New Book! Coming out late 2014 / early 2015

Implementing Medical Device Connectivity with Electronic Health Records and Data Warehouses: A Practicum for Hospital Enterprise Clinical and Information Technology Implementation Teams. ... Read More

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Dictionary of Computer Science, Engineering, and Technology

Zaleski, JR, (contributing Author), Dictionary of Computer Science, Engineering, and Technology, (CRC Press, Phil Laplante, Editor-in-Chief). ... Read More

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Integrating Device Data into the Electronic Medical Record

The seminal book on the interoperability of medical device data and health information technology systems. John Zaleski, Ph.D. A book on empirical practice of medical device interoperability, based on years of experience in the field. A Developer’s Guide to Des... Read More

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Medical Device Data and Modeling for Clinical Decision Making

This work combines much of the experience learned in medical device interoperability and clinical informatics I have gained over the course of the past 20+ years. I have leveraged work from my  PhD and experience in product management of critical care. The devic... Read More

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Weaning from Postoperative Mechanical Ventilation

Modeling Post-Operative Respiratory State in Coronary Artery Bypass Graft Patients: A Method for Weaning Patients from Mechanical Ventilation. This PhD research developed a model for real-time assessment of patient postoperative recovery and viability for weaning f... Read More

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Latest happenings from the blog

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Informs 2014 Joint Session on Analytics / Early Warning Notifications

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This past week, I hosted a session at the 2014 INFORMS Annual Conference in San Francisco. The title of the session was Clinical Analytics, Informatics and Clinical Decision Making.

The I also gave a presentation titled Early Warning Notifications Using Medical Device Data.

The purpose of this paper was to introduce types of early warning notifications and analytics that can be employed using medical device data retrieved from the bedside medical devices for use in clinical decision making. A copy of the presentation is available via the hyperlink above.

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Signal artifact smoothing using the Extended Kalman Filter in real-time arterial blood pressure measurements

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Arterial blood pressure (ABP) measurements and Extended Kalman Filter (EKF) track.

Arterial blood pressure (ABP) measurements and Extended Kalman Filter (EKF) track.

In an earlier post, I had explored the use of signal filtering on analyzing alarms produced by medical devices. In this post, I begin to explore the use of the Extended Kalman Filter (EKF) in smoothing of signal artifact from real-time wave forms. In an effort to save space, a PDF of the high-level analysis is provided here: “Filtering of Arterial Blood Pressure Signal Artifact Using the Extended Kalman Filter“. The algorithm and mathematical methodology I described in some detail in the earlier blog post. In this example, I use data from the MIMIC II waveform database to demonstrate the use of a simple tracking algorithm to assist in the smoothing of signal artifact. The key point of this analysis is to experiment with tuning of EKF filter parameters to minimize the effect of artifact while maximizing the adherence of the track to the true signal (and, thereby, ensure signal integrity).

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Tracking Blood Glucose, Part 1

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Diabetes diagnosis is typically associated with the following [1, 2]:

  • Two or more consecutive fasting glucose levels ≥ 126 mg/dL;
  • Any single ad-hoc measurement of blood glucose > 200 mg/dL;
  • A1c test result ≥ 6.5%; or,
  • A 2-hour oral glucose measurement value > 200 mg/dL.

Normal blood glucose levels during fasting (e.g.: usually morning levels, for which fasting has been undertaken for 8 hours nominally) should range between 70 and 99 mg/dL. Normal post-meal blood glucose (2 hours or so after eating) < 140 mg/dL. An important part of diabetes management is measurement: the old adage, you cannot control what you cannot measure, applies. Hence, in the process of managing one’s diabetes (through diet, exercise, insulin and oral medications), measurement of the blood glucose is key to determining, empirically, whether behaviors and diet are working (i.e., “good” behaviors) or a not working (i.e., “bad” behaviors). When it comes to diabetes management, the patient is the only one who can control it. Physicians, dieticians, and other members of the care team can facilitate, prescribe, assist and guide. But, ultimately, it is only the patient—the individual diagnosed with the ailment—who has absolute control over the effects of diabetes [3]. As a diabetic myself, I know that I am in control of my diet; I am in control of my weight & exercise regimen; and, I am in control of my medications—adhering to the regimens that work is completely in my control, and is my responsibility. The linked white paper provides some empirical assessment of glucose measurement in a single patient over a year, to illustrate the types of trending and potential benefits of following glucose closely for the diabetic patient.

[1] “What are normal blood glucose levels?” https://www.virginiamason.org/WhatareNormalBloodGlucoseLevels

[2] “How the Blood Sugar of Diabetes Affects the Body.” http://www.webmd.com/diabetes/how-sugar-affects-diabetes

[3] R. K. Bernstein, MD. “Dr. Bernstein’s Diabetes Solution: A Complete Guide to Achieving Normal Blood Sugars.” Little, Brown & Co. 1997. Page 3.

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