaac S.

    November 9 - 13, 2002, San Antonio, TX

    (Proceedings / AMIA ; 2002 ; [JAMIA ; 9, Suppl.])

    2002  

    Title variant Bio*medical
    Author's details ed. by Isaac S. Kohane
    Series title Proceedings / AMIA ; 2002
    [JAMIA ; 9, Suppl.]
    JAMIA
    Proceedings
    Collection JAMIA
    Proceedings
    Language English
    Size XL, 1258 S. : Ill., graph. Darst.
    Publisher Hanley & Belfus
    Publishing place Philadelphia
    Publishing country United States
    Document type Book
    HBZ-ID HT013542860
    ISBN 1-56053-600-4 ; 978-1-56053-600-0
    Database Catalogue ZB MED Medicine, Health

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    Zs A 4128 -[9,Suppl.]- not available for loan Zeitschriften-Lesesaal

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  3. Article ; Online: Unsupervised Anomaly Detection to Characterize Heterogeneity in Type 2 Diabetes.

    Argaw, Peniel N / Kushner, Jake A / Kohane, Isaac S

    AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

    2023  Volume 2023, Page(s) 32–41

    Abstract: Diabetes is associated with heterogeneous behaviors affecting patients' clinical characteristics and trajectories. This study includes 21,288 patients with type 2 diabetes (women, ages 30 to 65). The cohort was filtered through a set of preprocessing ... ...

    Abstract Diabetes is associated with heterogeneous behaviors affecting patients' clinical characteristics and trajectories. This study includes 21,288 patients with type 2 diabetes (women, ages 30 to 65). The cohort was filtered through a set of preprocessing heuristics in order to assure the cohort exhibited a similar clinical trajectory. Anomalous characteristics were then identified using dimensionality reduction and anomaly detection methods. Compared to the majority of the cohort, patients classified as anomalous were twice as likely to be admitted into the hospital (7.94[7.59 8.28] versus 3.12[3.06 3.17] times), have a higher incidence of comorbidities (2[1.64 2.36] times more), and be prescribed more insulin and less new and more expensive diabetes medications (such as Sodium glucose co-transporter 2 inhibitors). Patients with these anomalous characteristics may benefit from additional or specialized interventions to avert their risk for adverse outcomes.
    Language English
    Publishing date 2023-06-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2676378-3
    ISSN 2153-4063 ; 2153-4063
    ISSN (online) 2153-4063
    ISSN 2153-4063
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  4. Book ; Conference proceedings: Interpreting clinical data

    Kohane, Isaac S.

    [a spring 1994 symposium in Palo Alto, Calif.]

    (Artificial intelligence in medicine ; 7,6 : Special issue)

    1995  

    Author's details guest ed.: I. S. Kohane
    Series title Artificial intelligence in medicine ; 7,6 : Special issue
    Keywords Decision Making, Computer-Assisted / congresses ; Expert Systems / congresses
    Language English
    Size S. 471 - 582 : graph. Darst.
    Publisher Elsevier
    Publishing place Amsterdam u.a.
    Publishing country Netherlands
    Document type Book ; Conference proceedings
    HBZ-ID HT007197992
    Database Catalogue ZB MED Medicine, Health

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  5. Article ; Online: Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

    Amiri, Hadi / Kohane, Isaac S

    JAMA network open

    2021  Volume 4, Issue 2, Page(s) e2036220

    Abstract: Importance: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators to ... ...

