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Nosocomial Risk Datasets from MIMIC-III
Published: Sept. 15, 2022. Version: 1.0
Travis R Goodwin, Dina Demner-Fushman, A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision, Journal of the American Medical Informatics Association, Volume 27, Issue 4, April 2020, Pages 567–576, https://doi.org/10.1093/jamia/ocaa004
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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
Reliable longitudinal risk prediction for hospitalized patients is needed to provide quality care. Our goal is to foster the development of generalizable models capable of leveraging clinical notes to predict healthcare-associated diseases 24–96 hours in advance. We developed data to explore the problem of predicting the risk of hospital acquired (occurring 48 hours after admission) acute kidney injury, pressure injury, or anemia 24 hours before it is implicated by the patient’s chart, labs, or notes. We relied on the MIMIC-III critical care database and extract distinct positive and negative cohorts for each disease. We retrospectively determine the date-of-event using structured and unstructured criteria so that it may be used as a form of indirect supervision to train and evaluate automatic systems for predicting disease risk using clinical notes. This data was used as the experimental basis for the CANTRIP project.
Risk prediction from EHR data has received considerable attention over the last decade, with the majority of approaches predicting a specific outcome or single disease; however a recent review of 107 risk prediction studies observed that most studies (a) relied on only a small list of predefined variables rather than leveraging the breadth of data in the EHR, and (b) neglected to consider longitudinal relationships in the data. Moreover, very few studies involved clinical text in any capacity. We developed data for three common nosocomial diseases: hospital acquired acute kidney injury (HAAKI), hospital acquired pressure injury (HAPI), and hospital acquired anemia (HAA) -- each with its own training, validation, and testing cohorts.
Hospital acquired acute kidney injury (HAAKI)
Acute kidney injury (AKI) affects as many as 20% of all hospitalizations and is associated with increased mortality, end-stage renal disease, and chronic kidney disease[5,6]. Unfortunately, current criteria for AKI are primarily markers of established kidney damage or impaired function. As such, new approaches for earlier prediction of AKI before significant kidney damage is established could improve outcomes. This dataset is the first to our knowledge designed for predicting AKI or HAAKI using clinical notes.
Hospital acquired anemia (HAA)
A substantial number of hospital patients with normal HgB on admission become anemic during the course of their hospitalization resulting in increased length of stay, hospital charges, and substantial risk of in-hospital mortality (by 51%–228%, depending on severity). It has been shown that, in critical care, phlebotomy is highly associated with changes in HgB and hematocrit; moreover, critical care patients average 40–70 mL of blood drawn daily and every 50 mL of blood drawn increases their risk of moderate to severe HAA by 18%[8,9]. Consequently, the ability to automatically predict HAA would enable physicians to switch to small volume phlebotomy tubes, minimizing blood loss from in-dwelling catheters, and reducing blood tests. Although there has been some work on predicting anemias such as classifying iron deficiency anemia using artificial neural networks, or predicting moderate to severe anemia for patients with ulcerative colitis using logistic regression, we were unable to find any datasets designed for developing automatic methods for predicting hospital acquired anemia whether using structured or unstructured data.
Hospital acquired pressure injury (HAPI)
The development of pressure injuries (i.e., pressure ulcers or bed sores) can lead to several complications, including sepsis, cellulitis, osteomyelitis, pain, depression, and increased mortality (as high as 60% within 1 year of hospital discharge for older patients who develop a pressure ulcer during their stay)[12,13]. There have been no conclusive studies on the identification of pressure ulcer risk factors, nor were any of the existing risk-assessment scales developed especially for use in intensive care unit (ICU) patients. With this dataset, we enable data-driven approaches to reliably detect pressure ulcer for ICU patients without physician interaction or pre-specified feature extraction, allowing for potentially improved patient outcomes.
The data in this project is derived from MIMIC-III[1,2]. To account for irregular gaps in the patient’s hospital visit, we adopt an abstract representation of the patient’s hospital visit which we call their clinical chronology. We represent the chronology as
a discrete, discontiguous sequence of snapshots, , where each snapshot encodes the clinical observations documented in any clinical notes produced on the same (calendar) day, and
a sequence of elapsed times, such that encodes the number of hours between and and encodes the number of hours between hospital admission and the first clinical note.
Natural language preprocessing
We extracted the set of observations from each clinical note in MIMIC-III using MetaMap Lite. We then filtered out all observations that:
- were not affirmed, certain, present, and associated with the patient;
- occurred in a section corresponding to consults, family history, past medical history, or social history;
- had a UMLS semantic type not corresponding to a medical problem, intervention, drug, or anatomic region; or
- belonged to InfoBot’s medical stop word list.
