Practical computer program that diagnoses diseases in actual patients

• US 20080171916A1
• Filed: 03/13/2008
• Published: 07/17/2008
• Est. Priority Date: 05/20/2005
• Status: Abandoned Application

• Current Assignee: Carlos Feder, Tomas Feder
• Original Assignee: Carlos Feder, Tomas Feder
• Inventors: Feder, Carlos, Feder, Tomas
• Application Number:
  US12/075,609
• Publication Number:
  US 20080171916A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

This algorithm and corresponding computer program emulates the diagnostic reasoning of a clinician. Accurate and efficient, it concludes only those final diagnoses that agree with the diseases that actually afflict a patient. A differential diagnosis list is created and the probability of each diagnosis is calculated with a novel procedure that we call Mini-Max Procedure that uses the positive predictive value of clinical data present to increase probability and the sensitivity of clinical data absent to reduce probability. The probability of a diagnosis is considered equal to the maximum positive predictive value of all clinical data present that support the diagnosis, circumventing more complex and inaccurate prior art methods. The Mini-Max Procedure also identifies concurrent diseases. Bayes formula, because of its inability to process properly interdependent clinical data and concurrent diseases, is used with modifications. The algorithm recommends at each diagnostic step, the best cost-benefit clinical datum next to investigate. Furthermore, the algorithm can simultaneously recommend several best cost-benefit clinical data, avoiding the need to contact the patient after the result of each single test is obtained. Heuristic parameters and abridged output files reduce the great number of best cost benefit clinical data recommended, without compromising the accuracy of the diagnostic procedure. Interactions of drugs and concurrent diseases with clinical data of the primary disease is detected, precluding ruling out of serious diseases due to this masking effect. Overlooking of important diagnoses is precluded by searching and processing diagnoses that are related to confirmed diagnoses. The algorithm diagnoses clinical forms of disease and complex clinical presentations, where disease, syndromes, complications, and other clinical entities coexist in a single patient. The algorithm processes efficiently synonyms of clinical data and diagnoses. The algorithm is straightforward, logical and mathematically simple; heuristic restrictions preclude excessive proliferation of clinical data and diagnoses. Because it is expressed in natural language, it is readily understandable and user friendly.

Claims

1. A medical diagnostic algorithm excluding disease prevalence and subjective qualities of clinical data, said algorithm comprising the steps of:

a) computing for each clinical datum a sensitivity S based on disease cases to derive sensitivities as follows;

$S=\frac{\begin{array}{c}\mathrm{number}<br><br><br><br>\mathrm{of}<br><br><br><br>\mathrm{disease}<br><br><br><br>\mathrm{cases}\\ \mathrm{manifesting}<br><br><br><br>\mathrm{said}<br><br><br><br>\mathrm{clinical}<br><br><br><br>\mathrm{datum}\end{array}}{\mathrm{total}<br><br><br><br>\mathrm{number}<br><br><br><br>\mathrm{of}<br><br><br><br>\mathrm{diseases}<br><br><br><br>\mathrm{cases}};<br><br>$

b) computing for each clinical datum present a positive predictive value PP value_i supporting a disease diagnosis i, thereby obtaining PP value₁ through PP value_n;

$\mathrm{PP}<br><br><br><br>{\mathrm{value}}_i=\frac{S_i}{S_1+\mathrm{\dots <br><br>}+S_i+\mathrm{\dots <br><br>}+S_n},$

where S_i denotes sensitivity of said clinical datum present for said disease diagnosis i and S₁ . . . S_n denote sensitivities of said clinical datum for corresponding disease diagnoses 1 . . . n;

c) storing said sensitivities and said positive predictive values in a database linked to each said clinical datum and to a corresponding disease model;

d) ruling in disease diagnoses into a differential diagnosis list when clinical data present in a patient match at least one clinical datum present for said corresponding disease model;

e) establishing a cost of collecting a clinical datum not yet investigated in said patient, said cost of collecting being the maximum value of expense, risk and discomfort to said patient;

Cost=max(expense,risk,discomfort);

f) establishing a benefit of collecting said clinical datum not yet investigated in said patient based on its impact on a probability P of at least one of said disease diagnoses in said differential diagnosis list;

wherein said clinical datum not yet investigated is chosen for investigation in said patient in an analysis comprising a best cost-benefit consideration of said cost of collecting and said benefit of collecting.

