Introduction
I am developing this line of reasoning for a course I plan to take: Information Computing and the Future.
Computational Medicine is an area that is socially, politically and technically fascinating.
It is socially fascinating because, with the advent of successful machine learning, it holds the promise to democratize access to medical care.
It is politically fascinating because there are entrenched interests making large amounts of money from the status quo. These interests include health insurance companies, hospitals and care providers. The term "care providers" is a catch all for doctors, generalists and specialists, nurses, RN's and LPN's and support staff such as radiation technicians, respiratory therapists, physical therapists, lab technicians and so forth.
It is technically fascinating because everyone is in need of competent health care and to the extent that some portions of diagnosis and treatment can be automated, more people can receive timely and effective treatment.
I will focus on the computational aspects since they hold much promise for progress and are more tractable than the social and political areas.
Computational Medicine in the Small
The turn of the millennium produced the first nearly complete sequence of the human genome, which is computationally a base-four digital Turing Tape whose length is two instances of a 3.234 Giga base pairs, one from each parent. These codes are replicated 37 trillion times in the 250+ cell types of the body. There has been substantial recent progress in gene sequencing, but a gap remains between the code of the genes (genotype), and the invisible circuits that regulate gene expression (phenotype).
Understanding the underlying genetic components of disease will continue to be great step forward in guiding accurate diagnosis and treatment.
Computational Medicine in the Large
Machine learning has been applied with great effect to cancer detection at the macroscopic level of breast cancer radiology inspection and skin cancer (melanoma) screening. For a season a schism emerged between those who are domain experts in the biological sciences and those who are so trained in the computational sciences. As Machine Learning continues to outpace expensive cancer specialists, there may be a "circling of the wagons" by those who have held an economic monopoly in this diagnostic area. They can become conflicted in their duty to heal the patient and amass large profits for themselves and their institutions.Computational Medicine in the Huge
We can zoom our computational lens out to include populations of people, taking an "epidemiological" point of view to the national or worldwide level.
On January 2, 2019, a list of the drugs approved by the FDA in 2018 was released. Some of these drugs are "orphan drugs", that is drugs that treat conditions that are relatively rare in the world population. There is less economic incentive to manufacture and research these drugs than those for more common conditions such as cancer, HIV, cystic fibrosis, malaria and river-blindness. However the emergent theme in most of these new drugs is their astronomical pricing, making them unavailable to those, who in many cases, most need them. Here are just 3 out of 59 entries from the list above, two of which cost more than $250,000 wholesale per year:
One of the drugs in this list - Galafold, for fatty buildup disease - costs $315,000 per year, yet could be synthesized by someone with high school lab skill and a chemistry set!
This pharmacological injustice can lead to the social bifurcation of "haves and have-nots" - preconditions that fulment unrest, conflict and sometimes all out war.
But here I want to focus on a pattern of computational interaction that has a more positive end, and that could ultimately democratize access to diagnosis and treatment - all facilitated by information processing in the future.
Daisy Wheel Diagnostics™
Preamble
What follows below is more autobiographical than I want or like. I am attempting to reconstruct a line of thinking, a chain of reasoning that led to my current perspective, and enlightens what will come next. Apologies for the first-person perspective.
Introduction
When a family member of mine developed cancer I felt that it was important to understand this complex disease from a comprehensive point of view. Typically we are conditioned to look at "single-factor" causes of diseases that are in fact multifactor in nature. The first thing I did was to start trying to draw cause and effect graphs between the genes that are implicated in cancer, since visualization of that which cannot be seen has been a source of breakthroughs in clinical medicine, both with radiology, the microscope and clinical lab spectroscopy.
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| Some Connections to the Her2Neu Receptor Gene |
After that I took genetics, biochemistry and molecular biology and wrote a summary treatise on the various factors that enable cancer to develop and treatment approaches.
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| Four Categories of Carcinogenesis |
In broad strokes, here is a pattern of inquiry that I have developed over time, out of habit, first from seven years working in clinical laboratories, and later with five family members who have had cancer, or died of it, and two who have had mental illness. I have drawn the blood and taken EKG's on thousands of people. I have run hematology, clinical chemistry, and bacteriology tests on these same people and produced reports that were provided to attending physicians that determined treatment. I have attended appointments with surgical oncologists, radiation oncologists, and hematological oncologists (the mnemonic in cancer is "slash-burn-poison" for radiotherapy, chemotherapy and surgery respectively). That is the cancer front.
Mental illness is more of a black box with respect to the clinical laboratory because we do not as of yet have a way of sampling the concentration of intrasynaptic neurotransmitter levels along the neural tracts in the living brain. Nonetheless behavior and thought-patterns themselves can be diagnostic, which creates huge opportunities for machine learning diagnostics.
To challenge the black-box definition: Let me conclude this introduction with an observation, useful in the definition and treatment of mental illness:
"If a patient is successfully treated with a certain drug known to be specific for their symptoms, then in all likelihood, they have the disease associated with that set of symptoms." This is not circular, it is rather "substance-oriented".The constellation of drugs administered to a successfully treated patient, constitute a definition of their specific condition. The inference being made is that the successful suite of substances that restores neurotransmitter concentrations and brain chemistry to normal levels serves as a label for the condition from which the person suffers.