Further details are provided in .
Determining the Date-of-Event
We determined the Date-of-Event as the first date in which the disease is documented in a clinical note, or evidenced by the patient’s labs or chart. Specifically, for each disease, we defined 1 or more (a) seed concepts in the UMLS hierarchy, (b) lexical patterns, and (c) structured criteria using the laboratory, chart, and/or vital sign information in MIMIC. We determined the DOE as the first date in which (1) any observation extracted from a clinical note associated with that date descends from any of the UMLS seed concepts; (2) any observation or any text in the note contains any of the lexical patterns not immediately followed by a colon (to rule out structural matches, eg, “bed sore: none”); or (3) the structured criteria is met.
|Disease||UMLS Seed CUI||Lexical patterns (regular expressions)||Structured criteria|
|HAAKI||C0022660 (Kidney Failure, Acute)||kidney failure, renal failure, kidney injury, renal injury, AKI||KDIGO|
|HAPI||C0011127 (Pressure Ulcer)||bed sore, bed ulcer, pressure sore, pressure ulcer, decub* sore, decub* ulcer||NPUAP|
|HAA||C0002871 (Anemia)||anemia, anaemia, HAA||WHO|
Abbreviations: AKI, acute kidney injury; CUI, concept unique identifier; HAA, hospital acquired anemia; HAAKI, hospital acquired acute kidney injury; HAPI, hospital acquired pressure injury; KDIGO, Kidney Disease Improving Global Outcomes; NPUAP, National Pressure Uncler Advisory Panel; UMLS, Unified Medical Language System; WHO, World Health Organization.
Encoding elapsed times
We encoded elapsed times using the sinusoidal representation proposed in  and further detailed in .
Creating positive and negative examples
To enable a model to be trained without manually quantifying the risk of disease for each snapshot in each patient’s chronology, we used the DOE as a form of indirect supervision to produce positive and negative examples. Specifically, for each positive admission (i.e., admissions with chronologies in which the patient eventually develops the disease) we created a labeled example by:
Truncating each chronology to end at the last snapshot occurring 24–96 hours before the DOE;
Defining the prediction window as the elapsed time (in hours) between the final snapshot (after truncation) and the DOE; and
Assigning the label .
To create negative examples, we first grouped positive admissions into buckets based on demographic and admission information including the patient’s age, sex, and race as well as their admitting ICU, source of admission (i.e., clinic, physician, transfer, or other), type of admission (i.e., elective, emergency, urgent), Oxford Acute Severity of Illness Score and type of insurance (i.e., government, private, Medicaid, Medicare, or self pay). For each bucket , we assumed the Time-to-Event (TTE, i.e., the number of hours elapsed from hospital admission to DOE) followed a Gamma prior distribution (i.e., TTE ) and determined and using maximum likelihood estimates over each positive example in the bucket. This allowed us to create labels for our negative examples by:
Determining which bucket each negative example belonged to;
Sampling TTE′ ;
Defining the DOE as either (a) the date obtained by projecting TTE' from the date of hospital admission or (b) the discharge date, whichever occurred first;
Truncating the chronology to end at the snapshot 24–96 hours before the DOE; and
Defining as the hours elapsed between the final snapshot (after truncation) and the DOE.
This process is illustrated and further detailed in .
The chronology CSV files have the following format:
[observations] is encoded as a space separated list of observation IDs (e.g., UMLS CUIs), and
[timestamp] is the chart time for that set of observations.
The admission CSV files follow the format:
[timestamp] is the admission time for the associated hospital admission
The label CSV files have the following format:
[timestamp] is the chart time of the label and
[label] is a zero or one indicating the date-of-event for the disease
This data can be used to train models for predicting nosocomial disease risk 48-96 hours in advance. An example toolkit for this is provided through CANTRIP on GitHub[3,22]. If used, please cite the associated manuscript.
Initial release version 1.0
This project relies exclusively on de-identified data from MIMIC-3 and was approved by the NIH IRB.
This data was developed utilizing the computational resources of the NIH HPC Biowulf cluster and was produced for the paper A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision published in the Journal of the American Medical Informatics Association.
Conflicts of Interest
The authors have no conflicts of interest to disclose.
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- U.S. Department of Health and Human Services. (n.d.). NIH HPC Systems. National Institutes of Health. Retrieved August 12, 2022, from https://hpc.nih.gov/.
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