2. The algorithm of claim 1, further comprising grouping said clinical datum not yet investigated with additional clinical data not yet investigated based on said cost of collecting into cost categories.

3. The algorithm of claim 1, further comprising grouping said clinical datum not yet investigated with additional clinical data not yet investigated based on clinical procedures associated with collecting said clinical datum not yet investigated and said additional clinical data not yet investigated.

4. The algorithm of claim 1, wherein said probability P for each of said disease diagnoses for said patient equals a maximum value among PP value₁ through PP value_n for each of said disease diagnoses:

P=max(PP value₁,PP value₂, . . . , PP value_n).

5. The algorithm of claim 4, further comprising:

a) determining probabilities P_i for select clinical data, said select clinical data comprising said clinical datum not yet investigated and additional clinical data not yet investigated;

b) retaining among said select clinical data only those clinical data producing a predetermined change in probability P of corresponding diagnosis, thereby reducing the total number of clinical data not yet investigated to be included in said best cost-benefit consideration.

6. The algorithm of claim 1, further comprising parametrizing said clinical datum not yet investigated in view of additional clinical data not yet investigated.

7. The algorithm of claim 6, wherein said parametrizing comprises selecting at least one parameter for said clinical datum not yet investigated and said additional clinical data not yet investigated, and assigning a value to said at least one parameter.

8. The algorithm of claim 7, wherein said at least one parameter is selected from the group of parameters consisting of trim present no cost, trim absent no cost, trim present greater cost, trim absent greater cost, present difference cost, absent difference cost, confirmation threshold, deletion threshold, cutoff present, and cutoff absent.

9. The algorithm of claim 7, wherein said value of said at least one parameter is used to limit the number of said clinical data not yet investigated included in said best cost benefit consideration.

10. The algorithm of claim 1, further comprising creating output files consisting of abridged data cost quantity, abridged data cost procedure quantity, abridged parameter affected differential, and abridged global overview.

11. The algorithm of claim 10, wherein at least one of said output files is selected to limit the number of said clinical data not yet investigated included in said best cost benefit consideration.

12. The algorithm of claim 1, further comprising creating a plurality of complex clinical presentation models each comprising predetermined related diagnoses.

13. The algorithm of claim 12, wherein said related diagnoses are processed for concurrence with diagnoses in said differential diagnosis list having a probability P that reached said confirmation threshold parameter, said algorithm comprising the steps of:

a) matching each said diagnosis having a probability P that reached said confirmation threshold with at least one of said diagnoses comprised by at least one of said complex clinical presentation models;

b) selecting all said diagnoses comprised in said matched complex clinical presentation model and computing their probability P with a mini-max procedure; and

c) displaying those of said selected diagnoses that reach said confirmation threshold as concurrent diagnoses;

thereby precluding overlooking related diagnoses.

14. The algorithm of claim 1, further comprising creating a plurality of complex clinical presentation models each comprising a predetermined susceptible diagnosis with a susceptible clinical datum susceptible to be masked and predetermined masking diagnoses able to mask said susceptible clinical datum.

15. The algorithm of claim 14, wherein said masking diagnoses, said susceptible diagnosis, and said susceptible clinical datum are processed, said algorithm comprising the steps of:

a) matching each said susceptible diagnosis in said differential diagnosis reaching a predetermined cutoff present parameter and including said susceptible clinical datum absent with similar said susceptible diagnosis comprised by said complex clinical presentation model;

b) selecting all said masking diagnoses in said matched complex clinical presentation model and computing their probabilities P with a mini-max procedure;

c) displaying those of said selected masking diagnoses that reach said confirmation threshold parameter as concurrent diagnoses; and

d) computing again probability P of said susceptible diagnosis without considering said absent susceptible clinical datum if at least one of said masking diagnosis reaches said confirmation threshold parameter;

thereby precluding excessive reduction of probability P of said susceptible diagnosis by said masked clinical datum.

