pattern mining
TRANSCRIPT
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Pattern Mining for Critical Care
Shameek Ghosh
Supervised By
Associate Prof. Jinyan Li
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Outline
• Healthcare Analytics
• Details of the MIMIC II Database
• Some basic statistics
• The Research Problem
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Healthcare Analytics
• Mining of Raw EHR can help develop a clinical model of the patient’s history
• Aids in the diagnostic process by the hospital and physician
• Complex healthcare systems generating massive data
• Dearth of tools that can quantitatively support fast analysis of complex, high-frequency unstructured medical data streams
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MIMIC II Research Database
Patient Demographics Medications Lab Results
Nursing Notes
ECG Blood Pressure
Heart Rate Vital Physiological Signals
MIMIC II CLINICAL DATABASE MIMIC II WAVEFORM
DATABASE
PHYSIONET
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Describing an ICU patient
• Any patient (unique patient id?) who was admitted to the ICU on more than one occasion ( multiple icu stay ids in the same visit?) may be represented by multiple patient visits (unique hospital admission ids?)
• The adult ICUs (for patients aged 15 years and over) include medical (MICU), surgical (SICU), coronary (CCU), and cardiac surgery (CSRU) care units (unique care unit ids?)
• For neonates, the neonatal ICU (NICU) data was also collected
Note: Patient may have been admitted several times during the 10 year period in which MIMIC data was collected
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How to identify a patient?
Subject_ID Hadm_ID
icu_stay_ID
Case_ID
Waveforms
Can have many
Can have many
Can have many Will have a set of
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Patient timeline
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Tables of Importance
• The basic information for any given patient is stored in the table D_PATIENTS
• Subject_ID is widely used by throughout MIMIC II to specify to which patient a given measurement or recording refers to
• D_PATIENTS is associated by the subject_id to ICD9 ( codes for r International Classification of Diseases) and DRGEVENTS ( patient specific diagnosis events)
• The ICD-9 table records the ICD-9 codes applied to a particular patient during a specific hospitalization period.
• Diagnosis-related groups (DRGs) are stored in the DRGEVENTS table and their meanings are stored in D_CODEDITEMS.
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Tables of Importance
• Caregivers are stored in the D_CAREGIVER table (cgid). Caregivers related to many other tables such as medevents, noteevents and chartevents and is used to record the care giver who performed a particular operation, procedure or event
• The Careunits table D_CAREUNITS (cuid), stores information pertaining to the different ICU rooms in the hospital and whenever a problem occurs or a chart event is entered, the particular care unit is also recorded
• Medication(s) given to a patient are recorded in the medevents, d_meditems, a_meddurations and additives tables
• Patient medical chart data is recorded in the chartevents, d_chartitems, a_chartdurations and formevents tables
• Patient input/output (IO) data is recorded in the ioevents, d_ioitems, a_iodurations, deliveries, totalbalevents and additives tables
• Patient notes are recorded in the noteevents table
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Some Basic Statistics from MIMIC
• Total number MIMIC specific relations: 38
• Total No. of patients: 32535
• Patients with Cardiac Arrests: 640
• Patients with Acute Kidney Injury: 3396
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Blood Pressure Prediction
Waldin, A et al (2013). In the proceedings of the 30th International Conference on Machine Learning. JMLR W&CP volume 28.
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Importance of BP prediction in ICU • BP is related to hypotension
• Hypotension is related to Acute Kidney Injury (AKI)
• AKI can arise in an ICU for cardiopulmonary bypass, major trauma, mechanical ventilation, burn injuries, sepsis and in many other diseases
• AKI results in mortality (loss of life) and morbidity (increased hospital stay causing chronic conditions)
• Need to investigate hemodynamic (blood flow) perturbations to understand the incidence of AKI
• Discriminative sequential patterns could be explored in arterial blood pressure that might help in predicting stages of AKI
• Predicting Acute Hypotensive Episodes (Physionet Challenge 2009)
• Predicting an ICU patient's future blood pressure from a recording of his recent blood pressure history (Waldin, 2013)
Dennen, P., et al. (2010). Critical care medicine, 38(1), 261-275. Lehman, L. W., et al (2010). Computing in Cardiology, pp. 1095-1098). IEEE.
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Acute Hypotensive Episodes (AHE)
• An acute hypotensive episode requires effective and prompt intervention in an ICU
• 41% experienced recorded episodes of acute hypotension during their ICU stays
• AHE - any period of 30 minutes or more when 90% of MAP readings were at or below 60mmHg
• If one might forecast acute hypotensive episodes in the ICU, there is a possibility of improving care and survival of patients at risk of these events
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Problem Description
• Given a continuos mean arterial signal from time - 1…t, our goal is to predict the value of a statistic defined for the sample over a time period (t+k) to (t+k+a), where k is the lead time and a is the prediction window
• Additional issues that can be handled by algorithm: 1) Prediction window size 2) Definition of prediction statistic (as reported by Waldin, 2013)
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Conclusion
• Determination of interesting sequences of events indicative of future critical conditions in ICU patients is important
• Can help in understanding variations in physiological patterns
• Clustering treatment plans based on similar patterns in ICU patients
• In the mining of abnormal events
• Evolving set of events provides an excellent interpretive patient knowledge to a physician