Medicine as a data science (5)

A guide to deep learning in healthcare

Some months ago, Mckinsey released a guide to AI for executives. It says:

Deep learning is a type of machine learning that can process a wider range of data resources, requires less data preprocessing by humans, and can often produce more accurate results than traditional machine-learning approaches (although it requires a larger amount of data to do so). In deep learning, interconnected layers of software-based calculators known as “neurons” form a neural network. The network can ingest vast amounts of input data and process them through multiple layers that learn increasingly complex features of the data at each layer. The network can then make a determination about the data, learn if its determination is correct, and use what it has learned to make determinations about new data. For example, once it learns what an object looks like, it can recognize the object in a new image.

Now Nature publishes a helpful review article on deep learning in healthcare.

Some of the greatest successes of deep learning have been in the field of computer vision (CV). CV focuses on image and video understanding, and deals with tasks such as object classification, detection, and segmentation—which are useful in determining whether a patient’s radiograph contains malignant tumors

The next step is speech and text. Some advances are already available. Basically, Tensorflow by Google is feeding the beast.

PS. WHO and the classification of digital health interventions 1.0

PS. And the book to read:

When the regulator doesn't care about the danger within us

A must see Netflix documentary: The bleeding edge. It explains how medical devices are introduced in the market without appropriate control.
CBS news explains some details:

Just because it's new doesn't mean it's better, it may be dangerous and damage you for life. Unfortunately, this is the summary.
And the book to read:

Medicine as a data science (4)

The practical implementation of artificial intelligence technologies in medicine

One of the critical issues for AI implementation in clinical practice is about privacy. In this article you'll find a clear statement on the impact of EU regulation:

The GDPR will affect AI implementation in healthcare in several ways. First, it requires explicit and informed consent before any collection of personal data. Informed consent has been a long-standing component of medical practice (unlike in social media or onlinebased marketing), but having to obtain informed consent for an  collection of data still represents a higher bar than obtaining consent for specific items, such as procedures or surgical interventions. Second, the new regulation essentially lends power to the person providing the data to track what data is being collected and to be able to request removal of their data. In the healthcare context, this will shift some of the power balance toward the patient and highlights the importance of ongoing work needed to protect patient privacy and to determine appropriate governance regarding data ownership. 

More details inside.

Potential roles of AI-based technologies in healthcare.

 Integration of patient health information at multiple interfaces.

Medicine as a data science (3)

High-performance medicine: the convergence of human and artificial intelligence

If you want to know the current state of artificial intelligence in medicine, then Eric Topol review in Nature is the article you have to read. A highlighted statement:

There are differences between the prediction metric for a cohort and an individual prediction metric. If a model’s AUC is 0.95, which most would qualify as very accurate,
this reflects how good the model is for predicting an outcome, such as death, for the overall cohort. But most models are essentially classifiers and are not capable of precise prediction at the individual level, so there is still an important dimension of uncertainty.

And this is good summary:

Despite all the promises of AI technology, there are formidable obstacles and pitfalls. The state of AI hype has far exceeded the state of AI science, especially when it pertains to validation and readiness for implementation in patient care. A recent example is IBM Watson Health’s cancer AI algorithm (known as Watson for Oncology). Used by hundreds of hospitals around the world for recommending treatments for patients with cancer, the algorithm was based on a small number of synthetic, nonreal cases with very limited input (real data) of oncologists. Many of the actual output recommendations for treatment were shown to be erroneous, such as suggesting the use of bevacizumab in a patient with severe bleeding, which represents an explicit contraindication and ‘black box’ warning for the drug. This example also highlights the potential for major harm to patients, and thus for medical malpractice, by a flawed algorithm. Instead of a single doctor’s mistake hurting a patient, the potential for a machine algorithm inducing iatrogenic risk is vast. This is all the more reason that systematic debugging, audit, extensive simulation, and validation, along with prospective scrutiny, are required when an AI algorithm is unleashed in clinical practice. It also underscores the need to require more evidence and robust validation to exceed the recent downgrading of FDA regulatory requirements for medical algorithm approval

Therefore, take care when you look at tables like this one:

Prediction	n	AUC	Publication (Reference number)
In-hospital mortality, unplanned readmission, prolonged LOS, final discharge diagnosis	216,221	0.93* 0.75+0.85#	Rajkomar et al.96
All-cause 3–12 month mortality	221,284	0.93^	Avati et al.91
Readmission	1,068	0.78	Shameer et al.106
Sepsis	230,936	0.67	Horng et al.102
Septic shock	16,234	0.83	Henry et al.103
Severe sepsis	203,000	0.85@	Culliton et al.104
Clostridium difficile infection	256,732	0.82++	Oh et al.93
Developing diseases	704,587	range	Miotto et al.97
Diagnosis	18,590	0.96	Yang et al.90
Dementia	76,367	0.91	Cleret de Langavant et al.92
Alzheimer’s Disease ( + amyloid imaging)	273	0.91	Mathotaarachchi et al.98
Mortality after cancer chemotherapy	26,946	0.94	Elfiky et al.95
Disease onset for 133 conditions	298,000	range	Razavian et al.105
Suicide	5,543	0.84	Walsh et al.86
Delirium	18,223	0.68	Wong et al.100

1. LOS, length of stay; n, number of patients (training+ validation datasets). For AUC values:
2. *, in-hospital mortality; + , unplanned readmission; #, prolonged LOS; ^, all patients; @, structured + unstructured data; + +, for University of Michigan site.

In search for a fair price-setting in cancer drugs

Pricing of cancer medicines and its impacts

We all know that new cancer drugs represent a challenge for the whole society. Expectations from drug firms are high and public and private budgets do not increase according to such expectations. A technical report released by WHO sheds light on the issue.
Just one statement:

Overall, the analysis suggests that the costs of R&D and production may bear little or no relationship to how pharmaceutical companies set prices of cancer medicines. Pharmaceutical companies set prices according to their commercial goals, with a focus on extracting the maximum amount that a buyer is willing to pay for a medicine. This pricing approach often makes cancer medicines unaffordable, preventing the full benefit of the medicines from being realized.

You may find here former posts on the same topic.

PS. My comment on genetics in clinical practice in GCS 69, p.96

TATE MODERNEXHIBITION

THE C C LAND EXHIBITION

PIERRE BONNARD

THE COLOUR OF MEMORY

Beyond nudges

Beyond Nudges — When Improving Health Calls for Greater Assertiveness

From NEJM

And a book:

Barbarians at a drug firm

After Enron, Valeant Pharmaceuticals is one of the largest corruption scandals of Wall Street. If you want to know the details, you just have to connect Netflix. In episode 3 of the documentary series Dirty Money, you'll  find The Drug Short episode. You'll understand what happens when barbarians take unethical decisions, although they were legal. Meanwhile, the regulator was on vacation in the US.
Highly recommended.

PS. If you don't have Netflix and you are interested on how to proceed like a Valeant executive in the energy sector, then you have to watch "Enron: the smartest guys in the room".