16. The algorithm of claim 1, further comprising creating input files for said diseases models, data present, data absent, complex presentation models, said clinical procedures, and said parameters.

17. The algorithm of claim 1, further comprising applying a mini-max procedure to said disease diagnoses in said differential diagnosis list, said mini-max procedure comprising:

a) creating predetermined clinical data pairs consisting of one of said clinical datum present in said patient and one clinical datum absent from said patient, said clinical datum absent being selected for sensitivity S_i;

b) computing for each of said predetermined clinical data pairs a partial probability Pi in accordance with;

$\mathrm{partial}<br><br><br><br>P_i=\frac{\mathrm{PP}<br><br><br><br>{\mathrm{value}}_i<br><br>\left(1-S_i\right)}{\begin{array}{c}\mathrm{PP}<br><br><br><br>{\mathrm{value}}_1<br><br>\left(1-S_1\right)+\mathrm{\dots <br><br>}<br><br><br><br>\mathrm{PP}<br><br><br><br>{\mathrm{value}}_i<br><br>\left(1-S_i\right)+\mathrm{\dots <br><br>}<br><br>\\ \mathrm{PP}<br><br><br><br>{\mathrm{value}}_n<br><br>\left(1-S_n\right)\end{array}},$

whereby said partial probabilities P₁ . . . P_n satisfy a normalization condition P₁+ . . . +P_n=1.

18. The algorithm of claim 17, wherein said mini-max procedure further comprises creating a mini-max table for each of said predetermined number of said disease diagnoses retained in said differential diagnosis list, whereby a first column of each said mini-max table comprises said PP value₁ through PP value_n for each said disease diagnosis and a first row of each said mini-max table comprises for each said predetermined data pair said sensitivity S_i of said clinical datum absent.

19. The algorithm of claim 18, further comprising the steps of:

a) transferring each said partial probability P_i into cells of said mini-max table where said PP value_i for each said clinical datum present and said sensitivity S_i for each said clinical datum absent converge; and

b) selecting from among said partial probabilities P₁ . . . P_n in said cells a determining partial probability P_d having the smallest value in its row and the greatest value in its column.

20. The algorithm of claim 19, wherein from each said mini-max table, said determining partial probability P_d is selected as a total probability P_t for said disease diagnosis for which said mini-max table was created.

21. The algorithm of claim 20, further comprising applying at least one evaluation function from the group consisting of said deletion threshold and said confirmation threshold to said total probability P_t for said disease diagnosis.

22. The algorithm of claim 21, further comprising determining a magnitude of change in said total probability P_t produced by a presence and by an absence of at least one clinical datum, thereby guiding a process of collection of said clinical data.

23. The algorithm of claim 1, wherein said clinical datum and said diagnoses employ an anti-aliasing scheme.

24. The algorithm of claim 13, wherein said anti-aliasing scheme comprises an alphanumeric identifier for synonymous diagnoses and synonymous clinical data.

25. The computer program of claim 1, further comprising applying a method enabling accurate processing of a limited number of diseases, without compromising accuracy of medical diagnosis process, creating models named other diseases and other diseases same representing all other diseases not included in the database with corresponding said disease models, thereby overcoming the condition that for accuracy all known diseases must exhaustively be included in the database with corresponding disease models.

26. An auxiliary medical diagnostic algorithm, said algorithm, named datum program, creating clinical datum lists, comprising for each clinical datum all the diagnoses able to manifest said clinical datum and displaying for each said diagnosis and said clinical datum the corresponding positive predicted value and sensitivity.