Pattern Mining for Critical Care

Shameek Ghosh

Shameek.Ghosh@student.uts.edu.au

Supervised By

Associate Prof. Jinyan Li

Outline

• Healthcare Analytics

• Details of the MIMIC II Database

• Some basic statistics

• The Research Problem

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

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

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

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

Patient timeline

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.

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

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

Blood Pressure Prediction

Waldin, A et al (2013). In the proceedings of the 30th International Conference on Machine Learning. JMLR W&CP volume 28.

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.

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

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)

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