    Abstract Importance: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases.
    Objective: To develop computational models that effectively predict admission outcomes for applicants seeking UDN evaluation and to rank the applications based on the likelihood of patient admission to the UDN.
    Design, setting, and participants: This prognostic study included all applications submitted to the UDN from July 2014 to June 2019, with 1209 applications accepted and 1212 applications not accepted. The main inclusion criterion was an undiagnosed condition despite thorough evaluation by a health care professional; the main exclusion criteria were a diagnosis that explained the objective findings or a review of the records that suggested a diagnosis. A classifier was trained using information extracted from application forms, referral letters from health care professionals, and semantic similarity between referral letters and textual description of known mendelian disorders. The admission labels were provided by the case review committee of the UDN. In addition to retrospective analysis, the classifier was prospectively tested on another 288 applications that were not evaluated at the time of classifier development.
    Main outcomes and measures: The primary outcomes were whether a patient was accepted or not accepted to the UDN and application order ranked based on likelihood of admission. The performance of the classifier was assessed by comparing its predictions against the UDN admission outcomes and by measuring improvement in the mean processing time for accepted applications.
    Results: The best classifier obtained sensitivity of 0.843, specificity of 0.738, and area under the receiver operating characteristic curve of 0.844 for predicting admission outcomes among 1212 accepted and 1210 not accepted applications. In addition, the classifier can decrease the current mean (SD) UDN processing time for accepted applications from 3.29 (3.17) months to 1.05 (3.82) months (68% improvement) by ordering applications based on their likelihood of acceptance.
    Conclusions and relevance: A classification system was developed that may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Accelerating the admission process may improve the diagnostic journeys for these patients and serve as a model for partial automation of triaging or referral for other resource-constrained applications. Such classification models make explicit some of the considerations that currently inform the use of whole-genome sequencing for undiagnosed disease and thereby invite a broader discussion in the clinical genetics community.
    MeSH term(s) Adolescent ; Adult ; Area Under Curve ; Child ; Child, Preschool ; Computer Simulation ; Female ; Humans ; Infant ; Infant, Newborn ; Machine Learning ; Male ; Middle Aged ; Patient Admission ; Patient Selection ; Prospective Studies ; ROC Curve ; Rare Diseases/diagnosis ; Rare Diseases/genetics ; Referral and Consultation ; Reproducibility of Results ; Retrospective Studies ; Triage ; Undiagnosed Diseases/diagnosis ; Undiagnosed Diseases/genetics ; Whole Genome Sequencing ; Young Adult
    Language English
    Publishing date 2021-02-01
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2574-3805
    ISSN (online) 2574-3805
    DOI 10.1001/jamanetworkopen.2020.36220
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  6. Article ; Online: Data Citizenship under the 21st Century Cures Act.

    Mandl, Kenneth D / Kohane, Isaac S

    The New England journal of medicine

    2020  Volume 382, Issue 19, Page(s) 1781–1783

    MeSH term(s) Access to Information/legislation & jurisprudence ; American Recovery and Reinvestment Act ; Computer Security ; Electronic Health Records/legislation & jurisprudence ; Health Information Exchange/legislation & jurisprudence ; Health Information Interoperability/legislation & jurisprudence ; Health Information Systems/legislation & jurisprudence ; Health Insurance Portability and Accountability Act ; Humans ; Medical Records Systems, Computerized/standards ; United States
    Language English
    Publishing date 2020-03-11
    Publishing country United States
    Document type Journal Article
    ZDB-ID 207154-x
    ISSN 1533-4406 ; 0028-4793
    ISSN (online) 1533-4406
    ISSN 0028-4793
    DOI 10.1056/NEJMp1917640
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  7. Article ; Online: Association of Race and Socioeconomic Disadvantage With Missed Telemedicine Visits for Pediatric Patients During the COVID-19 Pandemic.

    Brociner, Evan / Yu, Kun-Hsing / Kohane, Isaac S / Crowley, McGreggor

    JAMA pediatrics

    2022  Volume 176, Issue 9, Page(s) 933–935

    MeSH term(s) COVID-19 ; Child ; Humans ; Pandemics ; SARS-CoV-2 ; Socioeconomic Factors ; Telemedicine
    Language English
    Publishing date 2022-05-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2701223-2
    ISSN 2168-6211 ; 2168-6203
    ISSN (online) 2168-6211
    ISSN 2168-6203
    DOI 10.1001/jamapediatrics.2022.1510
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  8. Article ; Online: HEALTH CARE POLICY. Ten things we have to do to achieve precision medicine.

    Kohane, Isaac S

    Science (New York, N.Y.)

    2015  Volume 349, Issue 6243, Page(s) 37–38

    MeSH term(s) Evidence-Based Medicine ; Genomics/trends ; Health Policy ; Humans ; Medical Record Linkage ; Reproducibility of Results ; United States
    Language English
    Publishing date 2015-07-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.aab1328
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  9. Article ; Online: Simulation of undiagnosed patients with novel genetic conditions.

    Alsentzer, Emily / Finlayson, Samuel G / Li, Michelle M / Kobren, Shilpa N / Kohane, Isaac S

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 6403

    Abstract: Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited ...

    Abstract Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.
    MeSH term(s) Humans ; Computer Simulation ; Phenotype ; Rare Diseases/diagnosis ; Rare Diseases/genetics ; Patients
    Language English
    Publishing date 2023-10-12
    Publishing country England
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-41980-6
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  10. Article ; Online: The digital-physical divide for pathology research.

    Kohane, Isaac S / Churchill, Susanne / Tan, Amelia Li Min / Vella, Margaret / Perry, Cassandra L

    The Lancet. Digital health

    2023  Volume 5, Issue 12, Page(s) e859–e861

    MeSH term(s) Digital Divide ; Telemedicine
    Language English
    Publishing date 2023-11-24
    Publishing country England
    Document type Journal Article
    ISSN 2589-7500
    ISSN (online) 2589-7500
    DOI 10.1016/S2589-7500(23)00184-X
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