In computational terms, these treatments constitute an alternate name for their condition, to wit, "The condition successfully treated by XYZ in amounts xyz." So the patient has the condition XYZxyz. This makes sense if we consider the underlying biochemical nature of mental illness at the small molecule level as being that of chords of specific receptors being up and down regulated in certain patterns at certain times. There are larger aggregations of neural tract organization that are obviously also important, but my sense is that these are more significant in aphasia and specific disabilities that are separately discerned from conditions such as bipolar disorder, obsessive-compulsive disorder, schizophrenia and so forth. End of introduction.
Patterns of Human vs. Computational Inquiry
Over time I have developed some habits of inquiry, due to my mother, a medical technologist and my dad, a software engineer, who taught me how to assess whether a given care provider "was good". Answering the question, "but are they good" was an unspoken goal that attempted to assess their competence, depth of education and instinctive ability to accurately diagnose and treat various illness. Whenever I am engaged with a medical care provider, I am trying to make an accurate estimation of their abilities, since in the end, it can spell life or death. That is a purely human activity.
Over the years, part of this process has become more computational in nature as I attempted to ask the most actionable set of questions during an appointment. These questions create an atmosphere of seriousness that most competent care providers enjoy. The advent of Google has amplified the ability to prepare to an extreme degree and can positively impact the continuing education of the care providers as well. Patient-provider education is an two-way, continuing and iterative process.
Design Patterns of Computationally-Assisted Inquiry:
- Drug-driven
- Disease-driven
- Cell-driven
- Test-driven
- Physiology-driven
Let's define each case:
Drug-Driven (or pharmacology driven):
In this scheme, the name of a candidate drug typically prescribed for treating a condition is used to find:
- Chemical structure,
- Indications for use
- Contraindications
- Mode of action
- Side effects
- Toxicity / L:D
The Physician's Desk Reference canonizes this information for commonly prescribed drugs. For example, digoxin, derived from the Foxglove plant is indicated for congestive heart failure and improves pumping efficiency, but its toxic level is very close to its therapeutic level making it somewhat risky to use.
There are intellectual rights issues that emerge, but machine learning can address these by enabling knowledge utilization without distribution of the original content.
Imagine you are in a pharmacy where all known drugs are present. Given you, your genes, and your current state, what is the optimal combination of these substances and the dosing schedule that most benefit both short and long term health and quality of life?
Disease Driven (or illness driven):
In this approach:
- A complaint is registered
- A history is taken
- Vital signs are measured
- Patient's symptoms are recorded
- Diagnostic tests are ordered and results recorded
These are combined in a complex composite record which is represented as the patient's chart which includes text, handwriting, hand-annotated diagrams,
These records accumulate as a stack with each successive visit. eCharting has been taking hold for the past few years, but a remarkable amount of patient information lies fallow in discarded, obsolete patient records in cold-storage. It is essential that organizations like Google digitize such patients charts before they are lost to perpetuity. This would involve scanning treatments and outcomes with the same scale and enthusiasm that the world's libraries have enjoyed in recovering old books. Thank you Google! This would create large training sets for machine learning and further of codification of medical knowledge in a machine-learning context. HIPAA laws and frivolous copyright lawsuits have obstructed this Nobel-prize worth activity, perhaps due to concerns of litigation-sensitive record custodians.
Cell-Driven (or genetics driven):
Cell-Driven strategies include:
- Cell population counting in hematology
- Differential cell identification (diff)
- Flow cytometry in cancer diagnosis
- Genome sequencing
Cell population counting includes red cell, white cell, platelets and differential cell identification as part of the complete blood count (CBC and Diff). Differential cell identification can be done manually or by flow cytometry where white cells are sorted into types including lymphocytes, monocytes, neutrophils, eosinophils and basophils.
Modern and future approaches include characterizing the
- Receptors (cell logic gates) expressed and their mutations
- Cytokines (chemical signals) that are driving their differentiation
- Metabolic products
Consider the gene-product as drug treatment point of view. If we knew the exact amount of over or under expression of genes and their cognate gene products, these could be compensated for by a custom cocktail of appropriate pharmacological agents. Small molecule agents can be ingested via the digestive tract, while protein products must often be administered intravenously since the protein structures are degraded when in contact with the digestive tract enzymes and low pH acidic conditions.
Test-Driven (clinical lab and radiology driven):
Test-Driven strategies include Clinical:
- Chemistry for Kidney, Liver, Heart and Organ System Function
- Bacteriology for Infections Disease
- Histology for Determination of Abnormal Cell Types
- Ultrasound in 2, 3 and 4 dimensions
- X-Rays
- PET scans (Positron-Emission Tomography)
- MRI/fMRI (functional Magnetic Resonance Imagery)
- CAT scans (Computer-Aided Tomography)
Physiology Driven (or illness driven):
Higher levels of organization than just the cell can be players in disease process. Organs operate in synchrony like the members of an orchestra, some autonomously and some with directed signaling. The sympathetic vs. parasympathetic nervous system are example of this. Endocrine disorders can manifest at relatively large distances from the source of the problem. Diabetes is a complex disease with multiple factors operating at multiple scales including genetics and environment.
With these design patterns in place, to borrow a phrase from software engineering, we should think about how they might be combined into a diagnostic and treatment system that could reduce the cost of healthcare help the most people possible.
Now recall the Daisy Wheel printers of the past. They achieved a speedup factor of two simply by not starting at the beginning of the next line to be printed. Since the mechanical movement of the print head was the most expensive step, the even lines were printed left to right, while the odd lines were printed right to left, which required only the minimal computation of reversing the order of the characters to be printed on the odd lines. Does this metaphor fit with the application of knowledge derived from each of the medical processes described above?
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